AI & Intelligent Systems
Cameras and microphones that report what they see and hear
Brihat Infotech builds vision and speech systems for the plant floor, the warehouse and the contact centre. Each starts with your own footage or recordings, not a demo dataset.
What the systems detect
- Visual quality inspection
a camera on the line flags scratches, misprints and missing parts.
- Safety monitoring
helmets, vests and restricted zones, with an alert to the shift supervisor.
- OCR and document vision
printed and handwritten fields read from forms, labels and certificates.
- Speech analytics
call transcription, the customer's intent and a quality score for each call.
- Edge deployment
the model runs on a device beside the camera, so footage need not leave the site.
The physical work that decides accuracy
Software gets blamed for what a bad camera angle causes. We design camera placement and lighting with you, then label a dataset from your own footage with your team's help. You own that dataset after the project, so a later model can reuse it.
What leaves the project with you
- A tuned model for your parts, zones or call types
- The annotated dataset, owned by you
- A deployment on an edge device or your server
- Accuracy reported class by class, for example per defect type
Every detection carries a confidence score. Low-confidence frames go to a person, since a score is an estimate and not a guarantee.
Edge or server
An edge device sits next to the camera and runs the model there. It suits a plant with weak bandwidth, or one that does not want footage to leave the site. A server suits many cameras that share one model and one dashboard. We often use both: the edge device flags an event and the server stores the evidence clip.
Speech in the languages your callers use
Transcription uses Whisper-class models, with Indian-language models where callers speak Hindi or a regional language. From the transcript the system tags the caller's intent and scores the call against your quality checklist, such as greeting, disclosure and closing. Supervisors review the lowest scores rather than listening to random calls.
How a build runs
A feasibility trial on real footage or audio opens the project and tells you what is detectable before the full build. The stack uses PyTorch for training, ONNX and NVIDIA Jetson devices at the edge, RTSP feeds from existing cameras, Whisper-class speech recognition, and models that handle Indian languages. Releases reach a live site every fortnight; see how we engage for the rest.
For a model to run for years, pair it with MLOps and AI infrastructure.
What the trial answers
Before a full build, the feasibility trial runs a rough model over your own footage or calls. It answers 3 questions. Can the defect, the missing helmet or the spoken phrase be told apart from normal? What camera position or microphone setup does it need? And how many labelled examples will the final model take? You see the answers on your own material, with the model's mistakes shown alongside its hits.
Each of the 3 answers feeds the plan. If the trial shows a defect is invisible at line speed, the trial report records it before any build starts, and you can change the camera, the lighting or the scope.
Send a few minutes of footage or call audio from your own site.
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.