Service
We integrate GPT-class models into real products — search, copilots, document intelligence and generation features that are fast, safe and grounded in your data.
The problem
The demo was easy. Production isn't.
Every team has a ChatGPT prototype. Very few have AI features that are accurate, fast, affordable at scale and safe to put in front of customers.
Hallucinations that erode user trust in one bad answer
Token costs that explode with usage
Latency that makes AI features feel broken
No evaluation framework — quality is anyone's guess
Our solution
Production-grade AI engineering
Retrieval pipelines grounded in your data, model routing that balances cost and quality, and evaluation suites that catch regressions before users do.
RAG architectures with your documents, databases and APIs
Multi-model routing across OpenAI, Anthropic and Gemini
Streaming UX patterns that make responses feel instant
Evals, guardrails and observability for every AI feature
Benefits
What this unlocks for you
Grounded, not guessing
Retrieval-augmented generation keeps answers anchored in your actual data.
Costs under control
Caching, routing and prompt engineering that cut inference costs by an order of magnitude.
Measurably good
Automated evaluation suites that quantify quality and catch regressions on every change.
Safe by design
Guardrails, moderation and data-privacy boundaries appropriate for enterprise use.
Technology
The stack behind the work
Chosen for reliability today and headroom tomorrow.
Our process
From first call to production
Use-case discovery
We find where AI genuinely moves your metrics — and where it's just a gimmick.
Data & retrieval design
Embedding pipelines, chunking strategies and hybrid search over your knowledge.
Prototype & evaluate
Rapid iterations measured against a golden dataset, not vibes.
Productionize
Streaming, fallbacks, rate limiting, cost monitoring and abuse prevention.
Improve continuously
Real usage feeds the eval suite; models and prompts improve every sprint.
Case study — Legal services
Legal-tech platform
Challenge
A legal platform wanted contract analysis for 40,000 users — but early prototypes hallucinated clauses, a dealbreaker in law.
Solution
Citation-grounded RAG over their document store with clause-level retrieval, a Claude/GPT routing layer, and an eval suite of 1,200 annotated contracts.
96%
clause extraction accuracy
−87%
cost per document vs prototype
11 min
average review time, down from 3h
FAQ
Common questions
OpenAI, Anthropic, Google Gemini and open-weight models when data must stay in your VPC. We usually recommend a routing layer so you're never locked into one provider's pricing or outages.
Ready to talk about ai integrations?
One call is enough to know whether we're the right team. No pitch decks — just engineers who ask good questions.