Built to work in the real world
Most LLM demos work on a happy-path prompt and fall apart the moment real users, real documents and real edge cases arrive. We build the other kind — retrieval-augmented systems, copilots and semantic search that answer from your actual content, cite their sources and stay reliable when the questions get messy. You get grounded answers over your own data, not a general chatbot guessing from what it half-remembers.
We start with the failure modes, not the model. We map where hallucination, stale context and prompt injection would actually hurt you, then build the retrieval, chunking and grounding to close those gaps — and an evaluation harness so 'it feels better' becomes a number you can track across every change. When a new foundation model ships, you swap it behind a stable interface and re-run the evals instead of praying it still works.
Where it delivers
A few of the ways teams put this to work — each one something we can scope and ship.
Knowledge assistant
Answer employee or customer questions from your documentation, policies and tickets with citations back to the source passage.
Document copilot
Draft, summarize and extract structured fields from contracts, reports or filings inside the tools your team already uses.
Semantic search
Replace brittle keyword search with meaning-based retrieval that finds the right passage even when the wording is different.
Support deflection
Resolve common inbound queries automatically and hand off to a human with full context when confidence is low.
What's included
Tech we build with
A path from idea to production
The disciplined path we follow on every engagement of this kind.
Grounding audit
We inventory your content, its structure and freshness, then define what a correct, cited answer looks like for each question type.
Retrieval build
We design chunking, embeddings and retrieval so the model is fed the right context before it ever generates a token.
Eval harness
We build a graded test set covering accuracy, citation fidelity and refusal so quality is measured, not guessed.
Guardrail hardening
We add prompt-injection defenses, grounding checks and fallbacks before anything is exposed to real users.
Everything you walk away with
Questions, answered
How do you stop the assistant from hallucinating?
We ground every answer in retrieved passages from your own content and add grounding checks that flag or refuse when support is weak. The evaluation harness then measures citation fidelity on a fixed test set, so hallucination rate is tracked as a number across every change rather than judged by feel.
Can we use our own data without it training the model?
Yes. Retrieval-augmented generation feeds your documents to the model at query time rather than baking them into weights, so nothing is used for training. We can also deploy against providers and configurations that contractually exclude your data from training and keep it inside your own infrastructure.
What happens when a better foundation model comes out?
We put the model behind a stable interface so it can be swapped without rewriting your application. You re-run the evaluation suite against the new model and compare scores directly, so upgrades are a measured decision instead of a leap of faith.
Do you support open-source or self-hosted models?
Yes. Where privacy, cost or latency demand it, we deploy open-weight models on your own infrastructure through the same retrieval and evaluation stack. We help you weigh the trade-offs against hosted APIs so the choice fits your constraints rather than a preference.
Ready to build with Generative AI & LLM Apps?
Tell us what you're building and we'll map the fastest reliable path to production.