Your LLM prototype works. Now make it survive users.
RAG pipelines, agents and LLM integrations built with evaluation, guardrails and cost control, so the demo becomes a feature you can charge for.
Talk to an engineerThe gap nobody warned you about
- It answers beautifully in the demo and confidently invents things in front of a customer.
- Nobody can say whether last week's prompt change made it better or worse.
- The bill arrives and a single feature costs more than the rest of the infrastructure.
- It cites a document that does not say what the answer claims it says.
- Latency is fine until the model is slow, and then the whole page hangs waiting for it.
- It works for the ten documents in the test folder and falls apart at ten thousand.
What we build
Retrieval that actually retrieves
Chunking tuned to your documents, hybrid vector and keyword search, reciprocal rank fusion to merge rankings, and diversity filtering so the context window is not five copies of the same paragraph.
Answers grounded in your sources
Responses constrained to retrieved evidence, with citations the user can open and check. The model's job is to summarise your data, not to remember the internet.
Evaluation before deployment
A fixed question set with expected outcomes, scored on every change, so a prompt or model swap is a measurable decision instead of a hunch.
Guardrails and failure paths
Input validation, output schemas, refusal handling, timeouts and fallbacks, so a bad model day degrades the feature instead of the product.
Agents that stay on a leash
Tool calling and structured outputs with bounded steps, explicit permissions and audit trails, because an agent that can act needs limits on what it can act upon.
Cost and latency under control
Model routing by task difficulty, caching, batching, token budgets and streaming, plus per-feature cost tracking so spend is attributable.
Typical projects
Internal knowledge assistant
Ingest the company's documents, make them searchable and answerable with citations, and keep the index current as documents change. Idempotent ingestion so re-running is safe.
Rescue a prototype that hallucinates
Measure how often it is actually wrong before changing anything, then fix the causes in order: usually retrieval quality first, grounding second, guardrails third.
Put an evaluation harness around an existing feature
Build the regression suite the feature never had, so the team can iterate on prompts and models without shipping a downgrade.
Cut the cost of an AI feature
Profile spend per request, route easy cases to cheaper models, cache what repeats, and trim context that was never earning its tokens.
Document processing at volume
Extraction and classification over large document sets, with structured outputs, confidence handling and a human review path for the uncertain cases.
What you get
- A working pipeline in your infrastructure, not a notebook
- An evaluation suite you can run in CI, with a baseline score to beat
- Cost and latency numbers per request, and where they come from
- Traces you can read when an answer is wrong, showing what was retrieved and why
- Documentation of what the system will not do, which matters as much as what it will
Questions we get asked
Do we need to fine-tune a model?
Almost never, and it is usually the expensive answer to a retrieval problem. Better chunking, hybrid search and grounding fix the majority of quality complaints at a fraction of the cost and effort. Fine-tuning earns its place for tone, format or a narrow domain vocabulary, after retrieval is already good.
How do you measure whether an AI feature is good?
A fixed set of representative questions with expected outcomes, scored automatically on every change, tracking correctness, grounding, refusal behaviour, latency and cost. The number matters less than the fact that it moves in a direction you can see before users do.
Which model provider do you use?
Whichever fits the task, and we keep the integration behind an interface so switching is a configuration change rather than a rewrite. Model pricing and capability move fast enough that locking your architecture to one vendor is a risk in itself.
Is our data used to train someone's model?
Not if the integration is set up correctly, and that is a deployment decision we make explicitly with you rather than inherit from a default. It is worth deciding before the first request, not after.
Start a conversation
Tell us what you are building, or what is currently breaking. You will get a straight answer from an engineer, not a sales script.