An India-built product is not automatically an Indian foundation model.
BrokenGPT is an India-built chat and API platform. Its public service routes across a private mixture of open-source models. The product does not claim that the underlying foundation models were trained in India or that every Indian language is equally capable.
Start with the same server-side contract in Python or Node.js.
Use https://brokengpt.com/v1 as the client base URL, keep the key in a secret manager, and send broken-one as the stable public model alias. The focused compatibility surface can reduce integration work for existing chat-completions clients.
- Prototype with a non-streamed text request.
- Add streaming after the basic response path is reliable.
- Use scoped keys for each environment and service.
- Set budgets and review token usage before opening the feature to customers.
Test Indic and Hinglish behavior with local evidence.
- Include native scripts, romanized text, code-switching, spelling variation, and local named entities.
- Score meaning preservation separately from fluency and tone.
- Test formal documents, customer support, code explanations, and the exact domain vocabulary you need.
- Record language, script, prompt, serving date, parameters, and human-review criteria.
- Use reviewers who understand the language and cultural context rather than relying only on automatic similarity scores.
Plan for latency, cost, privacy, and support as independent constraints.
A successful proof of concept is not yet a production service. Measure p50 and p95 latency from your users' regions, inspect rate-limit behavior, verify the governing privacy terms, and design a fallback when inference or accounting is temporarily unavailable.
- Do not promise data residency unless the deployed contract explicitly provides it.
- Keep request IDs for debugging without logging secrets.
- Use application-level validation and human review for high-impact decisions.
- Publish a user-visible failure state rather than a placeholder model answer.