Resolve the routine contacts end to end
The questions that make up most of your volume are already answered somewhere in your content. Grounded retrieval finds them, cites them, and hands over cleanly when it can't.
Send a policy document and three real questions. We ground an agent in it before the call.
Illustrative interface. Scores, latencies and counts are sample data, not a performance claim. The screen shows an OptiML answer trace for a Tier-1 support question: the customer's question, the grounded answer carrying two citations to the customer's own documentation, a groundedness gate scoring 0.91 and passing, a trace row showing which engine answered and how long it took, and a note that two unanswered questions were queued for a knowledge curator.
Ingest, deploy, hand over, close the gap
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Step one
Ingest your content
Bring your help centre, policy documents and product content. Upload PDF, DOCX, CSV and Markdown, crawl the site including JavaScript-rendered pages, OCR the scans, or connect the source through one of 30+ connectors.
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Step two
Deploy a hybrid agent
Deterministic FAQ matching first, then intent routing, then a grounded LLM. The cheap, certain path answers the questions it can, and the model only runs on what is left.
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Step three
Set the handover triggers
The triggers are low confidence, negative sentiment, an explicit request for a human, or repeated failure on the same intent. The conversation carries its transcript, its summary and the reason it moved.
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Step four
Close the gaps it finds
Questions the knowledge base couldn't answer are clustered and surfaced with the conversations attached. Successful resolutions are mined into draft answers. You review and accept. Nothing publishes itself.
Two things, honestly listed
The gap loop then tells you exactly which parts of your content are missing, ranked by how often they are hit. That ranking is a better content roadmap than an audit.
- Your existing help content. In whatever state it is in. A messy knowledge base is the normal starting condition, not a blocker.
- A queue to hand over to. One is enough to start. The handover carries the transcript, the summary and the trigger that fired.
Knowledge is scoped per agent. The billing agent doesn't answer from the HR handbook, and the public agent doesn't quote the internal runbook. There is no organisation-wide fallback that quietly widens the scope.
Password, billing, integrations — and the rest to a human
A SaaS company routes password, billing and integration questions to an AI agent across web chat and email. Containment on those intents runs high; everything else reaches a human with the transcript attached. Containment is reported per intent, per queue, per day.
The four handover triggers, what fires each one, and what the human agent receives when it does.
| Trigger | What fires it | What the human agent gets |
|---|---|---|
| Low confidence | The groundedness gate fails, or retrieval returns nothing that supports an answer | The question, what was retrieved, and why it wasn't enough |
| Negative sentiment | Live sentiment scoring crosses the threshold you set for the queue | The transcript with the sentiment trend across the conversation |
| Explicit request | The customer asks for a person, in any phrasing | An immediate handover, with the summary so far |
| Repeated failure | The same intent fails more than once in the same conversation | The failed attempts, so the agent doesn't repeat them |
| Unanswered question | No trigger — the conversation continues, and the gap is logged | Nothing now. The curator gets it, clustered, with the conversation attached |
Why this one doesn't hallucinate
Four-stage retrieval
Dense vectors, BM25 lexical search, reciprocal rank fusion, then a cross-encoder rerank against the actual question.
The groundedness gate
The drafted answer is checked against what was retrieved. A hallucination grader and a citation auditor catch unsupported claims before anything is sent.
Citations, every time
Every answer names the source it came from, so a supervisor can check it and a customer can be pointed at it.
15-dimension scoring
Every interaction is scored, AI-handled and human-handled alike, so containment is never bought with a worse conversation.
The parts this runs on
Four platform capabilities carry this use case. The analytics that tell you whether it is working sit in the same product, not in an export into someone else's.
Knowledge grounding
Hybrid retrieval, query rewriting, citations, and a gate before the agent answers. KnowledgeHybrid engine
Five engine types — prompt, FAQ, NLU, flow and hybrid — on one agent. Agent & flow builderWarm handover
The transcript, the summary and the reason arrive with the conversation. Agent DeskAnalytics
Containment per intent, topic mining and full-text conversation search, in 76 reports. AnalyticsWe publish outcome figures only with the customer's written approval, so this slot stays empty until one is signed off. In the meantime the mechanism above is the honest version.
What teams switch on next
Your agents stop looking things up
The same knowledge, pointed at the humans handling what the agent handed over. Agent assist and copilotServe customers where they already message
The same agent and the same knowledge, on a channel people actually open. WhatsApp customer serviceRetire the menu tree without changing your number
The same grounding, answering the phone instead of the chat widget. Replace your IVRThe platform underneath
Everything on this page is the OptiML CX Platform configured for Automate Tier-1 support, not a separate product. These are the modules it leans on.
Bring three questions you get every day
Send the content that should answer them. We ground an agent in it before the call, then you ask the questions yourself, including the one your current bot gets wrong.
- 30 minutes
- A working agent grounded in your content
- No slide deck unless you want one
Book a demo
30 minutes, on your own content. No slide deck unless you want one.
Your details stay private. We never share them.
