AI workflows and apps, run where your data lives.
Busywork in, finished work out.
We build AI agents and AI-native apps for companies of 20 to 200 people. We start with the one workflow that eats your team’s week: we map it, measure what it costs, build an agent that does it, and keep it running.
- 01Map
- 02Measure
- 03Build
- 04Run
What we build
One workflow, start to finish
We take the one job that eats your team’s week, map it, measure what it costs and build an agent that does it.
Then we keep it running, on your cloud or on your own hardware. The code, the prompts and your data stay yours.
Bring us the workflow. We’ll build the agent.
Book a callThe same four terms apply to every project, from a two-week workflow diagnostic to a full app build.
Fixed before we start. We don’t bill by the hour.
Our day overlaps the UK, EU, Gulf and Australia, and US mornings.
The code, the prompts and your data stay yours. NDA before the first call.
Two founders, both engineers, on every project. Based in Lucknow.
From the blog
- Local LLM vs API cost: a 4B model on a 2019 Core i9
We ran Qwen3-4B on a 2019 Core i9 with no graphics card to sort 20 support emails: 5.8 seconds each, 17 of 20 categories right, a few cents per 1,000.
- Why AI automations break after launch: model retirements
Model retirements and removed API parameters break AI workflows. A dated retirement table, what breaks first and a migration runbook you can copy.
- Is it safe to put confidential documents into ChatGPT?
What ChatGPT, Claude and Copilot keep, train on and for how long on each plan, linked to each vendor's policy, plus the admin settings to change.