Object detection and counting.
Find, count and locate products, vehicles, people or parts in images and video.
Typical use · shelves, yards, warehouses
── Computer vision development services ──
Computer Vision Development.
Detection, inspection, OCR and video analytics trained on your own images, deployed to cameras, edge devices or your cloud, with accuracy you can check.
Free 30-minute scoping call. Written estimate within 5 working days.
Your images · Edge or cloud · Privacy by design · Measured accuracy
Illustrative interface. Values are sample data.
By the numbers
0+.AI systems in production
0.Days to first deploy · median
0.0%.Uptime · managed inference · 12 mo
0.0×.Cost reduction · fine-tuned vs API · median
* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.
── 01 · Computer vision development services ──
Six services, from camera to decision.
Computer vision development is building models that turn images and video into decisions: counts, defects, readings and alerts your systems can act on.
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01
Computer vision consulting.
Which visual tasks are worth automating, what cameras and data they need, and the accuracy to expect.
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02
Computer vision development.
Detection, segmentation, OCR and tracking models trained on your images.
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03
System integration.
Results written to your MES, ERP, WMS or apps, with alerts where people need them.
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04
Edge optimisation.
Models compressed and tuned to run in real time on cameras, gateways and on-prem GPUs.
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05
Data and labelling.
Image collection plans, labelling guidelines and quality checks on every label.
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06
Monitoring and retraining.
Accuracy tracked per camera; new lighting, products or angles trigger retraining.
── 02 · What vision can do ──
Six things vision does reliably today.
Each works best with a clear question, a fixed camera position and a few thousand labelled examples.
Visual inspection.
Spot scratches, dents, missing parts and misalignment on the line, at production speed.
Typical use · manufacturing QAOCR and document reading.
Read printed and handwritten fields from invoices, forms, labels and IDs into clean data.
Typical use · finance, logistics, onboardingVideo analytics.
Detect events in live or recorded video and send an alert with the clip attached.
Typical use · safety, security, operationsSpatial analysis.
Measure footfall, dwell time and occupancy across a floor, with no one identified.
Typical use · retail, venues, facilitiesIdentity verification.
Match a selfie to an ID document with a liveness check, only with the person's consent.
Typical use · onboarding, KYC* Previews are illustrative. Identity verification is built only for consent-based use.
── 03 · Measuring accuracy ──
Accuracy you can check.
Every model ships with a confusion matrix on images it has never seen, so you know which mistakes it makes and how often.
- Test images held back from training and labelled twice
- Accuracy reported per class, per camera and per lighting condition
- Thresholds tuned to the cost of a miss versus a false alarm
- The same test re-run every time the model changes
Scratch · precision / recall91% / 93%
Dent · precision / recall93% / 93%
── 04 · Where it runs ──
Edge, on-premise or cloud.
Where the model runs decides latency, bandwidth, privacy and cost. Most projects use more than one.
| Compared on | Edge device | On-premise GPU | Cloud |
|---|---|---|---|
| Latency | Milliseconds | Tens of milliseconds | Hundreds of milliseconds |
| Bandwidth | Only results leave the site | Local network only | Video uploaded |
| Privacy | Images never leave the device | Images stay on site | Images in your cloud account |
| Scale | One model per device | Many cameras per server | Elastic |
| Best for | Real-time inspection, remote sites | Factories, warehouses, hospitals | Batch review across many sites |
── 05 · Privacy and reliability ──
Seeing only what it should.
Cameras raise fair questions about privacy and bias. These controls answer them before anyone asks.
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01
Privacy by design.
Faces and number plates are blurred at the edge unless the use case needs them, with a documented legal basis.
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02
Processing on the device.
Where possible, images are analysed on the camera and only results are sent on.
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03
Tested across conditions.
Accuracy is checked across lighting, angles and products and, where people are involved, across skin tones.
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04
People in the loop.
Low-confidence results go to a reviewer, and every decision can be traced back to its frame.
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05
Compliance support.
Built to support GDPR and sector rules on video and biometric data, with your counsel signing off.
Illustrative interface. Values are sample data.
— Case studies —
The first cohort is being written.
Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?
Talk to us →
── 06 · Industries ──
Computer vision for your industry.
Every industry has its own objects, cameras and rules. The labelling, testing and monitoring stay the same.
Mobility.
Dispatch, ETA and rider-support agents for ride-hailing and fleet platforms.
ETA models · Demand forecasting · Support agentsLogistics.
Route optimisation, document extraction and exception handling across the shipment lifecycle.
Route optimisation · Document AI · Exception triageFinance.
KYC document extraction, fraud signals and customer assistants that stay inside compliance rules.
KYC extraction · Fraud signals · Compliance copilotsReal estate.
Listing enrichment, valuation models and lead-qualifying agents for brokerages and proptech.
Valuation · Listing enrichment · Lead agentsRetail / Manufacturing.
Demand forecasting, visual inspection and inventory copilots on the shop and factory floor.
Forecasting · Visual inspection · Inventory copilotseCommerce / Consumer goods.
Product search, personalisation and post-purchase support agents that cite the order.
Semantic search · Personalisation · Support agentsTravel.
Itinerary assistants, dynamic pricing and disruption-handling agents for OTAs and operators.
Itinerary agents · Pricing · Disruption handling
— Not listed? —
Let's discuss your industry.
If the data is messy and the stakes are real, the method still applies. Bring the workflow; we'll bring the evaluation set.
Start a project →
── 07 · Development process ──
From camera survey to live model.
Discovery starts on site, with your cameras and real images. The test set is built before the model.
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01
Discovery · week 1
Workflow and data audit.
We map the process, pull a sample of real data, and check what is usable, missing or sensitive.
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02
Discovery · week 2
Evaluation set.
We build the test set from your real examples, so “done” has a number before any product code exists.
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03
Build · sprint 1
Architecture and prototype.
Model choice, retrieval design and integration plan, proven against the eval set.
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04
Build · every Friday
Build in the open.
Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.
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05
Run · go-live
Deploy.
A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.
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06
Run · monthly
Monitor and improve.
Drift alerts, model upgrades run through your evals, and a monthly cost and quality review.
── 08 · Technology stack ──
The vision stack we build on.
Detection and OCR models, optimisation for edge hardware, and serving on the cloud you use.
— Models —
OpenAI
Anthropic
Gemini
Llama
Mistral
Qwen
— Agents & orchestration —
LangChain
Temporal
FastAPI
Python
— Retrieval & data —
pgvector
Pinecone
Elasticsearch
Redis
Databricks
Snowflake
— Training & serving —
PyTorch
Hugging Face
vLLM
NVIDIA TensorRT
ONNX
OpenCV
— MLOps & observability —
MLflow
Weights & Biases
Grafana
Prometheus
OpenTelemetry
— Cloud & infrastructure —
AWS
Google Cloud
Azure
Kubernetes
Terraform
Docker
* Tools we build with. No vendor partnership or endorsement is implied.
── 09 · FAQ ──
Vision questions, answered.
Short answers here. Longer ones on the scoping call.
— Still have a question? —
Message us on WhatsApp. An engineer replies within one working day.
How can computer vision reduce costs?
It automates visual checks people do today: counting, inspecting, reading and watching. Savings come from fewer misses, less rework and staff freed for exceptions, measured against your current error rate.
What does a computer vision project cost, and how long does it take?
Projects start with a fixed-fee discovery sprint that includes a camera and data survey. A first production model usually takes six to twelve weeks, priced per milestone, and every quote is written and fixed for its scope.
How many images do we need?
Often a few thousand labelled images per class to start, fewer when we build on pretrained models. Discovery measures what you have and plans the collection and labelling needed.
How do you make sure the model is accurate?
We test on images held back from training, report accuracy per class and condition in a confusion matrix, and re-run the same test every time the model changes.
Can it run on our existing cameras?
Often, yes, if resolution, angle and lighting suit the task. Discovery includes a camera survey, and we recommend changes only where they make a measurable difference.
Can computer vision integrate with our ERP, MES or apps?
Yes. Results are sent through APIs or events to your ERP, MES, WMS, CRM or apps, and alerts go to the people who need to act.
How do you handle privacy?
We process on the device where possible, blur faces and plates unless the use case needs them, keep images in your environment, and document the legal basis with your counsel.
What happens when conditions change?
We monitor accuracy per camera. New products, lighting or angles trigger review and retraining, so accuracy doesn't quietly decay.
── Start a project ──
Bring the workflow. We'll bring the plan.
Three steps from first message to kickoff. No deck, no commitment until you sign.
- 01Scoping call30 minutes with an engineer, not a salesperson.
- 02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
- 03KickoffSign the scope and the discovery sprint starts.
Prefer to message directly? WhatsApp +91 7096010005
