Where and how many

Object Detection

Classification tells you what is in an image. Detection tells you where it is and how many there are — which is what most operational questions actually require.

Engagements from $10,000 to $100,000+Serving USA · UAE · UK · Canada · EuropeYou own the code and the IP
The problem

Detection is the easy half; the rules are the hard half

Detecting a forklift is straightforward. Deciding that a forklift entering a marked zone while a person is present constitutes an alert is where the engineering is.

We build the business logic on top of detection: zone definitions, line crossings, dwell thresholds, object association, and temporal rules that prevent a single noisy frame from firing an alarm.

Tracking matters too. Counting requires identity across frames, or the same object gets counted forty times as it moves through the view.

  • Multi-object tracking so counts are not inflated
  • Zone, line-crossing and dwell rules configurable without redeployment
  • Temporal smoothing to suppress single-frame noise
  • Confidence thresholds tuned per class and per camera
  • Evidence clips retained for verification
  • Alerts delivered to your existing systems, not a new dashboard nobody watches
Use cases

Detection applications

Safety compliance

PPE detection and restricted-zone alerting with evidence clips.

Production line monitoring

Component presence, orientation and count verification.

Vehicle and logistics

Yard occupancy, dock activity and vehicle counting.

Retail shelf monitoring

Stock presence, facing compliance and out-of-stock alerts.

Queue management

Live queue length feeding staffing decisions.

Asset tracking

Equipment location and utilisation from fixed cameras.

How we deliver

Ten stages from first call to a system your team trusts

Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.

Discovery

A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.

AI Opportunity Assessment

We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.

Solution Architecture

Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.

Proof of Concept

A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.

MVP

One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.

Production Development

Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.

Integration

Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.

Security Testing

Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.

Deployment

Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.

Monitoring & Optimization

Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.

Security & Governance

Security-conscious architecture, from the first design review

Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.

Data privacy & residency

Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.

Role-based access control

Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.

Authentication & authorization

SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.

Encryption

TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.

API security

Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.

Audit logging

Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.

Data isolation

Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.

Secure prompt handling

System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.

PII protection

Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.

Human approval workflows

High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.

Monitoring & anomaly detection

Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.

Rate limiting & abuse control

Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.

Secure deployment

Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.

On compliance: Ezulix designs compliance-ready architecture aligned to frameworks such as GDPR, HIPAA and SOC 2 control objectives. Certification status for any specific standard should be confirmed directly with our team before contract. [VERIFY: current Ezulix certifications]
FAQ

Questions enterprise buyers ask us first

Can detection run in real time?
Yes. Frame rate depends on model size, resolution and hardware. A GPU-backed server handles many streams concurrently; edge devices handle fewer, and we select model architecture to fit the target hardware rather than the other way round.
Does it need to run in the cloud?
Not necessarily, and often it should not. Edge deployment avoids sending video off site, which matters for bandwidth and privacy. Only events and evidence clips need to leave the premises.
How do you reduce false alarms?
Per-class and per-camera confidence thresholds, temporal rules requiring detection across consecutive frames, zone masking to exclude irrelevant regions, and a feedback loop where operator-dismissed alerts become training data.
Can it work with our existing cameras?
Usually. We assess resolution, frame rate, positioning and lighting during scoping. Sometimes the honest recommendation is to reposition or upgrade a camera — that is often far cheaper than compensating for bad imagery in the model.
Project brief

Talk to an AI solution architect

No junior sales rep, no discovery deck. The person on the call is the person who will design the system.

  • Response within one business day
  • Mutual NDA signed before detailed discussion
  • Written scope, one price, one delivery date
  • You own all source code, models and IP at launch
Email: sales@ezulix.com [VERIFY]

We use these details only to prepare your scope and estimate. Your idea stays yours — mutual NDA before any detailed discussion.

Next step

Bring us the process that is costing you the most.

Book a 45-minute call with a solution architect. You leave with a use-case shortlist, a reference architecture sketch and a realistic build envelope — whether or not you build it with Ezulix.