Professional data your model cannot get from the open web.

• The obvious answer is often wrong. A model that knows food and beverages are a blocked GST credit will confidently refuse a credit the law allows when an employer must run a canteen. A generic labeller marks that correct. A practising professional does not.
• Indian professional reasoning barely exists in a form models learn from. How a Chartered Accountant settles a GST question, or a Company Secretary works out a filing, is not on the open web.
• Public benchmarks age and leak. Once a test set is in the training data, the score stops measuring ability.
• One expert's mistake becomes your ground truth unless a second professional solves the case independently and disagreement is adjudicated.
• Rights are unclear. Scraped or crowd-sourced material rarely comes with a clean licence and a record of who wrote what.

Disagreement is recorded, not hidden. It often marks the cases most worth training on. The quality chain · Evidence and currency.

Indian users ask your model about GST, TDS, payroll and company filings every day, and Indian tax and compliance is a known weak spot: public research on Indian tax law has found that even leading models struggle to apply the law to facts. The gap is not the model's architecture; it is the training material.

General expert-data vendors can recruit anyone. They rarely have a calibrated bench of Indian Chartered Accountants and Company Secretaries, a legal check on every citation against current Indian law, and a record of which answers changed when the law did. That is our whole focus. Missions are sized to our calibrated capacity per domain; we tell you plainly what we can deliver and when.

Businesses will judge your model on professional tasks, not just conversation, and government and enterprise buyers need accuracy they can defend. Language data is increasingly available; deep, correct professional reasoning is not.

• Built in India. The professionals are Indian, the data is produced and stored in India, and the rights sit with an Indian company and are licensed to you cleanly.
• English today; Indian languages planned. Native Indian-language cases certified by bilingual professionals are planned from 2027, starting with Hindi.
• Public programmes. We can align formats and methods with national dataset and evaluation standards as they develop, and welcome research partnerships on evaluating AI in regulated professions. Research.

1. See the sample dataset

   five illustrative GST records and the field-by-field schema. Sample dataset.

2. Request a dataset

   describe topics, use, size and licence preference. We reply within two working days with questions or a draft specification. Request a dataset.

3. Pilot in one domain

   usually GST, checked against your own reviewers, with a small sample in your format before full production.

4. Scale on an agreed cadence

   larger missions or an expert case subscription, with a held-out benchmark to measure what the data did. A typical first mission takes five to seven weeks.

Illustrative A lab fine-tuning an open model for a finance product commissions a Data Mission of reasoning records on GST input tax credit: eligibility, blocked credits and provisos, GSTR-2B conditions and time limits, delivered in the lab's own JSONL schema. The same mission holds out a separate benchmark on the same topics, built from different items. A baseline run before training and a re-run after show the change by capability, and the lab's own internal evaluation set is kept apart from both.

This example is synthetic. We do not promise that data improves every model; improvement is measured per task, model and method. Professional Data.

• Every case is synthetic. No real taxpayer, employee or client data is used.
• Expected answers are set by qualified professionals independently of the vendor of any AI being tested.
• When the law changes, affected records are revised, re-dated and re-versioned. Evidence and currency.
• Private evaluation sets are kept out of every licensed pack. Training and test separation.