Enterprise AI Engineering Services
Most enterprise AI never leaves the lab. A demo impresses the room, then stalls on the hard parts: grounding it in real data, proving it is safe to ship, and running it reliably once real users arrive. The gap between a working prototype and a production system is where most budgets disappear.
Cabot closes that gap. It is the discipline of building, shipping, and operating applications on top of models, and it is exactly what we do. We design the system around the model, ground it in your data, put evaluation and guardrails in place before launch, and run it with the monitoring and MLOps that keep it dependable. You get AI that reaches production and stays there.
LLM apps · AI agents · RAG · MLOps & LLMOps · Evaluation | Security and governance ready
No obligation. Your details stay private.
$19.8B
Projected enterprise generative AI market by 2030, up from $2.9B in 2024, a 38.4% CAGR
30%
Share of generative AI projects expected to be abandoned after proof of concept by the end of 2025
700+ projects
Delivered for 140+ clients since 2010, the engineering track record behind getting AI to production
What is AI engineering, and how is it different from AI development?
AI engineering is the discipline of building, shipping, and operating production applications on top of AI models, where the model is one component of a larger system that also handles data, retrieval, orchestration, evaluation, and reliability. It is closer to software engineering than to data science. Where AI and machine learning development focuses on building and training the models themselves, the engineering discipline focuses on everything around the model that turns it into a dependable product: grounding it in your data, connecting it to your tools, measuring whether it behaves, and running it in production without surprises. The two are complementary, and our AI and machine learning development team leads the model side when a project needs it.
Why enterprise AI stalls before it reaches production
The demo is the easy part. What follows is where most initiatives lose momentum. Naming the failure points makes them addressable.
No evaluation framework. Without a way to measure quality, nobody can say whether the system is good enough to ship, so it never ships.
No MLOps or LLMOps. A prototype running on someone's laptop has no path to deploy, version, or roll back safely in production.
Hallucination and trust gaps. A model that is confidently wrong some of the time cannot go in front of customers until that risk is measured and contained.
Data governance risk. Feeding sensitive data to a model without controls raises privacy, residency, and compliance questions that block launch.
Cost and latency at scale. What is cheap and fast for one test user becomes slow and expensive across thousands, and no one budgeted for it.
Prototype-grade architecture. Built to demo rather than to run, the system needs re-engineering before it can carry real traffic.
The AI engineering services we deliver
Generative AI and LLM development
We build applications on top of large language models, from copilots to document processing, engineered for accuracy and safe behavior.See generative AI development and LLM engineering
AI agents and multi-agent systems
We build agents that take actions across your tools, with the orchestration and guardrails that make autonomy safe. See AI agents and multi-agent systems development.
RAG and enterprise knowledge systems
We ground models in your own content with retrieval-augmented generation, so answers are accurate and traceable to a source. See custom RAG implementation.
MLOps and LLMOps
We put the deployment, versioning, monitoring, and evaluation pipelines in place that let an AI system run reliably and improve safely. See LLMOps consulting
Intelligent process automation
We combine AI with automation to take repetitive, judgment-light work off your team, measured against real throughput. See robotic process automation.
Data foundation and predictive analytics
We get your data ready to power AI and build the models that forecast what matters. See data governance for AI and predictive analytics
What does an AI engineering engagement cost?
Cost tracks with the scope of the system, how much 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 get AI out of the lab and into production
Grounded in your data
We connect the model to your own content and systems through retrieval, so answers are based on your reality rather than a generic guess.
Measured before it ships
We define what good looks like and build evaluation that scores the system on it, so the decision to launch rests on evidence, not a hunch.
Guarded against failure
We add the guardrails, fallbacks, and human checkpoints that keep the system safe when a model does something unexpected.
Run and improved
We deploy with monitoring and the LLMOps pipeline that catches regressions early and turns production signals into the next improvement.
The AI engineering lifecycle, and where humans stay in control
What separates a proof of concept from a production AI system
Built for the way your industry adopts AI
Healthcare
We know clinical data cannot leak and an AI answer can carry weight, so we ground systems in your records, keep data in your environment, and build to HIPAA.
Financial services
We understand that a wrong or unexplained answer is a compliance event, so we build traceability, evaluation, and audit trails into every system.
SaaS and ISVs
We know an AI feature has to ship on a roadmap and scale with your users, so we engineer for cost, latency, and clean integration from the start.
Enterprise operations
We understand that internal AI has to work inside a web of existing systems, so we connect it to your tools and data without disrupting them.
Retail and logistics
We know demand and volume swing hard, so we build agents and forecasting that hold up when traffic and order flow spike.
Legal and professional services
We understand that answers must cite their source, so we lean on retrieval and evaluation to keep AI grounded and defensible.
Engineered to pass security, privacy, and AI governance review
An AI system is worth nothing if it cannot pass review. We build the security and governance controls into the system rather than bolting them on afterward, so it can face security teams, auditors, and a responsible-AI review without a rebuild.
The security practices below apply to every engagement. The AI-specific and regulatory items apply where your system handles the data or decisions they govern, and we scope that with you during discovery.
For products handling health data, we build to HIPAA standards and keep regulated data in your environment. To understand the standard itself, see what HIPAA requires. For the governance side of an AI program, see our AI process and governance practice
How we take your AI from idea to a system you can trust
A structured process that moves an AI initiative from a promising idea to a monitored production system, with a decision point at every step and evidence behind each one.
1. Assess and pick the use case
We test candidate use cases against business value and feasibility, so you back the one most likely to reach production, not the flashiest demo.
2. Data and architecture
We get your data ready, choose the model and retrieval approach, and design the system around them before any feature is built.
3. Build the system
We build the application, agents, and integrations in short cycles, with senior engineers reviewing every AI-assisted change before it merges.
4. Evaluate and red-team
We score the system against your criteria and probe it for failures and misuse, so the launch decision rests on evidence.
5. Deploy with MLOps
We ship to production with the pipeline, versioning, and cost controls that let it run reliably and roll back safely.
6. Monitor and improve
We watch it in production, catch drift and regressions early, and turn real usage into the next set of improvements.
The team that gets your AI to production and keeps it there
AI initiatives stall when nobody owns the whole path from model to running system. On our engagements, accountability is clear. An AI architect owns the system design and the choice of model, retrieval, and agent approach. AI engineers own the build and review every AI-assisted change before it merges. An evaluation lead owns the tests and red-teaming that decide whether the system is safe to ship. An MLOps engineer owns deployment, monitoring, and the pipeline that keeps it dependable.
Our depth shows in four specific places. We are strong at grounding models in messy enterprise data with retrieval. We are strong at making agent behavior safe and predictable. We are strong at building evaluation that reflects what the business actually cares about. And we are strong at running AI in production, where reliability and cost cannot slip. We do not claim to be equally deep in everything, and we will tell you where a specialist fits better.
We work as an extension of your team, 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.
Why technology leaders choose Cabot for enterprise AI engineering
Production, not proof of concept
We build for the reliability bar of a real system from day one, so your AI reaches production instead of stalling as a demo.Need more hands as it scales? Add forward-deployed engineers
Model-neutral by design
We do not lock you to one vendor. The model layer can be swapped as the field moves, without re-engineering the system around it.
Security and governance built in
We build privacy, access control, and responsible-AI review into the system, so it is review-ready on day one.See what HIPAA requires.
A real track record
700+ projects for 140+ clients since 2010, real engineering behind the AI, not a portfolio of demos.
Senior engineers who own the calls
People who decide the architecture and review every AI-assisted change. You own the code and the IP from day one.
Real AI and ML model depth
When your system needs a custom or fine-tuned model, our engineers build it. See our AI and machine learning development work.
Where to go next, depending on what you are building
You have a pilot stuck short of production
If a working prototype cannot get deployed and monitored, start with LLMOps consulting.
You want AI that takes actions
If the goal is autonomous workflows, our AI agents practice builds and governs them.
You need AI grounded in your own data
If answers must come from your content and cite a source, start with custom RAG implementation.
You are building an LLM-powered product
For copilots and language features, see generative AI development.
You need the model itself built
When a custom or fine-tuned model is the core, our AI and machine learning development team leads it.
You want to forecast, not just generate
For prediction on your own data, see predictive analytics
Our Clients
AI engineering is the discipline of building, shipping, and operating production applications on top of AI models. The model is one component of a larger system that also handles data, retrieval, orchestration, evaluation, and reliability. It is closer to software engineering than to data science, and it is what turns a model into a dependable product.
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. For a quick figure, try our Cost Calculator.
AI and machine learning development builds and trains the models. AI engineering builds the system around a model that turns it into a working product: grounding it in your data, connecting it to your tools, evaluating its behavior, and running it in production. The two are complementary, and we do both
We ground the system in your data, define and measure quality with an evaluation suite, add guardrails and human checkpoints, then deploy with the MLOps and monitoring that keep it reliable. Most pilots stall because these steps are skipped, and this is exactly the work we do.
Yes. We build AI agents and multi-agent systems that take actions safely across your tools, and retrieval-augmented generation systems that ground answers in your own content with traceable sources. Each has its own dedicated practice under this one.
A focused production use case often reaches a first release in a matter of weeks, while a larger program rolls out in phases. We agree the scope and the reliability bar during discovery, before the build starts.
We stay model-neutral, keep your regulated or sensitive data inside your environment rather than sending it to public endpoints, and do not use your data to train third-party models. We build access control, audit logging, and prompt-injection defense into the system.
We are model-neutral and match the choice to your product, working across the major model providers and the standard engineering tooling for retrieval, evaluation, and operations. The model layer stays swappable, so you are not locked to one vendor as the field moves. This practice sits within our wider product engineering practice.