Their world

Groww carries more retail investors than any broker in India, so every new feature is a reliability decision first.

At 12.6 million active clients and a quarter of NSE volume, "making investing simple" has a hard engineering corollary: the platform can never be down during market hours. That constraint already shaped Groww Lite. Now the roadmap is adding ML and LLMs — and they have to arrive under the same discipline.

Their pressure

Scale, a reliability floor, and an AI roadmap — at once.

The bridge

Make an AI feature one more tested service, not a new failure mode.

Meridian AI embeds with your team and ships production-grade LLM and agent systems — on your existing stack — in six weeks, not six quarters. The point for Groww is where the AI lives: retrieval and function-calling agents wired into your current services, with evals and guardrails running in CI, so a new feature ships behind the same gate as everything else and degrades safely alongside Groww Lite.

ci · llm-feature.yml

# every AI change enters the same pipeline
build        -> ok
eval-suite   -> pass  # accuracy + refusal + latency budgets
guardrails   -> pass  # input/output constraints on your data
human-review -> required # sign-off before regulated paths
deploy       -> canary # fallback lane: groww-lite

One proof

We ship on your existing stack — retrieval over your own data, function-calling agents wired into your current services, and evals plus guardrails in CI. No model training, no rip-and-replace, no new infrastructure to run.

One working session

Take one AI feature from idea to a CI eval gate.

Twenty minutes to pick one candidate, define its eval suite and guardrails, and decide where human review sits — before any code is written.

See the 20-minute teardown for Groww