What we start with
Most company data looks like this.
Spreadsheets nobody trusts, a CRM half filled in, PDFs in a shared drive, a database only one person understands. It is not an AI problem yet. It is the material.
VAI Five is an AI/ML lab in Bengaluru. We turn that material into assistants, models and forecasts that a company of ten or a hundred can actually run. Scroll, and the field on the right will show you how.
Chapter 1 of 6
Ground an assistant in it.
A private-data agent reads your documents and databases and answers with the source shown. It runs on your infrastructure or inside your cloud account, so nothing leaves. Sales teams ask it about contracts; support asks it about tickets; founders ask it what happened last quarter.
Chapter 2 of 6
Make more of it, safely.
Synthetic data keeps the statistics of your real records and none of the identities. Use it to train models when the real set is too small, share it with vendors without a data-sharing agreement, or test systems against edge cases you have not seen yet. We measure the fidelity and the privacy, and show you both.
Chapter 3 of 6
Build the system around it.
Some problems need a model built for them: scoring, matching, classification, routing. We run a two-week feasibility study first, then build to production with monitoring and a handover your engineers can maintain. You own the code and the weights.
Chapter 4 of 6
Tune it until it is right.
A smaller model fine-tuned on your domain is cheaper to run and often more accurate than a large general one. The part most teams skip is the evaluation set, so we build that first: a few hundred real cases with the right answers, run before and after every change.
Chapter 5 of 6
See what comes next.
Demand forecasting, document extraction, visual quality checks. Classic machine learning still wins on tabular and visual problems, and it costs a fraction of a language model to run. We tell you which tool fits before we pick one.
Chapter 6 of 6
Let it run.
The same methods run our own growth. seoclock.in is written, published, promoted and reported by a set of scheduled AI agents we built; its founder reviews the output rather than producing it. We set up the same system for marketing teams.
