Machine learning & applied AI
NLP, predictive ML and MLOps built for production, not notebooks.
The model is still running, and still being watched, a year after the pilot ends.
A question lands in someone’s queue.
“Is sampling witnessed for Q3 batches?” — the kind of question that costs an afternoon and gets asked again next quarter.
- Reads214 pages of the SOP, and finds the three sections that matter
- Checks1,204 batch records against them, and flags four
- Draftsa reply, with every claim carrying the source it came from
Then it stops.
Nothing is sent, filed or actioned until a named reviewer approves it. That is the architecture, not a setting you can switch off.
- Recordedwho asked
- Recordedwhat the system surfaced
- Recordedwho decided, and when
The system drafts. Your team decides.
Two years later, somebody will ask how that decision was made. The answer is already written down.
Illustrative run. Documents, counts and timings are generic examples.
Enterprise AI & agents
The gate is the architecture.
An agent that can act without review is a liability in any regulated business. Ours stop at a person by design — and write down who asked, what was surfaced, and who decided.
Capabilities
Six things we build.
AI agents & copilots
Agents that work inside your tools, with a human gate before action.
Your team keeps the final click, and the system keeps the record of who made it.
Document intelligence
RAG, knowledge graphs and search across the documents you already have.
Ask the archive a question instead of paying someone to re-read it.
Vision & medical imaging
Fundus, ultrasound, inspection and multimodal models.
A second pair of eyes that never gets to the end of a long shift.
Decision intelligence
Dashboards and forecasting that explain how they got there.
You can defend the number in the meeting, because the workings come with it.
Federated & edge AI
Privacy-preserving training, on-site inference, DPDP-aware by design.
The data never leaves your site, so the legal review is a short conversation.
Our view
The next gain is not a cleverer model.
Most of the last few years went into making one model think harder. We think the bigger win now is several of them working the same problem, talking to each other, and settling it between themselves before anyone shows you an answer.
- DraftOne model writes the first answer.
- CheckAnother holds it against the source record, line by line.
- ChallengeA third argues the opposite case, on purpose.
- ReconcileA fourth settles it, and records what stayed unsettled.
What reaches you is a single result that has already survived all of that — not a confident paragraph from one model, but one answer with the arguing done, the sources attached, and the places they could not agree written down rather than smoothed over.
None of this takes the person out. It raises what the person is handed. A qualified reviewer should be spending their judgement on a hard decision, not refereeing a machine that sounded sure of itself.
MENNIE is the first product we are building this way. Launching soon.
Agents
Four that earn their keep.
01
Document review
Reads long documents and surfaces what a reviewer needs to decide.
02
Regulatory & research
483 drafting and guidance tracking, with sources attached.
03
Data analysis
Plain-English questions, explainable answers.
04
Knowledge search
One question across wikis, drives and tickets.
How a request moves
- Your data & documents
- Praverse agent
- Expert review
- Action & audit trail
Have a workflow in mind?
Describe it in plain language. You get a written plan within two business days.