2026AI engineering, proven in production
We build AI systems.
Then we run them.
Seven products of our own are live right now — voice agents, retrieval, generation and tool use, running under real traffic for real customers.
Seven products of our own are live right now — voice agents, retrieval, generation and tool use, running under real traffic for real customers.
{ what we ship }
Not case studies from a client engagement — software we designed, shipped and now operate, including the on-call rota and the inference bill.
AI that answers the phone and the chat window
Speech-to-speech voice agents that handle inbound calls, plus an embeddable chat widget with ticketing — both grounded in your knowledge base and wired into your live systems.
AI-Powered 3D Creation & Creator Marketplace
AI-Powered Spreadsheet & Large-Scale Data Analysis
Your brand on autopilot, everywhere customers search
Tracks how your brand shows up inside ChatGPT, Gemini, Claude, Perplexity and Grok alongside Google, Maps and social — then writes and publishes the content needed to close the gaps.
Real-Time Indian Trade Data API Platform
Save, organize & share AI prompts
Business email that reaches the inbox
{ what we do for clients }
The AI work, and the engineering around it that decides whether AI survives contact with production. Every one of these is a service we deliver end to end.
Most AI projects die in the gap between a promising demo and something the business can rely on. The sequence below exists to close that gap early rather than discover it at launch.
We start with what is not working and what better would measure. Sometimes the answer is a query and a cron job rather than a model — we will say so before you spend on inference.
One complete path, end to end, against your real data with a measured baseline. Integration risk surfaces in week two instead of month five.
Evaluation suites in CI, provider fallbacks, cost ceilings and observability. The unglamorous layer that decides whether an AI feature survives real traffic.
We operate it alongside you, or hand it to your team with the practices to maintain it — mainstream stacks, documented decisions and a pairing period.
{ selected work }
Plenty of projects look good in a screenshot. These are the ones where the client measured the difference afterwards.
