Knowledge assistantInternal Knowledge Assistant (RAG)
A private assistant that answers from your own documents, with citations, without handing them to a third-party AI vendor.
Ingest → Retrieve → Govern
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Object Detection & Tracking
Counting, monitoring and safety checks from camera feeds, running on the edge rather than streaming everything to a cloud.
- Time to value
- Proof of value on recorded footage in 2–3 weeks.
- Deployment
- Can run fully on your infrastructure
- Category
- Data, Knowledge & Documents
The manual version of thisInvictt AI layer
Assess
Train
Deploy to the edge
Alert and log
Once it's AI-assisted01/The problem
You already have cameras. What you do not have is numbers — how many units on that shelf, how many vehicles through that gate, whether anybody entered the exclusion zone without a hard hat. Someone reviews footage after the fact, if at all.
02/See it in action
What you'd actually see
A demonstration of the mechanism — not a live system reading your data.
Inspection report
- Equipment
- PPE — compliant
- Work zone
Report drafted→ reviewer queue
03/How it works
The operating model
What enters, what the system does with it, and what reaches you.
Check
Assess
We check the feeds you have — placement, resolution, lighting, frame rate — and tell you honestly which questions they can and cannot answer before anyone buys hardware.
System
Train
Detection and tracking models tuned on footage from your site, because a model trained on stock imagery performs differently under your lighting.
Integration
Deploy to the edge
Inference runs on-site on a Jetson or a small GPU box. Video never leaves the premises; only the counts and events do.
Outcome
Alert and log
Threshold alerts to the channels your team already watches, and a queryable event log for everything that did not need an alert.
Read the detail behind each stage
01
Assess
We check the feeds you have — placement, resolution, lighting, frame rate — and tell you honestly which questions they can and cannot answer before anyone buys hardware.
02
Train
Detection and tracking models tuned on footage from your site, because a model trained on stock imagery performs differently under your lighting.
03
Deploy to the edge
Inference runs on-site on a Jetson or a small GPU box. Video never leaves the premises; only the counts and events do.
04
Alert and log
Threshold alerts to the channels your team already watches, and a queryable event log for everything that did not need an alert.
04/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Camera and feed suitability assessment before any purchase
Detection and tracking models tuned on your own footage
On-site edge deployment — video stays on the premises
Zone, line-crossing and dwell-time rules
Threshold alerting into Slack, Teams or email
Queryable event log and counting dashboard
Built with
- YOLO
- ByteTrack
- TensorRT
- NVIDIA Jetson
- RTSP
Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.
05/Also in this category
Data, Knowledge & Documents
Unstructured records turned searchable, verifiable and actionable.
IngestIndexAnswer
Knowledge assistant
Inspection reportingAI Inspection & Defect Reporting
Photograph a site; get a structured, drafted defect report before you are back in the van.
Capture → Classify → Review and issue
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Classification & segmentationImage Classification & Segmentation
Custom vision pipelines for the visual data that only exists in your industry.
Define the classes → Label efficiently → Deploy
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Is this the problem you have?
Tell us what it looks like at your end. We will say honestly whether this is the right fit — including when it is not.