Evidence at every step. No big-bang bets.
AI projects fail when they skip straight from idea to build. Our process puts a working result on your real data in front of you early — and gives you a clear decision point before each larger investment.
Five stages, each with a defined output.
Discover1–2 weeks
We map the decision you want to improve, audit the data available, and agree a baseline and a target metric. You get a written feasibility assessment — including an honest 'not yet' if the data isn't ready.
Prototype3–6 weeks
A working model on your real data, evaluated against the baseline. This is the point to stop or continue, with evidence rather than a slide deck.
Build6–12 weeks
Production-grade pipelines, APIs and interfaces, integrated with your systems, tested and security-reviewed.
Deploy1–3 weeks
Staged rollout on your cloud or on-premise infrastructure, with monitoring, alerting and a rollback plan in place before go-live.
Operate & improveOngoing
We monitor drift and accuracy, retrain on new data, and hand over to your team with documentation and training — or keep running it for you.
What stays constant across every project.
Baseline first
We measure the current process before building anything, so improvement is a number, not an opinion.
Evaluation as code
Test sets and evaluation scripts live in the repository and run on every change.
Security by default
Least-privilege access, secrets management, encrypted storage and audit logging from the first commit.
Explainability
Feature attributions, confidence scores and source citations so users understand — and can challenge — outputs.
Responsible AI
Bias checks across relevant groups, documented limitations and human review for high-impact decisions.
Knowledge transfer
Pairing, runbooks and architecture docs, so your team can operate and extend the system.
Choose how we work together.
Pilot
A defined problem, a fixed timeline and price, and a go / no-go decision at the end. The lowest-risk way to start.
- 4–8 weeks
- Fixed price
- Working prototype + report
Dedicated team
A cross-functional pod — ML engineers, data engineers and a lead — working as an extension of your team.
- Monthly engagement
- Scales up or down
- Your sprints, your tools
Advisory
Senior guidance for teams building AI in-house: architecture reviews, model audits, vendor selection and roadmaps.
- Retainer or per-review
- Architecture & audits
- Hiring support
Practical questions, straight answers.
How much data do we need to get started?
It depends on the problem. Some forecasting and classification tasks work with a few thousand historical records; LLM-based solutions over your documents often need no labelled data at all. The Discover phase answers this concretely for your case.
Can you work with sensitive or regulated data?
Yes. We can work entirely inside your cloud account or on-premise, use open-weight models that never send data to third-party APIs, and follow your security and compliance processes. NDAs are standard.
Do you work with companies outside India?
Yes. We're based in India and work with clients in India and internationally, with overlapping hours for US, UK, European, Middle-East and APAC time zones.
Which cloud and LLM providers do you use?
Whatever fits your constraints — AWS, Azure, GCP or on-premise, and commercial or open-weight models. We're vendor-neutral and will recommend based on accuracy, cost and data-residency needs.
Who owns the code and models you build?
You do. Intellectual property for project deliverables transfers to you on payment, and we hand over source code, model weights and documentation.
What does a first engagement typically cost?
Most clients start with a fixed-price pilot. Pricing depends on scope and data complexity — tell us about your problem and we'll send a written proposal within a few working days.
Have a problem AI might solve?
Tell us what you're trying to improve. We'll reply within one working day with honest next steps — even if that means AI isn't the answer yet.