Early preview · The full platform is on its way
Payments intelligence for agentic commerce
An India-hosted LLM, customised for commerce. Grounded in the protocols, not guessing at them.
Ask anything. Payments and commerce is where it is strongest.
No signup. Answers come from the documents below, running on our own hardware in Mumbai.
No approval and no waiting. Your key appears on this page in seconds and works immediately against an OpenAI-compatible endpoint, so an existing OpenAI client needs two lines changed.
Full reference, examples and limits on the API docs.
Ask for a product and it searches a real catalogue, quotes the fetched price, and works the EMI out in the backend. Ask it to spend beyond its mandate and it declines, showing which check stopped it.
Give it a product and a card and it returns the bank offer, cashback or no cost EMI worth most, with the saving and the effective price computed outside the model. Preview offer rates are illustrative and every answer says so.
Score a UPI Autopay book for which debits are likely to fail, why, and when to present them again. Scoring runs in code; the history it runs over is sample data in the preview.
API, webhook and error-code questions answered from the documentation, with sources. Onboarding questions answered in plain language rather than as a rejection code.
Same model, same infrastructure. Different catalogs, different mandate scopes.
Grounded in around 1,200 documents across P3P, Grantex, ACP, UCP, AP2, x402 and a reference set on the Indian payment rails, with commerce tools and payment policy built around it. Duplicates and boilerplate are stripped before indexing. Hybrid retrieval, regression tested. Answers cite their source, and say when a source is unverified.
The LLM runs in Mumbai on our own hardware. No external API call. No data leaving the country.
Mandate scope and spend limits are enforced in code, outside the model. The model proposes, the backend decides. It will decline a purchase that exceeds its limit.