Multilingual LLM output is harder than it looks. Some notes from building Permabullish.

I built an AI agentic pipeline that generates multilingual equity research reports in ~60 seconds. Sharing some notes on what made it hard.

The project is called Permabullish. It takes a stock ticker, pulls data, runs it through a multi-step reasoning pipeline, and outputs a structured research report in Hindi, Gujarati, Kannada, or English. Niche use case, but the engineering problems turned out to be interesting.

The LLM call itself was the easy part. Making the output consistently structured and professional rather than fluffy and generic took most of the work.

Chained agents with scoped responsibilities. One agent for data retrieval, one for thesis generation, one for formatting. Narrow scopes reduced hallucination drift significantly.

Output schema enforcement. Defining a strict JSON schema and validating every section before rendering helped a lot. Failures trigger a retry with a tighter constraint prompt.

Language output is not just translation. Running English financial jargon through a translation layer produces garbage. Each language needs its own prompt template with natively different terminology, sentence structure, and tone.

Latency vs quality tradeoff. Parallel agent execution gets to ~60s, but step sequencing matters more than expected. Some agents genuinely need the previous output to reason correctly.

Eval is still unsolved. Scoring something like "does this feel like a professional research report" at scale is hard without human review.

Curious how others have approached structured output reliability and multilingual generation in production.

Permabullish dot com if you want to try it out.