Generative AI, LLM & RAG
Generative AI that cites its sources, knows what it doesn't know, and improves with every evaluation cycle.
Grounded language systems that earn trust.
The hard part of generative AI is not generation — it's grounding. We engineer retrieval pipelines with hybrid search, re-ranking, and chunking strategies tuned to your corpus, then layer evaluation so quality is a number you can track, not a vibe.
We design prompt architectures, structured outputs, and guardrails that make LLM behaviour predictable, and we instrument everything so regressions surface before your users find them.
Why teams choose this
Grounded answers
Hybrid retrieval and citation keep responses tied to source truth.
Measured quality
Eval suites turn 'it feels better' into tracked metrics.
Structured outputs
Schema-constrained generation for reliable downstream use.
Cost & latency control
Caching, routing, and right-sized models manage spend.
What we build into every system
RAG pipelines
Hybrid search, re-ranking, and corpus-tuned chunking.
LLM application dev
Prompt architecture, routing, and structured outputs.
Knowledge ingestion
Connectors, parsing, and embedding pipelines.
Evaluation harness
Faithfulness, relevance, and answer-quality metrics.
Guardrails
Grounding checks, PII redaction, and refusal logic.
Fine-tuning
Domain adaptation when prompting isn't enough.
RAG architecture
A grounded retrieval pipeline with re-ranking, generation, and evaluation.
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Where it creates leverage
Enterprise knowledge assistant
Grounded answers over internal docs with citations.
Clinical documentation
Draft structured notes from clinician dialogue.
Contract analysis
Surface clauses, risks, and obligations on demand.
Tools we reach for
Let's build something that ships.
Bring us a problem. We'll tell you honestly whether AI is the right tool — and exactly how we'd build it.