Conversation & History Intelligence
Turn chat transcripts, call logs, and session history into structured insight, alerts, and product feedback.
Mine signal from every session
Every AI conversation is a data asset. We build pipelines that ingest session history, classify intent, extract entities, detect failure modes, and surface trends — so product and support teams can see what users actually ask, where the model struggles, and what to ship next.
Outcomes
Our approach.
Capture everything
Every turn, tool call, retrieval hit, and model response becomes a traced event with a stable ID — the foundation for any later analysis.
Normalize & enrich
Intent classification, topic clustering, entity extraction, sentiment, and failure-mode tagging run in batch over the stream.
Surface signal
Dashboards and alerts for product, support, and ML teams. Everyone sees the same ground truth — what users actually ask.
Close the loop
Top failure modes flow back into the eval set, and fixes are measured on real historical traffic — not synthetic tests.
What you get.
Production-shaped, from day one.
// Per-session enrichment pipeline
export async function enrich(session: Session) {
const intents = await classifyIntents(session.turns)
const entities = await extractEntities(session.turns)
const failures = await detectFailures(session, {
checks: ["hallucination", "refusal", "escalation"],
})
return store.upsert({
session_id: session.id,
intents,
entities,
failures,
cost_usd: sumCost(session),
sentiment: await score(session),
})
}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.
- Post-session coaching summaries and progress tracking
- Patient intake summarization for care coordination
- Support analytics: deflection, escalation, and satisfaction
- Prompt and model regression detection
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
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.
Do we need to rebuild our logging pipeline?
+
No — we can sit on top of Langfuse, LangSmith, or your existing traces and warehouse. If you don't have tracing yet, we set it up.
How quickly can we find failure modes?
+
Typically within hours of ingestion. Automated detectors surface likely failures, then humans review a small sample to confirm and label.
Is this just analytics or does it feed back into the system?
+
Both. Insights are the goal, but surfaced failures flow into the eval set so future model or prompt changes are tested on real traffic patterns.
Related solutions.
Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Agents & Workflow Automation
Agentic workflows that read, write, and act across your existing tools — with human-in-the-loop where it matters.
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.
