The AI team your organization
needs without building
one from scratch.
For organizations serious about AI, not just curious about it. We bring strategy, engineering, and deployment under one roof and stay in it with you until it works.
to Deployment
Strategy without execution
is a deck. Execution without
strategy builds the wrong thing.
Most organizations trying to adopt AI end up in one of two places. Pure consulting firms deliver a roadmap and move on, leaving you with recommendations and no one to build them. Dev shops take a brief and build what they are told, with no one in the room who understood the business problem before writing the first line of code.
"About a year before we sold, we knew we needed a real AI capability. Not a strategy document. Not a demo. Something built, running, and credible to an acquirer. AI Dev Lab came in, understood the business immediately, and delivered. It changed the outcome of the deal."Founder, Financial Services
A roadmap. Then a handoff.
A well-reasoned strategy and a prioritized plan, followed by a handoff. Implementation is your problem. The project stalls six months in when no one can execute what was recommended.
A prototype. Then they move on.
A working build of what you asked for, built without the organizational context that determines whether it will actually work in production. Then they move on.
One team. Strategy to live system.
We think through the strategy, design the right solution, engineer and deploy it in your live environment, and iterate until it performs the way your organization needs. No handoffs. No gaps.
One team. First conversation
to live system.
Every engagement is a partnership, not a project. We come in at the leadership level and stay through deployment and beyond.
Understand the Business First
Before strategy, before architecture, before any technology decisions, we learn how your organization actually operates. The workflows, the constraints, the edge cases, the things that have broken before.
Design the Right Solution Together
We do not present a pre-packaged solution and adapt it to your situation. We design the right one for your specific environment with you in the room throughout.
Build and Deploy in Your Environment
We engineer and deploy into your live systems, integrate with what you already have, and validate against real operational conditions before anything touches your team at scale.
Stay Until It Works
We monitor, retrain, and optimize after deployment because production AI requires ongoing care, not a one-time delivery. The engagement is not over when the build is done.
Real problems.
Real systems. Real outcomes.
These are not prototypes or demos. Every engagement on this page is a system running in a live operational environment, solving a problem that mattered enough to build something serious around.
The Sales Team Stopped Processing Emails. They Started Closing Deals.
A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.
A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.
100% of inbound RFQs handled automatically · Zero sales hours on intake · Multilingual Read case study →Every Dealer. Every Model. Every Question. Answered in Seconds.
One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.
A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.
Global deployment across multiple continents · 100% catalog coverage · Multilingual · Instant response Read case study →Every Agent Was Looking at the Same Listings. We Changed That.
A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.
A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.
Proprietary intelligence no competitor can see · Continuous real-time scoring · 3 data sources unified Read case study →A Full AI Intelligence Layer Built Into a Financial Services Firm.
A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.
Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.
70% time reduction · 85% issue detection · 95% forecast accuracy · 500+ professionals served Read case study →An ML System That Finds the Right Path for Every Student. Not Just the Average One.
Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.
A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.
3 ML architectures tested · Real student data validated · Federal funding renewed on results Read case study →
Production AI built on
17 years of live transit
The most operationally grounded AI platform in transit.
IT Curves spent 17 years learning how transit actually operates: dispatch exceptions, broker billing, ADA constraints, everything that happens between the first call and the last trip. AI Dev Lab turned that knowledge into six specialized agents now running live across five transit modes.
This is what it looks like when AI is built from inside an industry rather than adapted to it from the outside.
See the Transit Platform →
Built for organizations
where failure is not
an option.
Every industry on this list represents a production system we designed, built, and deployed. Not a capability statement. Not a pilot program. Work that is running today, in real operational environments, under real conditions.
Six production AI agents running live across paratransit, fixed route, NEMT, and microtransit. Handling rider communication, call automation, dispatch intelligence, and analytics across 12 agencies — built on 17 years of live operations knowledge no outside vendor can replicate.
See the platform in depthAutomated the full inbound quote workflow for a global freight forwarder. NLP classification, data extraction, and conversational follow-up replacing hundreds of manual email hours each week.
AI accounting assistant integrated via API directly into practice management software. Natural language commands execute invoices, reports, and account queries without touching a menu.
Behavioral ML engine surfacing buyer intent signals invisible to standard MLS tools. Deployed inside a national SaaS platform, prioritizing leads for agents in real time based on in-app behavior.
Dealer-facing conversational AI for a multinational OEM trained on the full vehicle catalog to handle complex comparisons, specifications, and pricing questions across a global sales network.
NSF-funded curriculum recommendation engine prescribing personalized learning pathways for students with disabilities — validated against real student data, built to support continued federal funding.
disappears first.
Client stories from
the field.
Real engagements, real outcomes. Each story walks through the problem, what we built, and what changed as a result.
Six AI Agents Running Live in Paratransit Operations
How IT Curves and AI Dev Lab built a fully agentic platform handling rider communication, call automation, dispatch intelligence, and feedback collection across 12 agencies.
Eliminating Manual Email Processing for a Global Freight Forwarder
A German freight company was spending hours each day manually processing inbound quote requests. We built an NLP system that now handles the entire intake workflow automatically.
Surfacing Off-Market Buyer Leads with Behavioral AI
A national real estate SaaS platform needed to give agents a competitive edge. We built a behavioral ML engine that identifies and prioritizes leads competitors cannot find.
We work with organizations
that are serious about building
real AI capability.
Not every organization is the right fit. The engagements that go well tend to share a few traits on both sides of the table.
The right fit
- A successful organization at an inflection point where AI represents the next serious operational lever, not an experiment.
- Leadership that wants a thinking partner, not a vendor. Someone in the room, not handing off a brief and disappearing.
- A real operational problem that AI can measurably improve, not an idea in search of a use case.
- Commitment to building something that works in production with a realistic budget and timeline behind it.
- An organization that values long-term capability over short-term deliverables and wants the relationship to outlast the first project.
Probably not the right fit
- Early-stage startups testing a hypothesis with no operational foundation or committed budget behind it.
- Organizations looking for a one-time project to hand off and manage internally with no ongoing support.
- Engagements driven by curiosity rather than a specific operational problem that AI will actually solve.
- Situations where the decision maker is not in the room. We do our best work with leadership engaged from the start.
You work with
senior leadership,
not a junior team.
AI Dev Lab is led by Jason Wells, whose background spans AI strategy, product development, and technical implementation across industries including transit, financial services, automotive, logistics, real estate, and education. His clients consistently describe the same experience: he understood the business problem before the technical conversation even started.
Every AI Dev Lab engagement is designed and overseen at the principal level. You are not pitched by a senior partner and then managed by a project team. The same strategic thinking that shapes the initial conversation shapes every decision through deployment.
Engagements begin with a focused discovery conversation about your goals, your workflows, and where AI creates the most leverage. When the scope warrants it, Jason leads that conversation directly.
What it is like
to work with us.
Very good customer service and vast experience with AI. Amazed how quickly they understood our project in just the initial call. AI Dev Lab works well under tight deadlines and delivers a wonderful product. Would definitely recommend.
Fantastic to work with, professional, organized, and thoughtful in tackling our specific needs. Always available to discuss issues or concerns. Highly recommended for anything related to data science or machine learning.
More than fair. More than kind. More help than I got from anyone before or since. Great soft and hard skills and even better resourcefulness. The day I connected with AI Dev Lab was a very good day.
Let's figure out
what is actually possible.
Tell us about your organization, your workflows, and where you think AI creates the most leverage. We will come back with a clear picture of what is achievable and a realistic path to get there.
Start a Conversationto Deployment
Strategy without execution
is a deck. Execution without
strategy builds the wrong thing.
Most organizations trying to adopt AI end up in one of two places. Pure consulting firms deliver a roadmap and move on, leaving you with recommendations and no one to build them. Dev shops take a brief and build what they are told, with no one in the room who understood the business problem before writing the first line of code.
"About a year before we sold, we knew we needed a real AI capability. Not a strategy document. Not a demo. Something built, running, and credible to an acquirer. AI Dev Lab came in, understood the business immediately, and delivered. It changed the outcome of the deal."Founder, Financial Services
A roadmap. Then a handoff.
A well-reasoned strategy and a prioritized plan, followed by a handoff. Implementation is your problem. The project stalls six months in when no one can execute what was recommended.
A prototype. Then they move on.
A working build of what you asked for, built without the organizational context that determines whether it will actually work in production. Then they move on.
One team. Strategy to live system.
We think through the strategy, design the right solution, engineer and deploy it in your live environment, and iterate until it performs the way your organization needs. No handoffs. No gaps.
One team. First conversation
to live system.
Every engagement is a partnership, not a project. We come in at the leadership level and stay through deployment and beyond.
Understand the Business First
Before strategy, before architecture, before any technology decisions, we learn how your organization actually operates. The workflows, the constraints, the edge cases, the things that have broken before.
Design the Right Solution Together
We do not present a pre-packaged solution and adapt it to your situation. We design the right one for your specific environment with you in the room throughout.
Build and Deploy in Your Environment
We engineer and deploy into your live systems, integrate with what you already have, and validate against real operational conditions before anything touches your team at scale.
Stay Until It Works
We monitor, retrain, and optimize after deployment because production AI requires ongoing care, not a one-time delivery. The engagement is not over when the build is done.
Real problems.
Real systems. Real outcomes.
These are not prototypes or demos. Every engagement on this page is a system running in a live operational environment, solving a problem that mattered enough to build something serious around.
The Sales Team Stopped Processing Emails. They Started Closing Deals.
A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.
A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.
100% of inbound RFQs handled automatically · Zero sales hours on intake · Multilingual Read case study →Every Dealer. Every Model. Every Question. Answered in Seconds.
One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.
A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.
Global deployment across multiple continents · 100% catalog coverage · Multilingual · Instant response Read case study →Every Agent Was Looking at the Same Listings. We Changed That.
A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.
A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.
Proprietary intelligence no competitor can see · Continuous real-time scoring · 3 data sources unified Read case study →A Full AI Intelligence Layer Built Into a Financial Services Firm.
A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.
Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.
70% time reduction · 85% issue detection · 95% forecast accuracy · 500+ professionals served Read case study →An ML System That Finds the Right Path for Every Student. Not Just the Average One.
Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.
A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.
3 ML architectures tested · Real student data validated · Federal funding renewed on results Read case study →
Production AI built on
17 years of live transit
The most operationally grounded AI platform in transit.
IT Curves spent 17 years learning how transit actually operates: dispatch exceptions, broker billing, ADA constraints, everything that happens between the first call and the last trip. AI Dev Lab turned that knowledge into six specialized agents now running live across five transit modes.
This is what it looks like when AI is built from inside an industry rather than adapted to it from the outside.
See the Transit Platform →
Built for organizations
where failure is not
an option.
Every industry on this list represents a production system we designed, built, and deployed. Not a capability statement. Not a pilot program. Work that is running today, in real operational environments, under real conditions.
Six production AI agents running live across paratransit, fixed route, NEMT, and microtransit. Handling rider communication, call automation, dispatch intelligence, and analytics across 12 agencies — built on 17 years of live operations knowledge no outside vendor can replicate.
See the platform in depthAutomated the full inbound quote workflow for a global freight forwarder. NLP classification, data extraction, and conversational follow-up replacing hundreds of manual email hours each week.
AI accounting assistant integrated via API directly into practice management software. Natural language commands execute invoices, reports, and account queries without touching a menu.
Behavioral ML engine surfacing buyer intent signals invisible to standard MLS tools. Deployed inside a national SaaS platform, prioritizing leads for agents in real time based on in-app behavior.
Dealer-facing conversational AI for a multinational OEM trained on the full vehicle catalog to handle complex comparisons, specifications, and pricing questions across a global sales network.
NSF-funded curriculum recommendation engine prescribing personalized learning pathways for students with disabilities — validated against real student data, built to support continued federal funding.
disappears first.
Client stories from
the field.
Real engagements, real outcomes. Each story walks through the problem, what we built, and what changed as a result.
Six AI Agents Running Live in Paratransit Operations
How IT Curves and AI Dev Lab built a fully agentic platform handling rider communication, call automation, dispatch intelligence, and feedback collection across 12 agencies.
Eliminating Manual Email Processing for a Global Freight Forwarder
A German freight company was spending hours each day manually processing inbound quote requests. We built an NLP system that now handles the entire intake workflow automatically.
Surfacing Off-Market Buyer Leads with Behavioral AI
A national real estate SaaS platform needed to give agents a competitive edge. We built a behavioral ML engine that identifies and prioritizes leads competitors cannot find.
We work with organizations
that are serious about building
real AI capability.
Not every organization is the right fit. The engagements that go well tend to share a few traits on both sides of the table.
The right fit
- A successful organization at an inflection point where AI represents the next serious operational lever, not an experiment.
- Leadership that wants a thinking partner, not a vendor. Someone in the room, not handing off a brief and disappearing.
- A real operational problem that AI can measurably improve, not an idea in search of a use case.
- Commitment to building something that works in production with a realistic budget and timeline behind it.
- An organization that values long-term capability over short-term deliverables and wants the relationship to outlast the first project.
Probably not the right fit
- Early-stage startups testing a hypothesis with no operational foundation or committed budget behind it.
- Organizations looking for a one-time project to hand off and manage internally with no ongoing support.
- Engagements driven by curiosity rather than a specific operational problem that AI will actually solve.
- Situations where the decision maker is not in the room. We do our best work with leadership engaged from the start.
You work with
senior leadership,
not a junior team.
AI Dev Lab is led by Jason Wells, whose background spans AI strategy, product development, and technical implementation across industries including transit, financial services, automotive, logistics, real estate, and education. His clients consistently describe the same experience: he understood the business problem before the technical conversation even started.
Every AI Dev Lab engagement is designed and overseen at the principal level. You are not pitched by a senior partner and then managed by a project team. The same strategic thinking that shapes the initial conversation shapes every decision through deployment.
Engagements begin with a focused discovery conversation about your goals, your workflows, and where AI creates the most leverage. When the scope warrants it, Jason leads that conversation directly.
What it is like
to work with us.
Very good customer service and vast experience with AI. Amazed how quickly they understood our project in just the initial call. AI Dev Lab works well under tight deadlines and delivers a wonderful product. Would definitely recommend.
Fantastic to work with, professional, organized, and thoughtful in tackling our specific needs. Always available to discuss issues or concerns. Highly recommended for anything related to data science or machine learning.
More than fair. More than kind. More help than I got from anyone before or since. Great soft and hard skills and even better resourcefulness. The day I connected with AI Dev Lab was a very good day.
Let's figure out
what is actually possible.
Tell us about your organization, your workflows, and where you think AI creates the most leverage. We will come back with a clear picture of what is achievable and a realistic path to get there.
Start a Conversationto Deployment
Strategy without execution
is a deck. Execution without
strategy builds the wrong thing.
Most organizations trying to adopt AI end up in one of two places. Pure consulting firms deliver a roadmap and move on, leaving you with recommendations and no one to build them. Dev shops take a brief and build what they are told, with no one in the room who understood the business problem before writing the first line of code.
"About a year before we sold, we knew we needed a real AI capability. Not a strategy document. Not a demo. Something built, running, and credible to an acquirer. AI Dev Lab came in, understood the business immediately, and delivered. It changed the outcome of the deal."Founder, Financial Services
A roadmap. Then a handoff.
A well-reasoned strategy and a prioritized plan, followed by a handoff. Implementation is your problem. The project stalls six months in when no one can execute what was recommended.
A prototype. Then they move on.
A working build of what you asked for, built without the organizational context that determines whether it will actually work in production. Then they move on.
One team. Strategy to live system.
We think through the strategy, design the right solution, engineer and deploy it in your live environment, and iterate until it performs the way your organization needs. No handoffs. No gaps.
One team. First conversation
to live system.
Every engagement is a partnership, not a project. We come in at the leadership level and stay through deployment and beyond.
Understand the Business First
Before strategy, before architecture, before any technology decisions, we learn how your organization actually operates. The workflows, the constraints, the edge cases, the things that have broken before.
Design the Right Solution Together
We do not present a pre-packaged solution and adapt it to your situation. We design the right one for your specific environment with you in the room throughout.
Build and Deploy in Your Environment
We engineer and deploy into your live systems, integrate with what you already have, and validate against real operational conditions before anything touches your team at scale.
Stay Until It Works
We monitor, retrain, and optimize after deployment because production AI requires ongoing care, not a one-time delivery. The engagement is not over when the build is done.
Real problems.
Real systems. Real outcomes.
These are not prototypes or demos. Every engagement on this page is a system running in a live operational environment, solving a problem that mattered enough to build something serious around.
The Sales Team Stopped Processing Emails. They Started Closing Deals.
A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.
A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.
100% of inbound RFQs handled automatically · Zero sales hours on intake · Multilingual Read case study →Every Dealer. Every Model. Every Question. Answered in Seconds.
One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.
A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.
Global deployment across multiple continents · 100% catalog coverage · Multilingual · Instant response Read case study →Every Agent Was Looking at the Same Listings. We Changed That.
A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.
A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.
Proprietary intelligence no competitor can see · Continuous real-time scoring · 3 data sources unified Read case study →A Full AI Intelligence Layer Built Into a Financial Services Firm.
A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.
Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.
70% time reduction · 85% issue detection · 95% forecast accuracy · 500+ professionals served Read case study →An ML System That Finds the Right Path for Every Student. Not Just the Average One.
Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.
A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.
3 ML architectures tested · Real student data validated · Federal funding renewed on results Read case study →
Production AI built on
17 years of live transit
The most operationally grounded AI platform in transit.
IT Curves spent 17 years learning how transit actually operates: dispatch exceptions, broker billing, ADA constraints, everything that happens between the first call and the last trip. AI Dev Lab turned that knowledge into six specialized agents now running live across five transit modes.
This is what it looks like when AI is built from inside an industry rather than adapted to it from the outside.
See the Transit Platform →
Built for organizations
where failure is not
an option.
Every industry on this list represents a production system we designed, built, and deployed. Not a capability statement. Not a pilot program. Work that is running today, in real operational environments, under real conditions.
Six production AI agents running live across paratransit, fixed route, NEMT, and microtransit. Handling rider communication, call automation, dispatch intelligence, and analytics across 12 agencies — built on 17 years of live operations knowledge no outside vendor can replicate.
See the platform in depthAutomated the full inbound quote workflow for a global freight forwarder. NLP classification, data extraction, and conversational follow-up replacing hundreds of manual email hours each week.
AI accounting assistant integrated via API directly into practice management software. Natural language commands execute invoices, reports, and account queries without touching a menu.
Behavioral ML engine surfacing buyer intent signals invisible to standard MLS tools. Deployed inside a national SaaS platform, prioritizing leads for agents in real time based on in-app behavior.
Dealer-facing conversational AI for a multinational OEM trained on the full vehicle catalog to handle complex comparisons, specifications, and pricing questions across a global sales network.
NSF-funded curriculum recommendation engine prescribing personalized learning pathways for students with disabilities — validated against real student data, built to support continued federal funding.
disappears first.
Client stories from
the field.
Real engagements, real outcomes. Each story walks through the problem, what we built, and what changed as a result.
Six AI Agents Running Live in Paratransit Operations
How IT Curves and AI Dev Lab built a fully agentic platform handling rider communication, call automation, dispatch intelligence, and feedback collection across 12 agencies.
Eliminating Manual Email Processing for a Global Freight Forwarder
A German freight company was spending hours each day manually processing inbound quote requests. We built an NLP system that now handles the entire intake workflow automatically.
Surfacing Off-Market Buyer Leads with Behavioral AI
A national real estate SaaS platform needed to give agents a competitive edge. We built a behavioral ML engine that identifies and prioritizes leads competitors cannot find.
We work with organizations
that are serious about building
real AI capability.
Not every organization is the right fit. The engagements that go well tend to share a few traits on both sides of the table.
The right fit
- A successful organization at an inflection point where AI represents the next serious operational lever, not an experiment.
- Leadership that wants a thinking partner, not a vendor. Someone in the room, not handing off a brief and disappearing.
- A real operational problem that AI can measurably improve, not an idea in search of a use case.
- Commitment to building something that works in production with a realistic budget and timeline behind it.
- An organization that values long-term capability over short-term deliverables and wants the relationship to outlast the first project.
Probably not the right fit
- Early-stage startups testing a hypothesis with no operational foundation or committed budget behind it.
- Organizations looking for a one-time project to hand off and manage internally with no ongoing support.
- Engagements driven by curiosity rather than a specific operational problem that AI will actually solve.
- Situations where the decision maker is not in the room. We do our best work with leadership engaged from the start.
You work with
senior leadership,
not a junior team.
AI Dev Lab is led by Jason Wells, whose background spans AI strategy, product development, and technical implementation across industries including transit, financial services, automotive, logistics, real estate, and education. His clients consistently describe the same experience: he understood the business problem before the technical conversation even started.
Every AI Dev Lab engagement is designed and overseen at the principal level. You are not pitched by a senior partner and then managed by a project team. The same strategic thinking that shapes the initial conversation shapes every decision through deployment.
Engagements begin with a focused discovery conversation about your goals, your workflows, and where AI creates the most leverage. When the scope warrants it, Jason leads that conversation directly.
What it is like
to work with us.
Very good customer service and vast experience with AI. Amazed how quickly they understood our project in just the initial call. AI Dev Lab works well under tight deadlines and delivers a wonderful product. Would definitely recommend.
Fantastic to work with, professional, organized, and thoughtful in tackling our specific needs. Always available to discuss issues or concerns. Highly recommended for anything related to data science or machine learning.
More than fair. More than kind. More help than I got from anyone before or since. Great soft and hard skills and even better resourcefulness. The day I connected with AI Dev Lab was a very good day.
Let's figure out
what is actually possible.
Tell us about your organization, your workflows, and where you think AI creates the most leverage. We will come back with a clear picture of what is achievable and a realistic path to get there.
Start a Conversationto Deployment
Strategy without execution
is a deck. Execution without
strategy builds the wrong thing.
Most organizations trying to adopt AI end up in one of two places. Pure consulting firms deliver a roadmap and move on, leaving you with recommendations and no one to build them. Dev shops take a brief and build what they are told, with no one in the room who understood the business problem before writing the first line of code.
"About a year before we sold, we knew we needed a real AI capability. Not a strategy document. Not a demo. Something built, running, and credible to an acquirer. AI Dev Lab came in, understood the business immediately, and delivered. It changed the outcome of the deal."Founder, Financial Services
A roadmap. Then a handoff.
A well-reasoned strategy and a prioritized plan, followed by a handoff. Implementation is your problem. The project stalls six months in when no one can execute what was recommended.
A prototype. Then they move on.
A working build of what you asked for, built without the organizational context that determines whether it will actually work in production. Then they move on.
One team. Strategy to live system.
We think through the strategy, design the right solution, engineer and deploy it in your live environment, and iterate until it performs the way your organization needs. No handoffs. No gaps.
One team. First conversation
to live system.
Every engagement is a partnership, not a project. We come in at the leadership level and stay through deployment and beyond.
Understand the Business First
Before strategy, before architecture, before any technology decisions, we learn how your organization actually operates. The workflows, the constraints, the edge cases, the things that have broken before.
Design the Right Solution Together
We do not present a pre-packaged solution and adapt it to your situation. We design the right one for your specific environment with you in the room throughout.
Build and Deploy in Your Environment
We engineer and deploy into your live systems, integrate with what you already have, and validate against real operational conditions before anything touches your team at scale.
Stay Until It Works
We monitor, retrain, and optimize after deployment because production AI requires ongoing care, not a one-time delivery. The engagement is not over when the build is done.
Real problems.
Real systems. Real outcomes.
These are not prototypes or demos. Every engagement on this page is a system running in a live operational environment, solving a problem that mattered enough to build something serious around.
The Sales Team Stopped Processing Emails. They Started Closing Deals.
A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.
A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.
100% of inbound RFQs handled automatically · Zero sales hours on intake · Multilingual Read case study →Every Dealer. Every Model. Every Question. Answered in Seconds.
One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.
A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.
Global deployment across multiple continents · 100% catalog coverage · Multilingual · Instant response Read case study →Every Agent Was Looking at the Same Listings. We Changed That.
A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.
A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.
Proprietary intelligence no competitor can see · Continuous real-time scoring · 3 data sources unified Read case study →A Full AI Intelligence Layer Built Into a Financial Services Firm.
A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.
Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.
70% time reduction · 85% issue detection · 95% forecast accuracy · 500+ professionals served Read case study →An ML System That Finds the Right Path for Every Student. Not Just the Average One.
Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.
A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.
3 ML architectures tested · Real student data validated · Federal funding renewed on results Read case study →
Production AI built on
17 years of live transit
The most operationally grounded AI platform in transit.
IT Curves spent 17 years learning how transit actually operates: dispatch exceptions, broker billing, ADA constraints, everything that happens between the first call and the last trip. AI Dev Lab turned that knowledge into six specialized agents now running live across five transit modes.
This is what it looks like when AI is built from inside an industry rather than adapted to it from the outside.
See the Transit Platform →
Built for organizations
where failure is not
an option.
Every industry on this list represents a production system we designed, built, and deployed. Not a capability statement. Not a pilot program. Work that is running today, in real operational environments, under real conditions.
Six production AI agents running live across paratransit, fixed route, NEMT, and microtransit. Handling rider communication, call automation, dispatch intelligence, and analytics across 12 agencies — built on 17 years of live operations knowledge no outside vendor can replicate.
See the platform in depthAutomated the full inbound quote workflow for a global freight forwarder. NLP classification, data extraction, and conversational follow-up replacing hundreds of manual email hours each week.
AI accounting assistant integrated via API directly into practice management software. Natural language commands execute invoices, reports, and account queries without touching a menu.
Behavioral ML engine surfacing buyer intent signals invisible to standard MLS tools. Deployed inside a national SaaS platform, prioritizing leads for agents in real time based on in-app behavior.
Dealer-facing conversational AI for a multinational OEM trained on the full vehicle catalog to handle complex comparisons, specifications, and pricing questions across a global sales network.
NSF-funded curriculum recommendation engine prescribing personalized learning pathways for students with disabilities — validated against real student data, built to support continued federal funding.
disappears first.
Client stories from
the field.
Real engagements, real outcomes. Each story walks through the problem, what we built, and what changed as a result.
Six AI Agents Running Live in Paratransit Operations
How IT Curves and AI Dev Lab built a fully agentic platform handling rider communication, call automation, dispatch intelligence, and feedback collection across 12 agencies.
Eliminating Manual Email Processing for a Global Freight Forwarder
A German freight company was spending hours each day manually processing inbound quote requests. We built an NLP system that now handles the entire intake workflow automatically.
Surfacing Off-Market Buyer Leads with Behavioral AI
A national real estate SaaS platform needed to give agents a competitive edge. We built a behavioral ML engine that identifies and prioritizes leads competitors cannot find.
We work with organizations
that are serious about building
real AI capability.
Not every organization is the right fit. The engagements that go well tend to share a few traits on both sides of the table.
The right fit
- A successful organization at an inflection point where AI represents the next serious operational lever, not an experiment.
- Leadership that wants a thinking partner, not a vendor. Someone in the room, not handing off a brief and disappearing.
- A real operational problem that AI can measurably improve, not an idea in search of a use case.
- Commitment to building something that works in production with a realistic budget and timeline behind it.
- An organization that values long-term capability over short-term deliverables and wants the relationship to outlast the first project.
Probably not the right fit
- Early-stage startups testing a hypothesis with no operational foundation or committed budget behind it.
- Organizations looking for a one-time project to hand off and manage internally with no ongoing support.
- Engagements driven by curiosity rather than a specific operational problem that AI will actually solve.
- Situations where the decision maker is not in the room. We do our best work with leadership engaged from the start.
You work with
senior leadership,
not a junior team.
AI Dev Lab is led by Jason Wells, whose background spans AI strategy, product development, and technical implementation across industries including transit, financial services, automotive, logistics, real estate, and education. His clients consistently describe the same experience: he understood the business problem before the technical conversation even started.
Every AI Dev Lab engagement is designed and overseen at the principal level. You are not pitched by a senior partner and then managed by a project team. The same strategic thinking that shapes the initial conversation shapes every decision through deployment.
Engagements begin with a focused discovery conversation about your goals, your workflows, and where AI creates the most leverage. When the scope warrants it, Jason leads that conversation directly.
What it is like
to work with us.
Very good customer service and vast experience with AI. Amazed how quickly they understood our project in just the initial call. AI Dev Lab works well under tight deadlines and delivers a wonderful product. Would definitely recommend.
Fantastic to work with, professional, organized, and thoughtful in tackling our specific needs. Always available to discuss issues or concerns. Highly recommended for anything related to data science or machine learning.
More than fair. More than kind. More help than I got from anyone before or since. Great soft and hard skills and even better resourcefulness. The day I connected with AI Dev Lab was a very good day.
Let's figure out
what is actually possible.
Tell us about your organization, your workflows, and where you think AI creates the most leverage. We will come back with a clear picture of what is achievable and a realistic path to get there.
Start a Conversationto Deployment
Strategy without execution
is a deck. Execution without
strategy builds the wrong thing.
Most organizations trying to adopt AI end up in one of two places. Pure consulting firms deliver a roadmap and move on, leaving you with recommendations and no one to build them. Dev shops take a brief and build what they are told, with no one in the room who understood the business problem before writing the first line of code.
"About a year before we sold, we knew we needed a real AI capability. Not a strategy document. Not a demo. Something built, running, and credible to an acquirer. AI Dev Lab came in, understood the business immediately, and delivered. It changed the outcome of the deal."Founder, Financial Services
A roadmap. Then a handoff.
A well-reasoned strategy and a prioritized plan, followed by a handoff. Implementation is your problem. The project stalls six months in when no one can execute what was recommended.
A prototype. Then they move on.
A working build of what you asked for, built without the organizational context that determines whether it will actually work in production. Then they move on.
One team. Strategy to live system.
We think through the strategy, design the right solution, engineer and deploy it in your live environment, and iterate until it performs the way your organization needs. No handoffs. No gaps.
One team. First conversation
to live system.
Every engagement is a partnership, not a project. We come in at the leadership level and stay through deployment and beyond.
Understand the Business First
Before strategy, before architecture, before any technology decisions, we learn how your organization actually operates. The workflows, the constraints, the edge cases, the things that have broken before.
Design the Right Solution Together
We do not present a pre-packaged solution and adapt it to your situation. We design the right one for your specific environment with you in the room throughout.
Build and Deploy in Your Environment
We engineer and deploy into your live systems, integrate with what you already have, and validate against real operational conditions before anything touches your team at scale.
Stay Until It Works
We monitor, retrain, and optimize after deployment because production AI requires ongoing care, not a one-time delivery. The engagement is not over when the build is done.
Real problems.
Real systems. Real outcomes.
These are not prototypes or demos. Every engagement on this page is a system running in a live operational environment, solving a problem that mattered enough to build something serious around.
The Sales Team Stopped Processing Emails. They Started Closing Deals.
A global freight forwarder was losing thousands of hours a year to one bottleneck: someone had to read every inbound quote request, track down missing details, and package it before pricing could begin. Every minute spent on intake was a minute not spent closing. We eliminated that workflow entirely.
A five-stage AI pipeline that reads, classifies, extracts data, follows up conversationally for missing information, and routes a fully formatted RFQ package to the pricing team — without a human touching a single email.
100% of inbound RFQs handled automatically · Zero sales hours on intake · Multilingual Read case study →Every Dealer. Every Model. Every Question. Answered in Seconds.
One of the world's largest automotive manufacturers needed consistent, complete product knowledge across a dealer network spanning multiple continents. Buyers were arriving more informed than ever, asking precise questions about configurations, towing specs, and warranty terms. Dealers were often unable to answer on the spot. That gap was costing deals.
A custom AI assistant trained on the full vehicle catalog — every model, trim, option package, and configuration dependency — deployed globally, responding in any language, with zero lookup time on any question or comparison.
Global deployment across multiple continents · 100% catalog coverage · Multilingual · Instant response Read case study →Every Agent Was Looking at the Same Listings. We Changed That.
A national real estate SaaS platform was sitting on a competitive advantage buried in their own data. Buyers were signaling intent through their behavior — returning to listings, narrowing searches, spending time on floor plans — and no one was reading it. Every agent on every platform was waiting for the same hand-raise from the same buyers.
A behavioral ML engine that reads buyer activity across the platform, weights each signal by intent strength, and surfaces a ranked, continuously updated lead feed. Agents see the best buyers in their market before those buyers contact anyone.
Proprietary intelligence no competitor can see · Continuous real-time scoring · 3 data sources unified Read case study →A Full AI Intelligence Layer Built Into a Financial Services Firm.
A financial services firm was spending 4 to 6 weeks producing reports that were outdated before anyone acted on them. The data existed. The analysis did not. What started as an efficiency problem became something larger: four interconnected AI capabilities that the firm eventually turned into the foundation of a standalone company.
Deep financial analysis across 21 metrics, GAAP compliance scanning, industry benchmarking, and ML cashflow forecasting — connected, automated, and deployed across more than 500 professionals.
70% time reduction · 85% issue detection · 95% forecast accuracy · 500+ professionals served Read case study →An ML System That Finds the Right Path for Every Student. Not Just the Average One.
Students with disabilities have always been handed the same curriculum paths as everyone else and told to make it work. A software company awarded an NSF grant needed a validated ML model that could read each student's individual profile and prescribe the sequence most likely to lead to real employment outcomes. Federal funding renewal depended on the results.
A recommendation engine using collaborative filtering, K-nearest neighbors, and graph convolutional matrix completion — tested against three ML architectures and validated on real student data to prescribe curriculum aligned with specific career goals.
3 ML architectures tested · Real student data validated · Federal funding renewed on results Read case study →
Production AI built on
17 years of live transit
The most operationally grounded AI platform in transit.
IT Curves spent 17 years learning how transit actually operates: dispatch exceptions, broker billing, ADA constraints, everything that happens between the first call and the last trip. AI Dev Lab turned that knowledge into six specialized agents now running live across five transit modes.
This is what it looks like when AI is built from inside an industry rather than adapted to it from the outside.
See the Transit Platform →
Built for organizations
where failure is not
an option.
Every industry on this list represents a production system we designed, built, and deployed. Not a capability statement. Not a pilot program. Work that is running today, in real operational environments, under real conditions.
Six production AI agents running live across paratransit, fixed route, NEMT, and microtransit. Handling rider communication, call automation, dispatch intelligence, and analytics across 12 agencies — built on 17 years of live operations knowledge no outside vendor can replicate.
See the platform in depthAutomated the full inbound quote workflow for a global freight forwarder. NLP classification, data extraction, and conversational follow-up replacing hundreds of manual email hours each week.
AI accounting assistant integrated via API directly into practice management software. Natural language commands execute invoices, reports, and account queries without touching a menu.
Behavioral ML engine surfacing buyer intent signals invisible to standard MLS tools. Deployed inside a national SaaS platform, prioritizing leads for agents in real time based on in-app behavior.
Dealer-facing conversational AI for a multinational OEM trained on the full vehicle catalog to handle complex comparisons, specifications, and pricing questions across a global sales network.
NSF-funded curriculum recommendation engine prescribing personalized learning pathways for students with disabilities — validated against real student data, built to support continued federal funding.
disappears first.
Client stories from
the field.
Real engagements, real outcomes. Each story walks through the problem, what we built, and what changed as a result.
Six AI Agents Running Live in Paratransit Operations
How IT Curves and AI Dev Lab built a fully agentic platform handling rider communication, call automation, dispatch intelligence, and feedback collection across 12 agencies.
Eliminating Manual Email Processing for a Global Freight Forwarder
A German freight company was spending hours each day manually processing inbound quote requests. We built an NLP system that now handles the entire intake workflow automatically.
Surfacing Off-Market Buyer Leads with Behavioral AI
A national real estate SaaS platform needed to give agents a competitive edge. We built a behavioral ML engine that identifies and prioritizes leads competitors cannot find.
We work with organizations
that are serious about building
real AI capability.
Not every organization is the right fit. The engagements that go well tend to share a few traits on both sides of the table.
The right fit
- A successful organization at an inflection point where AI represents the next serious operational lever, not an experiment.
- Leadership that wants a thinking partner, not a vendor. Someone in the room, not handing off a brief and disappearing.
- A real operational problem that AI can measurably improve, not an idea in search of a use case.
- Commitment to building something that works in production with a realistic budget and timeline behind it.
- An organization that values long-term capability over short-term deliverables and wants the relationship to outlast the first project.
Probably not the right fit
- Early-stage startups testing a hypothesis with no operational foundation or committed budget behind it.
- Organizations looking for a one-time project to hand off and manage internally with no ongoing support.
- Engagements driven by curiosity rather than a specific operational problem that AI will actually solve.
- Situations where the decision maker is not in the room. We do our best work with leadership engaged from the start.
You work with
senior leadership,
not a junior team.
AI Dev Lab is led by Jason Wells, whose background spans AI strategy, product development, and technical implementation across industries including transit, financial services, automotive, logistics, real estate, and education. His clients consistently describe the same experience: he understood the business problem before the technical conversation even started.
Every AI Dev Lab engagement is designed and overseen at the principal level. You are not pitched by a senior partner and then managed by a project team. The same strategic thinking that shapes the initial conversation shapes every decision through deployment.
Engagements begin with a focused discovery conversation about your goals, your workflows, and where AI creates the most leverage. When the scope warrants it, Jason leads that conversation directly.
What it is like
to work with us.
Very good customer service and vast experience with AI. Amazed how quickly they understood our project in just the initial call. AI Dev Lab works well under tight deadlines and delivers a wonderful product. Would definitely recommend.
Fantastic to work with, professional, organized, and thoughtful in tackling our specific needs. Always available to discuss issues or concerns. Highly recommended for anything related to data science or machine learning.
More than fair. More than kind. More help than I got from anyone before or since. Great soft and hard skills and even better resourcefulness. The day I connected with AI Dev Lab was a very good day.
Let's figure out
what is actually possible.
Tell us about your organization, your workflows, and where you think AI creates the most leverage. We will come back with a clear picture of what is achievable and a realistic path to get there.
Start a Conversation