AI SaaS · Meeting Intelligence
AI Meeting Intelligence Platform
Turning raw meeting conversation into structured, searchable, actionable intelligence - summaries, decisions, and follow-ups people actually trust.
Executive context
An AI-native platform that ingests meeting conversations and produces structured intelligence: concise summaries, decisions, action items with owners, and a searchable memory of what was discussed across many meetings. The product's job is to make the content of meetings usable after they end.
Problem
Meeting audio is long, messy, and low in signal per minute. A single summarization call produces something that reads well and is subtly wrong - misattributed decisions, invented action items, lost context. The real difficulty was making the output trustworthy enough that people would rely on it, and making months of meetings searchable without the answers drifting from what was actually said.
Commercial risk
If users cannot verify an AI-generated decision or action item, trust collapses quickly. That threatens adoption, increases support burden, and makes the product difficult to approve for business-critical workflows.
Solution
We framed the product around trust rather than novelty. Every generated item is traceable back to the moment it came from, so users can verify rather than take it on faith. Summaries are structured - decisions, actions, open questions - instead of a wall of prose, because that's the form the information is actually used in. Search was designed to answer questions across meetings, grounded in transcript, with the source always one click away.
Result
A platform where the AI output was trusted because it could be verified - structured, grounded, and searchable. The evaluation and observability work meant quality could be improved deliberately over time instead of shifting unpredictably with each model change.
Delivery sequence
From exposed risk to production control.
Sequence shown rather than invented calendar dates; actual timing depends on scope, dependencies, and client availability.
Risk and requirements
Clarify the users, commercial exposure, constraints, and evidence needed for success.
Architecture and validation
Resolve high-cost decisions early and validate the riskiest product behavior with real data.
Product build
Ship working vertical slices across interface, services, data, and integrations.
Hardening and launch
Complete QA, observability, operational controls, documentation, and production handover.
Engineering approach
A processing pipeline handled transcription, segmentation, and structured extraction, with each stage independently observable so quality could be measured and improved rather than guessed at. Generated content was grounded in the transcript and constrained to reduce fabrication. Retrieval powered cross-meeting search over embedded transcript segments. The pipeline was built to run asynchronously and scale with load, and evaluation was wired in so regressions surfaced before users saw them.
Key capabilities
- Structured summaries: decisions, action items, open questions
- Every insight traceable back to its source moment
- Contextual search across many meetings, grounded in transcript
- Asynchronous, observable processing pipeline
- Evaluation harness to catch quality regressions
Relevant services
Built as part of professional product engineering work. Client and product identities, proprietary names, and confidential details have been omitted - the interface visuals are stylized representations, not confidential screenshots.
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