We put AI where it changes a number in your business — response time, qualification rate, hours of manual work — and we can point at production systems where it already does.
The gap in AI work right now is not model access. Everyone has that. The gap is between a convincing demo and a system that survives contact with real users, messy data and the person who has to run it on a Tuesday afternoon.
We build the second kind. Our AI work ships inside products that already have users, authentication, billing and a support burden — which changes almost every design decision compared to building a prototype.
What we build
Generative AI features
Drafting, summarising, extraction and classification wired into an existing workflow, with the prompt layer version-controlled and the outputs reviewable by a human before anything leaves the building.
AI agents that take actions
Agents with real tool access — reading your database, updating a CRM record, booking a slot — built with explicit guardrails on what they may do unsupervised and what needs sign-off.
AI automation of manual work
The unglamorous, highest-ROI category: the four-hour daily task someone does by hand. We measure it before and after, and we tell you when automation will not pay for itself.
Voice AI
Inbound and outbound calling in English and Hindi, with handoff to a human when the caller wants one. Running in production today on our own platform.
How we work
- 1
Find the number
One week. We identify which metric AI is supposed to move and whether it is measurable today. If it is not, that is the first deliverable.
- 2
Narrow prototype
Two to three weeks against your real data, not sample data. This is where most AI ideas either prove out or get honestly killed — killing one here is a cheap outcome, not a failure.
- 3
Production hardening
Evaluation set, fallback behaviour when the model is wrong, cost ceilings, logging, and a human review path. This phase is usually longer than the prototype and is the part demos skip.
- 4
Run and tune
Model costs and capabilities move fast. We keep the evaluation set green as the underlying models change underneath you.
How we price it
AI work is priced per phase, not as one lump sum, because the honest answer after the prototype is sometimes "this does not justify the spend". You get that answer with the prototype invoice, not after a six-month build.
Software of ours you can open right now
These are live production sites running on our platform, not mockups. Open any of them, then ask us to walk you through the admin side on a call.
- Bawana Industrial Tools & Hardware
- Delhi Corporate Chambers
- Delhi Skyline Realty
- Dot2Dotz Industrial Marketplace
- Foodlet
- Freelance HUB
- JustLaunch
- Propnal Real Estate
Questions we get asked
Will you tell me if AI is the wrong tool for my problem?
Yes, and we do it regularly. A large share of the requests we get are better solved with a database query, a form, or a rules engine that costs a fraction as much and never hallucinates. We would rather do the smaller correct project.
Whose AI models do you use?
Whichever fits the task, cost ceiling and data-residency constraint. We build the integration layer so the underlying model can be swapped without rewriting your application — that has already saved clients real money as pricing has shifted.
What happens to our data?
We scope this explicitly before any code is written: what is sent to a third-party model, what stays on your infrastructure, and what is retained. If the answer needs to be "nothing leaves our servers", that constrains the model choice and we will say so upfront.
How do you stop the AI from being confidently wrong?
Evaluation sets that run on every change, confidence thresholds that route uncertain cases to a human, and — where the cost of a wrong answer is high — no unsupervised action at all. Any vendor who tells you hallucination is a solved problem is selling something.
Can you work with our existing systems?
That is the normal case. Most of our AI work attaches to software the client already runs. We integrate at the API or database layer rather than asking you to migrate.
Talk to us
Tell us what you are trying to build. The first call is a scoping conversation, not a pitch — and if you do not need us, we will say so.