Computer Vision & Voice AI Solutions

Image & Video Classification / Object Detection

Off-the-shelf vision APIs are trained on generic objects, they weren't built to recognize your specific SKUs, equipment, vehicles, or scene layout, and that gap shows up as missed counts and misclassifications the moment you put them into production. This service builds and fine-tunes a detection or classification model against your actual footage and images, so it reliably identifies, counts, and tracks the exact things your business needs to watch. It's for teams that need automated visibility into inventory levels, foot traffic, equipment on a yard, or objects moving through a process, at a scale manual review can't sustain. The output plugs into the systems you already use (a dashboard, an alert, an API call) rather than sitting as a standalone demo.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

1
Diagnose: collect sample images and video from your real environment and pin down exactly what needs to be detected, counted, or tracked, and how accurate it has to be.
2
Pilot: label a starter dataset and train a baseline model against one camera feed or image set, then run it against real footage side by side with a manual count.
3
Review: walk through precision and recall together (what it catches, what it misses, what it gets wrong) and decide if it's worth hardening.
4
Build: expand the training data, integrate the model into a live pipeline with an API or dashboard, and set up drift monitoring as lighting, angles, or objects change.

What You Get

Deliverables from this engagement

A trained detection or classification model tuned to your specific objects and environment
An API or integration endpoint for real-time or batch inference
A precision/recall accuracy report benchmarked against a labeled test set
A monitoring dashboard tracking detection volume and confidence over time
Documentation for retraining the model as new object types appear

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Data Collection

Sample Images and Video Clips Collected
Manual Labeling of Target Objects
Small Training Dataset

Model Build

Fine Tuned Pretrained Detection Model
Local Inference Script

Validation

Accuracy Checked Against Labeled Set
Meets Accuracy Bar
Add More Labeled Examples
Model Approved for Pilot

Output

Detections Written to CSV
Simple Count Dashboard

Cloud-Native / Managed Services Architecture

Data Ingestion

Camera or Upload Source
Cloud Object Storage for Frames

Managed Training

Managed Labeling Service
Cloud ML Training Pipeline
Managed Model Registry

Inference Layer

Managed Inference Endpoint
Auto Scaling Detection Service

Delivery Layer

Detection Results Database
Managed Analytics Dashboard
Alert Service for Threshold Breach
Notification to Operations Team

Event-Driven & Automation-First Architecture

Triggers

New Frame Captured
Frame Queued for Inference
Scheduled Batch Video Upload

Automation Layer

Inference Worker Pool
Object Detected Event
Object Count Threshold Crossed
Auto Alert Triggered
Result Logged Silently

Action Layer

Webhook to Inventory or Ops System
Automated Restock or Response Workflow

Feedback Loop

Aggregated into Rolling Dashboard
Model Drift Monitor Checks Accuracy
Drift Detected
Retraining Job Queued

Enterprise-Grade & Compliance-Ready Architecture

Governance

Data Usage and Retention Policy
Model Validation Standard
Access Control Policy

Secure Pipeline

Access Controlled Footage Repository
Encrypted Model Artifact Store
Role Based Inference Access

Audited Inference

Versioned Detection Model
Inference Results with Confidence Logged
Periodic Accuracy Audit
Model Passes Audit
Model Rolled Back to Previous Version
Model Certified for Production

Compliance Reporting

Redundant Backup of Detection Logs
Audit Trail Export
Compliance Sign Off Report

AI-Augmented, Human-in-the-Loop Architecture

Data In

Live Camera Feed or Uploaded Footage
Historical Labeled Dataset

AI Detection

AI Detection and Tracking Model
AI Object Counts and Classifications
Confidence Below Threshold

Human in the Loop

Flagged Frame Sent to Reviewer
Human Reviewer Confirms or Corrects
Correction Fed Back to Model
Auto Accepted Result

Output

Result Published to Dashboard
Alert Sent Only After Human Confirmed High Stakes Case

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

Inventory Management System
Detection and Tracking Core
Security or Facility System
Business Intelligence Platform

Detection Core

Object Count and Location Engine
Business Rule Mapping Layer

Sync Layer

Inventory System Stock Sync
Security System Alert Sync
BI Platform Data Feed Sync

Governance Reporting

Unified Operations Dashboard
Cross System Exception Report

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Image & Video Classification / Object Detection

Most engagements like this start as a $500–$2,500 pilot, see full pricing.