SOLUTIONS

Different problems, one shape

Floor compliance, back-office workflow and a company rebuilding around its runway look like different products. Underneath they are the same query: join what was recorded to the rule that governs it, on one timeline, with the evidence attached.

// the same platform, pointed at the problem in front of you

WHERE TEAMS START
an operation, a limit, a document and a clock

SOP compliance

This is the compliance problem where video provably is not enough.

a trigger, a join, a decision and a person

Complex workflow optimization

The choice today is a brittle script or an agent nobody can review.

a team, a runway, a product and a deadline

Miriel Refound

Burn is the constraint, and the usual answers make the product worse.

WHY THEY ARE THE SAME PRODUCT

The capability worth pointing at is not recognizing something in a frame. It is stitching facts across time.

Ask where an object was left and the answer needs four separate observations joined: the object was seen; it was last with a named person; it then sat alone long enough to count as put down; and the nearest fixed object becomes the spoken location.

“Where was this left, by whom, and when” is the same query as “who last handled that spool of 316L” — and as “was anyone at that station without a hood down”, and as “how long did that assembly sit waiting for fit-up”. Material traceability, station attribution and dwell time are one shape underneath.

That is why this is one platform rather than several. Build the join once, point it at an industry.

What a frame becomesA camera frame and three other sources fan out into detections, text, embeddings, transcripts and sensor readings. All of them land on one shared timeline, which a single question sweeps across to return an answer with the evidence frames attached.ONE SAMPLED FRAMEperson 0.94torch 0.88cam-3 · 14:02:11 · 1.82 mAND EVERYTHING ELSERTSP · depthmicrophonesPLC historianPDF corpusOpen-vocabulary detection: theclasses are text prompts, so a newobject needs no retraining and nolabelled data.detections · trackstext / OCRembeddingstranscript · voicessensor readingsWritten at boundaries — whensomething appears, changes, movesor leaves — not once per frame.≈20× less storageONE TIMELINEThe store is the memory. Answerssurvive a restart; an audit trailis queryable a year later.“who had it last?”evidenceframes
Every source lands on one timeline: detections, text, embeddings, transcripts and sensor readings. One question sweeps across all of it and comes back with the evidence frames attached.

GET STARTED

Pick the question, not the product.

Tell us one thing you cannot answer today and we will tell you what it takes — including when the answer is that you do not need us.

// no spam, just a beta waitlist