An AI-first company building the whole stack: generative and agentic AI, computer vision and video understanding, and the document intelligence underneath. We turn unstructured reality into structured facts, then put agents on top that finish the task - on factory floors, in hospital corridors and open fields, not just in demos.
line-2 crossed 3× baseline inside twelve minutes.mes.batch.lookup(line=2, window="09:10-09:34")vision.query(class="surface_scratch", window="09:10-09:34")WO-2291 open · lot quarantined · supplier claim drafted.Everything we ship does the same thing in a different medium: take something unstructured, give it a schema, and hand it to something that can act. Pixels and paper are the same problem.
Perception is step one. Our agents match, reconcile, chase exceptions, write the email and post to the ledger - inside a permission contract your risk team can read.
Assistants grounded in your own operation - footage, telemetry, tickets, archives. They answer with the clip and the row they used, or they say they do not know.
Detection, recognition, segmentation, counting, measurement and tracking - trained on your parts, your produce, your equipment, your lighting.
Frames are cheap; events are what matter. We turn continuous footage into a queryable timeline of actions, states and exceptions.
Extraction that survives real paper - and understands it. Smudged invoices, 400-page bundles, handwritten margin notes that contradict the line item.
Once the pipeline has been producing structured data for a while, it becomes training data. Forecasting, scoring and anomaly models built on your own record.
Handing you a JSON file or a bounding box is not the deliverable. The last two stages are where work actually leaves someone's desk.
Email, SFTP, scanners, RTSP cameras, drones, PACS, an API, or a photo taken on a loading dock.
OCR and vision models tuned for the bad cases - poor light, motion blur, skew, stamps, handwriting, occlusion.
Layout- and scene-aware models bind every value to its label, its table, its frame - and to your schema.
Cross-checks against your systems of record. Anything below threshold routes to a reviewer with the evidence open.
An agent posts the entry, opens the work order, halts the line, books the follow-up, closes the ticket.
A conveyor, a ward and a forty-hectare block have more in common than they look: continuous signal, scarce expert attention, and a decision that has to happen now.
Surface defects, weld porosity, misassembly, missing fasteners, label and batch-code verification - caught at line speed on an edge box beside the conveyor, with the reject gate wired to the same signal. Video understanding adds the layer above: cycle times, operator ergonomics, changeover duration, and the ten seconds before every unplanned stop.
Referral letters, discharge summaries, insurance pre-auth packs and handwritten ward notes become structured clinical data. On the imaging side, detection and segmentation models act as a second reader that never gets tired, always flags rather than decides, and always shows the region it is talking about.
Drone, tractor-mounted and fixed-camera imagery turned into per-plant records: stand counts, canopy cover, disease and pest signatures, weed maps for spot spraying, ripeness grading at the pack house. Every observation carries a location, a date and an image crop, so the agronomist checks the evidence rather than the claim.
Loan files, KYC packs, claims bundles, land records and legacy municipal archives - split, classified, named, extracted and cross-checked, with a reviewer queue for everything the model is not sure about. The archive stops being a warehouse and becomes something you can ask questions of.
We deploy inside your VPC, your datacentre or an edge box in the plant. Nothing is used to train a shared model. Every automated action is attributable, reversible and logged - and anything the system is unsure about becomes a human's queue item, not a silent guess.
# where the model lives is your call deployment: mode: on_prem # on_prem | vpc | edge | air_gapped egress: none gpu: 2 × L4 # or Jetson Orin at the line privacy: redact: [pii, phi, account_no] retain_raw: false train_on_customer_data: never humans: review_below: 0.92 approve_writes_over: 100000 queue: ops.exceptions audit: log: immutable fields: [input_hash, model_ver, output, actor, ts] retention: 7y
Ten scanned documents nobody wants to open, or an hour of footage from your worst-lit camera. We come back in two weeks with a working prototype on your own data and an honest read on what it will and will not do.