Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Grounded chat over your own data
We design and ship chat experiences that understand your domain. Your users ask questions in natural language; the system retrieves the right context, generates grounded answers, and cites the source. We handle conversation state, multi-turn context, tool calls, streaming, auth, and rate limits end to end.
Outcomes
Our approach.
Scope & success criteria
We pin down the top 20 real user questions, the data sources they touch, and what a "good" answer looks like — measurable, not vibes.
Retrieval & grounding
We build a permission-aware retrieval layer over your data, with hybrid search, reranking, and citation tracking by default.
Guardrails & eval harness
PII detection, out-of-scope refusal, prompt-injection resistance, and a faithfulness eval that gates every change.
Ship & observe
Deploy to your cloud with streaming, tracing, cost attribution, and dashboards — so the team sees what users actually ask.
What you get.
Production-shaped, from day one.
// Grounded chat with citation + scoped retrieval
const response = await chat.ask({
query: "What medications am I currently taking?",
user: patient.id,
scope: { permissions: "own_records_only" },
retrievers: ["ehr", "notes", "labs"],
evaluators: ["faithfulness", "pii_leak", "refusal"],
stream: true,
})
for await (const chunk of response) {
render(chunk.text, chunk.citations)
}
// -> { trace_id, cost_usd, latency_ms, citations[] }A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- Patient-facing health information chat over mapped EHR data
- Internal knowledge assistants for support and ops teams
- Customer-facing product Q&A with docs and changelog
- Coaching assistants that reference session history and goals
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
Coach session intelligence & program updates
Turned coaching session notes and history into structured program updates, progress summaries, and next-action recommendations.
What teams usually ask.
How do you prevent hallucinations?
+
Citation-grounded generation, faithfulness evals on every change, and configurable refusal behavior for out-of-scope queries. We measure hallucination rate and gate deploys on it.
Can the chat access private or user-specific data safely?
+
Yes — we build permission-aware retrieval so each user only sees context they're entitled to. Access is enforced in the retrieval layer, not just the prompt.
Which model do you use?
+
We route across providers (OpenAI, Anthropic, Bedrock, Azure OpenAI) based on the task, cost, and latency requirements. Model choice is never a lock-in.
Related solutions.
Retrieval-Augmented Generation
End-to-end RAG pipelines from ingestion to retrieval to answer generation, built for accuracy and cost control.
Conversation & History Intelligence
Turn chat transcripts, call logs, and session history into structured insight, alerts, and product feedback.
Cloud AI Infrastructure
We stand up the platform layer so your AI systems are secure, observable, scalable, and cost-governed from day one.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
