Production AI — not demoware.
We build AI features that survive real users: grounded answers, measurable accuracy, predictable cost, and a plan for what happens when the model is wrong.
Four shapes of AI work we do over and over.
AI agents & copilots
In-product assistants that use your tools: call APIs, fill forms, navigate UIs, and hand off to humans when uncertain.
RAG & knowledge search
Retrieval over your docs, tickets, contracts, or product catalog — with citations, permission checks, and freshness guarantees.
Classification & extraction
Pull structured data out of PDFs, emails, and call transcripts. Tag, route, and summarize incoming volume at scale.
Automation workflows
Long-running workflows that combine LLMs, APIs, and human review. Queue-backed, observable, retryable.
Seeing systems, at scale.
Years of applied computer vision and OCR work across identity, logistics, traffic, and compliance. We bring classical CV and modern transformer-based models — whichever fits the latency, accuracy, and cost budget.
OCR & HTR
Printed and handwritten text recognition from scans, IDs, invoices, cheques, and forms. Structured output with confidence scores.
Image & video analysis
Frame-by-frame processing, event detection, scene segmentation, and motion tracking for surveillance, sports, and content moderation.
Object & human classification
Detect, count, and classify people, vehicles, defects, safety gear. Fine-tuned to your footage and edge-deployable when needed.
Traffic & safety
Helmet detection, number-plate recognition, traffic-violation pipelines. Alert streams integrated with your ops dashboards.
We start with the eval, not the demo.
Before we ship, we know how to tell if the system is working. That changes everything about how fast you can iterate safely.
Grounding
Collect ground-truth data and agree on the success bar. No bar = no project.
Baseline
Pick the smallest model and simplest prompt that passes the bar. Avoid spending before we know what works.
Ship
Put it in front of real users behind a flag. Add observability, logs, and human-in-the-loop controls.
Optimize
Tune for latency, cost, and edge cases using real traffic. Re-run evals on every prompt and model change.