For CTOs and CIOs, production LLM systems built in your stack and handed to your engineers.
Bluegrass builds systems on large language models that read documents, check them against your rules and draft work for people to review. We build in your repositories and cloud accounts, with your engineers in the code from the first week.
We hold no reseller agreements or revenue share with any vendor, so each system uses the model that clears your accuracy bar at the lowest cost, and it can be swapped when a better one appears.
What technical leaders need from an AI partner
Evidence rather than demos
Accuracy measured on a labeled set of your own documents before launch and monitored after, with every change run against the same set.
Your architecture
Systems that run in your cloud accounts, read data in place and connect to your core systems through the interfaces they provide, under your security and deployment standards.
No lock-in
Code your team owns and can change, with model providers that can be swapped behind a stable interface and no per-seat license underneath.
Governance that holds up
Version control for prompts, models and rules, a record of every output and the documentation that a model validation, an auditor or an examiner will ask for.
How the systems are built
Document pipelines
Native files, scans and spreadsheets parsed into structured data, with low-confidence fields flagged rather than guessed and every value linked to its source page.
Your procedures as instructions
Your rules, checklists and procedures written into the instructions and checks the system follows, kept in version control and reviewed like the rest of your code.
Retrieval and tools
Answers grounded in your own policies and records through retrieval, and actions taken only through the interfaces your systems expose, with permissions scoped to each task.
Review queues and staged autonomy
Outputs routed to people through a review queue, with the thresholds for automatic handling set by the business and raised only as measured accuracy supports.
Evaluation and observability
A golden dataset, regression tests, tracing, cost controls and fallbacks for the days a model is wrong or unavailable, all handed over with the system.
What your engineers inherit
The code
In your repositories from the first commit, with tests that run in your own pipeline.
The evaluation set
The labeled examples and the harness that runs them, so that your team can measure every change after we leave.
The documentation
Architecture, design decisions, operations and model documentation, written for the engineer who maintains the system and the reviewer who audits it.
The runbook
How to operate, monitor, upgrade and roll back the system, with working sessions to hand it over.
Questions technical leaders ask
- Which models do you use?
- Whichever clears the accuracy bar at the lowest cost for each task, benchmarked on your own documents. If data cannot leave your environment, that narrows the choice, and the evaluation shows what is achievable.
- Do you build traditional machine learning models?
- Our practice is built on large language models. Statistical models trained on your history, such as credit scoring, fraud detection or pricing, are a different discipline, and if that is what you need, we will say so.
- How do you work with our engineering team?
- Your engineers work in the repository, the weekly demos and the design decisions from the first week, and we are done when they can ship the next change without us. We can also help you hire and mentor if you are building in-house.
- How is data handled?
- The systems run in your cloud accounts and read data where it already lives, and the model provider and its data retention terms are agreed with your security team before any data is processed.
- What does it cost to run?
- Cost per document or case is measured during the evaluation and tracked in production, so you know the running cost before launch and can switch to a cheaper model that clears the bar.
A senior team that has done this before
We are a small, entirely senior team with fifty years of combined AI engineering, multiple granted patents and Y Combinator founders, from Yale, MIT, McKinsey, Goldman Sachs, Microsoft and Vista Equity Partners. The people who scope your work are the people who write the code.
Selected work
We built the AI transformation roadmap for a top-50 P&C brokerage, and it is saving the firm millions of dollars in vendor fees it did not need to pay.
Clients include some of the largest banks, RIAs, fintechs and AI labs, as well as a top-3 P&C carrier, a top-10 P&C brokerage, a top-5 trucking brokerage, a leading specialty MGA and a leading trucking MGA.
We run only 3 to 5 engagements at a time, so that every client works directly with the senior team.
About the team- Dozens
- of insurance companies, RIAs, banks and fintechs we have worked with
- 50 yrs
- combined AI engineering experience
- 10+ Patents
- granted to members of the team
- 100%
- of the IP we build is assigned to you
Put your hardest questions to the people who would build it.
The first call is with an engineer rather than a salesperson, and a good place to test our approach against your architecture, data and constraints.