Generative AI Development Services
Almost every company has now seen a convincing prototype. Far fewer have one running in front of customers. The gap is rarely the model. It is everything around the model: grounding answers in your own content, measuring quality before release, containing what the system is allowed to do, and operating it once real usage arrives.
Cabot provides generative AI development services that treat this as an engineering problem rather than a science experiment. We build the retrieval, evaluation, guardrails, and operations that turn a promising output into a dependable one, then integrate it with the systems your teams already use. You stay model-neutral, your regulated data stays in your environment, and you own the code and the IP from the first commit.
LLM applications · RAG · Agents · Evaluation · MLOps | Model-neutral and security led
No obligation. Your details stay private.
95%
Share of enterprise AI pilots that produced no measurable return, the gap between a working demo and a working business system
88%
Mid-size and large organizations now spending more than 5% of their IT budget on AI
700+ projects
Delivered for 140+ clients since 2010, across the stacks this work integrates with
What is generative AI development, and how is it different from buying a tool?
Generative AI development is the work of building an application around a generative model so it performs a specific job for your business reliably, using your own data and rules. A subscription to a public assistant gives your staff a general tool. Development gives you a system: one that reads your documents, follows your policies, connects to your software, refuses what it should refuse, and can be measured and improved. The model itself is a component you rent and can swap. The value sits in the engineering around it, which is why two companies using the same model can end up with completely different results.That engineering is the discipline this page describes, and it belongs to the wider practice of AI engineering services.
Why most generative AI projects stall before they reach customers
The demo was built on a happy path. It answers the questions someone thought to ask, and nobody knows how it behaves on the thousands nobody tried.
Nothing defines what good looks like. Without an evaluation set, quality is a matter of opinion, so no one can approve a release with confidence.
The answers are not grounded. A system that reasons from general knowledge instead of your documents will invent details that sound correct and are not.
Security was left until last. Once real data enters the picture, questions about exposure, retention, and access control stop the project cold.
It sits outside the workflow. A separate window that staff must remember to open gets abandoned, however good its output is.
Nobody owns it after launch. Usage patterns shift and models change underneath, and without ownership the system quietly degrades.
What we build with generative AI
Use case discovery and shaping
We work through candidate use cases with you and pick the ones where this technology genuinely fits, then define the quality bar each has to clear before it ships. Weak candidates get named early, before budget follows them.
LLM application development
We build the product around the model: the interface, the prompting and orchestration, the fallbacks, and the integration with your systems.See our LLM engineering services
Retrieval and knowledge grounding
We connect the system to your documents and databases so answers cite your material rather than guesswork, with sources a user can check.See our RAG implementation work
Agents and workflow automation
We build systems that take actions across your tools within limits you set, with human checkpoints where the stakes justify one. See our AI agent development
Model selection, integration, and tuning
We match the model to the job on quality, latency, and cost, keep the layer swappable, and tune or fine-tune only where it earns its cost over better retrieval and prompting.
Evaluation, guardrails, and operations
We build the test suite that proves quality, the controls that contain behavior, and the monitoring that catches drift after release.See our LLMOps practice
What does a generative AI build actually cost?
Cost tracks with the scope of the system, the data engineering it needs, and the reliability bar it has to clear, not a fixed package price. We scope it up front against your use case, so you are never signing a blank check. Get a quick figure in minutes, then talk to an engineer about your specifics.
How we take a working prototype into production
Ground it in your material
We connect the system to your documents, records, and policies so its answers come from your material, with citations a reviewer can follow back to the source.
Define quality, then measure it
We build an evaluation set from real questions and expected outcomes, so quality is a number that a release either clears or does not, rather than a matter of opinion.
Contain what it can do
We set boundaries on the actions the system can take, defend against prompt injection, and add human checkpoints wherever a mistake would be expensive.
Run it like software
We deploy with version control over prompts and models, monitor output quality and cost in production, and keep improving it on what real usage reveals.
The stack behind the systems we ship
Target stack
Models and orchestration
Retrieval and data
Application and operations
What happens at each stage
How we choose and govern the tooling
What separates a demo from a system you can put in front of customers
Built for the way your industry is judged
Healthcare
We know a clinical or patient-facing answer carries consequences a marketing draft does not, so we ground output in approved sources, keep protected health information inside your environment, and put a clinician in the loop where it belongs.
Financial services
We understand that an auditor will ask why the system said what it said, so we keep retrieval traceable and every interaction logged and reviewable.
SaaS and ISVs
We know your users judge an AI feature against the best they have used elsewhere, so we build for latency and cost per request from the first release, not after the bill lands.
Industries we already understand
Healthcare
Ecommerce
Fintech
Travel and Tourism
Security
Automobile
Stocks and Insurance
Restaurant
Built to pass review from users, auditors, and security teams
A capable system is worth nothing if it cannot pass review. We build current security controls into the work rather than bolting them on afterward, so what we deliver can face users, auditors, and security teams without another round of rework.
Which standards apply depends on the market you operate in. The security practices below apply to every engagement. The regulatory items apply where your system handles the data they govern, and we scope that with you during discovery.
Governing what these systems are allowed to do, and proving it later, is its own discipline. See our data governance for AI practice. For systems that handle health data, we build to HIPAA standards; to understand the standard itself, see what HIPAA requires.
How we get you from an idea to a system in production
A structured path from candidate use case to a running system, with a decision point at every step and something you can judge at the end of each.
1. Frame the use case
We agree what job the system does, who it serves, and what a good answer looks like, so you decide what goes forward on evidence rather than enthusiasm.
2. Map the data
We find the content that has to inform the answers, check its condition and permissions, and tell you plainly if it is not ready.
3. Prove it on your material
We build a working version on your real content and test it against real questions, so you decide whether to fund the full build on results, not a slide.
4. Set the quality bar
Together we define the evaluation set and the pass mark, and you approve the number the system has to clear before release.
5. Build, harden, and integrate
We build it into your workflow, add guardrails and access control, and review with you in short cycles rather than one long silence.
6. Release and operate
We roll it out to a controlled group first, monitor quality, cost, and drift, then widen it and keep improving on real usage.
The team that owns whether this works
These projects go wrong when the work is split between people who understand models and people who understand production, with nobody accountable for the join. On our engagements that line does not exist. An AI engineer owns the system design, the retrieval strategy, and the prompting and orchestration. A data engineer owns the pipelines that keep the content fresh and correctly permissioned. A QA lead owns the evaluation set and the pass mark, and has the authority to hold a release. A solutions architect owns how the system meets your existing software and your security model.
Our depth shows in four specific places. We are strong at retrieval over messy enterprise content that was never written to be machine-read. We are strong at building evaluation sets that catch the failures users would have found. We are strong at integrating these systems into software people already use, rather than beside it. And we are strong at operating them in regulated environments where the audit question comes later. We do not claim to be equally deep in everything, and we will tell you where a research group fits better than an engineering one.
We work as an extension of your team, not a black box down the hall. You see the evaluation results, the decision points, and the cost per request, and you own the code and the IP from the first commit. When you need more hands as the work scales, you can add forward-deployed engineers to the same team rather than starting over with a new one.
Why enterprise leaders choose Cabot for production-grade generative AI development
Production, not proof of concept
We are judged on what runs in front of users, not what shows well in a meeting. Evaluation, guardrails, and operations are in scope from the start, not a later phase you fund separately.
Model-neutral by design
We match the model to the job and keep that layer swappable, so a better or cheaper option next quarter is a change you can make rather than a rebuild you have to justify.
Security and compliance built in
Access control, audit logging, and prompt-injection defense are part of the build. Where a standard applies, such as HIPAA in healthcare or GDPR for EU users, we build to it.See what HIPAA requires.
Grounded in your own material
We connect systems to your documents and records so answers carry sources a reviewer can check, which is what turns an interesting tool into one people trust.
Senior engineers who own the calls
People who decide the architecture and review every change before it merges, not junior contractors following a script. You own the code and the IP from day one.
A full engineering practice behind it
Agents, retrieval, and operations each have a deeper practice within our AI engineering services, so the work does not stop at the edge of one specialty.
Where to go next, depending on what you need
You know the answers must come from your documents
If grounding and citations are the whole point, start with RAG implementation
You want it to act, not just answer
If the system needs to complete tasks across your tools, look at AI agent development
You have something built and it is fragile
If the problem is reliability and cost in production, start with our AI engineering services.
Our Clients
Generative AI development is the work of building an application around a generative model so it performs a specific job for your business reliably, using your own data and rules. It covers connecting the system to your content, defining and measuring quality, adding guardrails, integrating it with your software, and operating it once real usage arrives. The model is a component; the engineering around it is what makes the result dependable.
Cost tracks with the scope of the system, the data engineering it needs, and the reliability bar it has to clear. We scope it up front against your use case, so you are not signing a blank check, and we measure running cost per request during the build so the operating bill holds no surprises. For a quick figure, try our Cost Calculator.
A public assistant gives your staff a general tool that knows nothing about your business. A built system reads your documents, follows your policies, connects to your software, declines what it should decline, and can be measured and improved. If the job is general drafting, a subscription is often enough. If the job depends on your data, your rules, or your workflow, it needs to be built.
Two ways, used together. We ground answers in your own content so the system retrieves real material rather than reasoning from general knowledge, and we cite the source alongside the answer. Then we measure it: an evaluation set of real questions with known good answers, scored on every release, so accuracy is a number you can hold us to rather than a hope.
Yes. We build retrieval-augmented generation systems that ground answers in your own content with traceable sources, and agents that take actions across your tools within limits you set. Each has a deeper practice within our AI engineering services.
A focused use case usually reaches a working version on your real content within a few weeks, and a first production release inside a quarter, depending on the state of your data. Larger programs roll out use case by use case. We agree the scope and the quality bar during discovery, before the build starts.
We keep regulated or sensitive data inside your environment rather than sending it to public endpoints, and we do not use your data to train third-party models. Access control, audit logging, and prompt-injection defense are built into the system rather than added later. Where a standard such as HIPAA or GDPR applies, we build to it.
We are model-neutral and match the choice to your product on quality, latency, and cost, working across the major providers and open-weight options that can run in your own environment. The model layer stays swappable by design, so you are not tied to one vendor as the field moves.