AI Engineering / 3 systems in production
Two-language voice memos from the field. Every recorded call in a contact centre. Social conversations too large to read. We have shipped the pipelines that turn all of it into structured, scoped, auditable intelligence — then did the hard part: made it boring enough to trust.
Live pipeline
Follow one voice note from the moment a field rep records it to the alert a manager receives. Every step is written in plain words; flip on engineering detail for the model names, vector sizes and retry rules underneath. Click any input to follow that one instead.
Shipped
Each one started with data nobody could read at scale and ended with a screen someone checks every morning. The clients stay anonymous. The engineering is ours to tell.
Field reps record Hindi-English voice memos at pharmacies and clinics. We transcribe and diarize them on local GPUs, extract and canonicalize brands and competitors, tag against a controlled taxonomy, score quality, and roll it all into 15-minute dashboards — with row-level security so one region never sees another's data.
Large-scale social data, embedded and clustered into communities, then rendered as an interactive graph where analysts trace relationships, spot the accounts that bridge communities, and watch narratives travel — instead of scrolling through them.
Each agent call is transcribed, evaluated against rubric prompts, and distilled into a summary, key call drivers, action items and separate agent and customer sentiment — streamed to live dashboards that show supervisors exactly where to step in.
Grounded Q&A
Every answer comes from your own records, only the ones the asker is allowed to see, with the source attached. Numbers below are illustrative. Try the last prompt, the one most vendors hope you never ask. Flip on engineering detail to watch every call it makes.
How we keep it trustworthy
The demos above are the fun part. These are why the systems behind them survive contact with real users, real auditors and 3 a.m. retries.
Dashboards and trend questions answer from pre-computed rollups in milliseconds. Zero LLM calls on the query path.
Scope in the API, row-level security in the database, filters in the vector store. Access control is never a post-filter.
Every pipeline stage is idempotent: it clears what it owns and rewrites. A crash overnight is a re-run, not an incident.
One interface for every model. Local GPU today, cloud API tomorrow, and the business logic never learns the difference.
Append-only audit trail, versioned vocabularies. Last quarter's numbers still reproduce next year.
Bring the messiest data you own. In thirty minutes we will tell you what a pipeline for it looks like, what it costs to run, and what we would refuse to automate.