Neurabit VMS · On-Premise AI Video

The AI video management system built for plants, not data centres.

Your cameras already see everything. Nobody is watching. Neurabit VMS records every camera, watches all of them continuously, decides what is genuinely out of place, and tells the right person within seconds — on hardware inside your own building.

  • Works with the internet down
  • Footage never leaves your site
  • DPIIT recognised
  • Deployed in 7+ countries

AI Video Management System for Indian Factories

The Arithmetic

Footage gets reviewed after something happens. Never before.

768
hours

of footage a 32-camera site produces every single day

4
streams

is what one supervisor can meaningfully watch at once

20
minutes

before attention measurably degrades

A 32-camera site produces 768 hours of footage every day. One supervisor can meaningfully watch about 4 streams at once, and attention measurably degrades after about 20 minutes. That is why footage at most Indian plants is only reviewed after something goes wrong.

If You Have Tried This Before

Why most AI CCTV analytics fail in factories

Most plants that have tried this have the same story. A vendor installed something, it worked in the demo, and within six weeks nobody opened the alerts. Three reasons, and it is the same three every time.

It cried wolf.

Forty false alerts per camera per day trains a supervisor to ignore the system. Once that trust is gone, no later improvement recovers it.

We target under one false positive per camera per day on critical alerts, and every rule shows you its own false-positive rate.

It needed the internet.

Streaming 32 cameras to a cloud analytics service at recording quality needs roughly 128 Mbps of sustained upload, more than a typical Indian industrial leased line can spare alongside everything else the site needs. When the link drops, monitoring stops.

Everything runs on hardware inside your own building. Full function with the WAN down. Alerts queue and deliver on reconnect.

It could not be changed.

Twelve fixed use cases. Need a thirteenth, or want the loitering threshold moved from five minutes to fifteen? That is a change request and a quotation.

Your team writes and tunes the rules in plain language, and can upload your own ONNX models. No ticket, no waiting on our roadmap.

We built this because we kept hitting all three while deploying computer vision for industrial clients.

In Production

Running live at an electrical substation in Odisha

Neurabit runs production edge video analytics at a live electrical substation operated by a power distribution utility in Odisha. A multi-camera pipeline on NVIDIA Jetson inside the substation detects fire and smoke, PPE violations and restricted-zone entry. No footage leaves the premises.

It is also where we learned what the tuning weeks are for. Substation lighting, reflective equipment and uniform colours all produced false positives out of the box. Fine-tuning on the site's own footage is what brought alerts to a level operators act on, and that process is built into every Neurabit VMS deployment.

Client name withheld at the client's request.

Watch

17 use cases, live from day one

Perimeter & Access

  • Perimeter intrusion and fence climbing during non-working hours
  • Unauthorised entry to restricted areas: server room, cash room, stores, electrical rooms
  • Unauthorised vehicle entry, exit or movement outside approved timings
  • Visitors in unauthorised areas or overstaying their approved duration
  • Staff leaving without completing an exit punch

Workforce Compliance

  • Staff on premises after hours without authorisation
  • Loitering in canteens and break areas beyond designated break timings
  • Gathering in corridors, staircases, parking or wash areas during working hours
  • Prolonged inactivity at workstations
  • Security personnel absent from post or missing patrol rounds

Safety

  • Slips, falls and collapses requiring immediate response
  • Physical altercations and workplace misconduct
  • Fire and smoke, including on holidays and after hours
  • PPE compliance: helmet, vest, gloves, boots, harness

System Integrity

  • Camera tampering, obstruction, defocus or feed loss
  • Loading and unloading outside approved timings
  • Unattended bags, cartons or objects in common areas

Every rule is schedule-aware. The same person in the canteen is fine at 1pm and a flag at 4pm, because the system knows your shifts, break windows, holiday calendar and who is authorised where.

Where It Runs

A plant is the hardest case, not the only one

Anywhere with cameras, shifts and a boundary is the same problem. We lead with manufacturing because dust, glare and contractors in street clothes are the hardest conditions to hold accuracy in — an office lobby is a far easier room to get right.

Manufacturing plants

Dust, shift patterns and contractors in street clothes. PPE and near-miss evidence carry the audit.

PPE detection in detail
  • PPE compliance
  • Slips & falls
  • Fire & smoke
  • Restricted-area entry
  • Crowding & gathering
  • Loading off-hours
  • Prolonged inactivity
  • Camera tampering

Warehouses & logistics

Bay discipline and vehicle timings decide throughput; most losses happen outside approved hours.

  • Loading off-hours
  • Vehicle movement off-hours
  • PPE compliance
  • Slips & falls
  • Unattended objects
  • After-hours presence
  • Camera tampering
  • Restricted-area entry

Corporate offices & IT parks

Access control and visitor discipline matter far more than PPE. Server and records rooms are the sensitive zones.

  • After-hours presence
  • Restricted-area entry
  • Visitor overstay
  • Exit without punch
  • Unattended objects
  • Crowding & gathering
  • Fire & smoke
  • Camera tampering

Hospitals & healthcare

Patient falls and altercations need a response in seconds, and restricted wings need a real boundary.

  • Slips & falls
  • Altercations
  • Restricted-area entry
  • Visitor overstay
  • After-hours presence
  • Fire & smoke
  • Unattended objects
  • Guard off post

Schools & campuses

Perimeter and after-hours presence first, then anything involving a crowd forming where it should not.

  • Perimeter intrusion
  • Restricted-area entry
  • Altercations
  • Crowding & gathering
  • Visitor overstay
  • After-hours presence
  • Fire & smoke
  • Unattended objects

Retail & showrooms

Shrinkage happens after hours and at the stockroom door; crowding and abandoned bags are the floor risks.

  • After-hours presence
  • Restricted-area entry
  • Unattended objects
  • Altercations
  • Loitering past a limit
  • Crowding & gathering
  • Camera tampering
  • Exit without punch

Hotels & hospitality

Back-of-house access, guest-area incidents, and whether the person on the desk is actually on the desk.

  • Restricted-area entry
  • Visitor overstay
  • Guard off post
  • Altercations
  • Unattended objects
  • Fire & smoke
  • After-hours presence
  • Exit without punch

Gated communities & residential

Boundary, gate and guard discipline — the three things residents actually pay the association for.

  • Perimeter intrusion
  • Vehicle movement off-hours
  • Visitor overstay
  • Guard off post
  • Unattended objects
  • Fire & smoke
  • Camera tampering
  • Altercations

All 17 scenarios are available in every deployment. The tags above are the ones that earn their place in each kind of building — what changes between them is the zones you draw, the schedules you enter, and which rules you actually arm.

The Whole Product

Record. Search. Respond. Prove.

A video management system, not just analytics

Analytics without an archive is half a product. When something happens, you need the footage.

Recording that fits the camera

Continuous, motion, event or scheduled recording per camera, with retention set by days or by disk quota, whichever binds first. Storage forecasting warns you before retention drops below policy, not after footage is already gone.

A timeline that tells the truth

Scrub from thirty days down to one second. Synchronised playback across cameras on a single clock. Gaps where a camera was offline are shown explicitly, never silently skipped, because an investigator needs to know footage is missing.

Evidence that holds up

Export any clip with burned-in timestamp and camera ID, a SHA-256 manifest and a chain-of-custody statement naming who exported what and when. Every view and every export is written to an append-only audit log.

An archive that survives a power cut

The segment index is crash-safe and rebuildable. We test this by killing the recorder at fifty random points and verifying the archive is readable every time.

Configure

No change request. No waiting on our roadmap.

This is what most vendors do not offer, and it is the reason we built the product this way.

Your team writes the rules

Pick a scenario, choose cameras and zones, set the schedule, set the threshold in plain language. Not a config file. Not a support ticket.

Your team tunes it

Every rule shows its false-positive rate. Change a threshold and see immediately what the last seven days would have produced at the new setting, before you commit.

Your team can ask why

Every alert carries its reasoning: which rule, which detections, which conditions passed. And the reverse, which matters more: when something should have alerted and did not, the system names the condition that blocked it. Usually a schedule or a zone boundary, not the AI.

Bring your own models

The pipeline accepts standard ONNX models. Upload one, the system builds and optimises it for your hardware, versions it, and lets you roll back. Chain models together visually: detect, crop, classify, count.

Test before you arm

Run any new rule or model against recorded footage and see exactly what it would have caught, before it goes live.

New rule
IFpersondwells inZone 3 · Canteenlonger than8 minutes
ANDtime is16:00 – 22:00onMon – Sat
THENnotifyShift SupervisorviaWhatsApp
False positives, last 7 days0.4 / camera / day

Replayed against the last 7 days of recorded footage before it goes live — 3 matches, 0 false alarms.

Illustrative — this is the rule editor your own team uses.

Edge AI CCTV Analytics

Why on-premise wins

We build on NVIDIA Jetson and on-premise x86 with NVIDIA GPUs. Everything runs at your site.

Internet requirement

CloudContinuous, high bandwidth
NeurabitNone for core operation

Upload, 32 cameras

Cloud~128 Mbps sustained
NeurabitZero

When the link drops

CloudMonitoring stops
NeurabitKeeps running; alerts queue and deliver on reconnect

Where footage lives

CloudVendor's cloud
NeurabitYour premises

Data residency, DPDP

CloudComplicated
NeurabitStraightforward

Cost model

CloudPer camera per month, forever. Nothing is ever yours.
NeurabitOwn the hardware outright, or subscribe with hardware included.

The architectural reason it works

We do not run every model on every frame of every camera. A lightweight detector runs continuously across all channels; heavier models run only on cropped regions, and only when a condition is met. That cascade is what lets a single edge device analyse up to 10 cameras continuously, and what keeps per-camera cost sane.

Scale

One plant or fifty

Multi-site from one console

Site health, alert volume and compliance trends across your estate, with each site still fully autonomous.

Configuration as code

Commission site one properly, then replicate its zones, schedules, rules and templates across the rest in an afternoon instead of a week.

Updates that roll back themselves

Staged rollout, health-checked, automatic rollback on any site that fails.

Integrations

HRMS punch reconciliation, visitor management, gate pass, access control. Badge or facial identity binding, consent-managed under the DPDP Act, with templates that never leave your site.

DPDP Act and CCTV
Deployment

What it actually looks like

Nobody goes live on day one. Be suspicious of anyone who says otherwise.

  1. Install, connect cameras, verify streamsHalf a day
  2. Draw zones, enter shifts and holidays1–2 days
  3. Switch on scenario templatesHalf a day
  4. Recording and alertingDay 3
  5. Tuning against real site conditions2–4 weeks
  6. Alerts your team trusts and acts onWeek 4–6

The gap between day 3 and week 5 decides whether a deployment succeeds. On day 3 the system works but flags shadows as people and cartons as abandoned bags. Weeks one to four are spent marking false positives and tuning until it settles. We stay with you through it. That is not a support add-on, it is how the product is designed to be deployed.

Three things that decide the outcome

01

Camera placement matters more than the software

A camera pointed into a bright doorway, or mounted too high to resolve a person, will underperform regardless of the model. We run a site survey before quoting and will tell you which of your cameras need moving.

02

Someone has to own the alerts

A system nobody is responsible for answering becomes wallpaper in six weeks.

03

We need footage from your site early

Models tune to your dust, your lighting, your uniforms, your contractors in street clothes. Every site differs, and the first two weeks are how the system adapts to yours.

What It Does Not Do

Being clear about the boundary saves everyone time.

It does not read job cards, verify chemical dosing sequences, or check that a pH reading was logged. Those are ERP and checklist problems, not camera problems.

It does not fix bad cameras.

Comparison

How this differs from Milestone, Genetec or Verkada

Those are strong products. Milestone and Genetec are enterprise VMS platforms where AI analytics usually come from separately licensed partners, and Verkada is cloud-managed around its own cameras. We are built for Indian industrial sites.

Full function without internet

Not a degraded offline mode. Recording, detection, rules and local alerting all continue with the WAN down.

Two ways to buy it

Own the edge hardware outright, or take it on a per-camera subscription with the hardware included. Either way it sits on your premises.

Rules your own team changes

Thresholds, schedules, zones and even the AI models themselves — no change request, no quotation.

Priced for an Indian cost base

From ₹800 per use case per camera per month, hardware included. A published rupee price, not a global list price converted at the day's exchange rate.

Pricing

Two ways to buy it

Which one is right for you is a budget question, not a product question — the system is identical either way. Pick the one that matches how your plant spends.

Own it

Hardware from roughly ₹50,000 per device

Buy the edge hardware once, then license each use case per camera.

Best for: Sites with a capex budget and a long horizon.

  • The edge hardware is bought once and is yours outright, an asset on your books rather than a rental.
  • Each edge device handles up to 10 cameras, so the hardware grows with your camera count.
  • Each use case is licensed per camera, on a perpetual or annual licence priced on request.
  • Tuning support through the first weeks on site is part of every deployment.

Add devices, cameras and use cases later as the site grows.

Subscribe

From ₹800 per use case per camera per month

Per use case, per camera, per month. Hardware included.

Best for: Opex-only budgets, leased premises, or a first site ahead of a wider rollout.

  • No capital outlay. The edge hardware is supplied, maintained and replaced by us as part of the subscription.
  • One predictable monthly figure per camera covering hardware, software, updates and support.
  • Scale cameras and use cases up or down as the site changes, instead of re-quoting a hardware purchase.
  • The fastest route to a live site when procurement is the bottleneck.

The hardware still sits on your premises throughout, and so does your footage.

  • Footage is recorded and processed on your premises on either tier. Neither one sends video to us.
  • Discount tiers are available on request. The site survey confirms your camera count, which cameras are usable and how much storage your retention policy needs, and we confirm the final figure after it.
  • No per-camera cloud fee, no bandwidth bill, and no charge for storage you already own.

Example: a 32-camera plant running PPE compliance and fire and smoke detection pays ₹51,200 per month on subscription (32 cameras × 2 use cases × ₹800), with hardware included. To own it instead, the same plant needs four edge devices of up to 10 cameras each, roughly ₹2 lakh of hardware, plus licences for the two use cases on each camera, priced on request.

Technical Specifications

For the person who will evaluate it

Cameras
Any ONVIF-compliant IP camera, RTSP
Compute
NVIDIA Jetson Orin, or x86 with NVIDIA GPU
Analysed channels per device
Up to 10 per edge device; larger sites use several devices
Recorded channels
Higher than analysed; disk-bound rather than compute-bound
Recording
Continuous, motion, event or scheduled, per camera
Retention
By days or disk quota, with forecasting
Deployment
On-premise. Optional cloud console for multi-site
Offline operation
Full function with the WAN down
Custom models
Standard ONNX, uploaded through the interface
Access control
Role-based, per-camera permissions, SSO via OIDC and SAML
Audit
Append-only, hash-chained
Languages
English, Hindi, and regional
Built by a Team That Ships to Plants

Neurabit is a DPIIT-recognised deep-tech company headquartered in Sambalpur, Odisha, with production AI systems deployed across 7+ countries. We build edge AI, computer vision and physical AI systems. This product exists because we kept rebuilding the same pieces for industrial clients and decided to build them properly, once.

AI that works where the internet doesn't.

Start Here

Get a site survey and a sized quote.

We do not quote from a price list. Tell us what your site looks like and we will size the hardware, tell you which cameras are usable as they are, and walk you through what the first six weeks would actually involve.

  • We tell you which of your existing cameras need moving — before you spend anything.
  • Two ways to buy: own the hardware outright, or subscribe per camera with it included.
  • A real engineer replies within one business day, not a sales sequence.
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We use this only to prepare your survey. No newsletter, no list, no reselling.

FAQ

Frequently asked questions

A VMS records and manages footage from your cameras. An AI video management system adds continuous analysis on top: it watches every stream, recognises defined situations, and raises an alert with the evidence attached, instead of waiting for someone to review footage afterwards.

Yes. Everything runs on a device at your site. Recording, detection, rules and local alerting continue with the WAN down. Notifications queue and deliver in order once connectivity returns.

Any ONVIF-compliant IP camera with an RTSP stream, which covers most commercial and industrial CCTV installed in the last decade. We verify each camera during the site survey, because resolution, mounting angle and keyframe settings all affect what is achievable.

Each edge device analyses up to 10 cameras, and entry hardware starts at roughly ₹50,000 per device. Larger sites run several devices, managed together from one console. We size against your actual camera count and use cases rather than quoting a generic number.

Neurabit VMS subscription starts at ₹800 per use case per camera per month, with the edge hardware included. At that rate, a 32-camera plant running PPE compliance and fire and smoke detection pays ₹51,200 per month. To own the hardware instead, edge devices start at roughly ₹50,000 and each handles up to 10 cameras, with each use case licensed per camera on a perpetual or annual licence, priced on request. Discount tiers are available on request, and we confirm the final figure after a site survey.

Almost always false positive rates, not detection accuracy. A system at high accuracy that still fires forty false alerts per camera per day gets ignored within a fortnight. We target under one false positive per camera per day for critical alerts, and the tuning period exists to get there.

No. Video is recorded and processed on-site. Only alert notifications leave, and only to recipients you configure.

Yes. The pipeline accepts standard ONNX models with a manifest describing inputs and outputs. Upload it, and the system builds and optimises it for your hardware.

Those are strong products. Milestone XProtect and Genetec Security Center are enterprise VMS platforms where AI analytics typically come from separately licensed partners, and Verkada is cloud-managed around its own cameras. Neurabit VMS includes 17 AI scenarios in the core product, runs fully without internet, lets your own team change rules and upload models, and publishes a rupee price per camera.

The right choice depends on your site, but for Indian factories the deciding factors are usually whether it works without internet, how many false alerts it raises per camera per day, whether your own team can change the rules, and whether it runs on your existing cameras. Neurabit VMS is built around all four: it runs on-premise, targets under one false positive per camera per day, is configured by your own EHS team, and works with any ONVIF IP camera.

Yes. Neurabit VMS runs entirely on edge hardware at your site. Recording, AI detection, rules and local alerting all continue with the internet down, and it can also run alongside an existing Milestone or Genetec system on the same cameras.

The Digital Personal Data Protection Act, 2023 does not ban face recognition, but face data identifies people directly, so it needs a documented purpose, tight access control and strong security safeguards. Most obligations under the DPDP Rules apply from 13 May 2027. In Neurabit VMS, facial identity is optional, face templates are consent-managed and never leave your site, and every view and export is written to an append-only audit log. Confirm your specific setup with counsel.

Helmets and vests are detected reliably in most industrial conditions once the system is tuned on the site's own footage. Gloves are the hardest item because hands are small in the frame. Dust, glare and poor camera angles mainly raise false positives, which is why every deployment includes 2 to 4 weeks of on-site tuning toward under one false positive per camera per day.

For most Indian plants, edge is cheaper because cloud analytics needs sustained upload bandwidth: about 128 Mbps for 32 cameras at recording quality, on top of a per-camera cloud fee. Neurabit VMS needs zero upload bandwidth. A 32-camera site running two scenarios costs ₹51,200 per month on subscription at ₹800 per use case per camera, or about ₹18.4 lakh over three years, with hardware included.

Neurabit runs production edge video analytics at a live electrical substation operated by a power distribution utility in Odisha, covering fire and smoke, PPE compliance and restricted-zone entry on NVIDIA Jetson hardware. Neurabit's wider AI systems are deployed across 7+ countries.

All of India. We are headquartered in Sambalpur, Odisha, and regularly work across Odisha, Jharkhand, Gujarat, Maharashtra and Tamil Nadu. Site surveys, commissioning and tuning visits are done on-site.

Still have a question we have not answered? Ask it in the survey form and an engineer will answer it directly.

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Last updated 24 September 2026. Technical content reviewed by Abhishek Goel, Chief Technology Officer, Neurabit Solution.

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