AION Indian Market Intelligence for Macro Event & Sector Impact Analysis MCP server

AION Indian Market Intelligence for Macro Event & Sector Impact Analysis

An API that turns an Indian financial headline into a signed sector vector — which sectors are exposed, in which direction, and who is affected.

A sentiment score says negative 0.82. Negative for whom? One event moves different sectors in opposite directions, and a single number has to pick one and discard the rest.

What it does

flowchart LR
    A["Your headline feed<br/>NSE circulars · news · RSS"] --> B["analyze(headline)"]
    B --> C["AION IMI<br/>sector_vector + context"]
    C --> D["Your LLM · agent · dashboard"]
    D --> E["Prioritised actions<br/>risk flags · alerts · research"]

Quickstart in 60 seconds

pip install aion-indian-market-intelligence
export AION_API_KEY="<your_api_key>"

from aion_indian_market_intelligence import analyze

intel = analyze("RBI hikes repo rate by 25 bps")
print(intel["sector_vector"])
# {'Information Technology': 0.18, 'Realty': -0.52,
#  'Financial Services': -0.31, 'Automobile and Auto Components': -0.26}

Get a key at dashboard.aiondashboard.site/access/register. The free tier is 1,000 requests a month.

What this is NOT

- Not investment advice. It describes exposure; it does not tell you what to do about it.

- Not a trading signal. There is no entry, exit, target or position size anywhere in the output.

- Not a downloadable model. Inference runs through the hosted API. This repository contains no weights, coefficients, taxonomy files or server code.

Before / After

The same headline, through a polarity model and through this one:

Unseasonal rainfall and hailstorm hit apple orchards in Himachal Pradesh in April

Market implication: agriculture-linked exposure under pressure, logistics bottlenecks possible, substitute supply chains may benefit.

Use Cases

Three patterns, all of which people are already building. Longer write-ups in the DEV.to article.

1. LLM / RAG augmentation

For AI engineers. Store the sector vector next to the headline in your vector store. When the LLM retrieves the document it also retrieves India-specific impact context, so it reasons about the event rather than paraphrasing it.

intel = analyze(headline)
enriched = (
    f"Headline: {headline}\n"
    f"Sector impact: {intel['sector_vector']}\n"
    f"Stakeholders: {intel.get('stakeholder_views')}"
)
# embed `enriched`, or store the vector as metadata alongside the headline

2. SaaS sales & CRM intelligence

For B2B SaaS founders. Map a macro event onto your own customer list by sector. Instead of "this is negative", your system knows which accounts sit in the affected sectors, in which direction, and how hard.

flowchart TD
    E["Policy · weather · commodity shock"] --> I["analyze() → sector impact + severity"]
    I --> C["Your CRM / lead list, by sector"]
    C --> L["LLM with India-specific causal context on YOUR accounts"]
    L --> A["Prioritised leads · risk flags · personalised outreach"]

Shortened. The full README is on GitHub.