Business Insights v2

Find a business's name, logo, merchant category code (MCC), head-office address with map coordinates, phone number, email address and social media links from its website. It's built for bank teams that do merchant onboarding, KYB, merchant risk and transaction enrichment.

https://github.com/user-attachments/assets/c81a6b4b-88c0-450d-95f9-4b7f3657b9bd

Every answer comes with a status (ok, needs_review, not_found or error), a confidence, its source and the evidence it rests on, so an analyst can check it. Nothing is guessed.

$ bi2 analyze https://bellwoodsbrewery.com/ https://www.barvolo.com/ ... --fast
[5/24] https://bellwoodsbrewery.com/  name ok  logo ok  mcc ok  address ok  phone ok  email ok  social ok  7.1 s

How it works

Evidence first, models last. bi2 reads the page once, with no model: its structured data, meta tags, the images that could be its logo with their places on the screenshot, its phone numbers, emails, postcodes and links, and what Wikidata knows about big brands. Then seven field specialists run at once, each climbing a ladder that stops at the first sure answer:

1. Rules, free and instant.

2. Jev, a typed-decision model, picks among the rules' own candidates with a probability: which numbered box is the logo, which code fits, which number is the main one. It answers in under a second and can't make an answer up.

3. An LLM, Gemini unless you choose another, reads what no candidate covers, like a name inside a logo or an address in an unfamiliar format, and must quote the page.

4. An investigator searches the web for what the pages never say, like a chain's head office.

Read more in the architecture and what a site costs.

Related MCP server: AgentRank MCP

Results

On the 56 gold sites, with Jev and Gemini, on the same saved pages every run:

On the 166-site test set, which no rule was tuned on, counting the answers a person or a drafted check against the site's own pages has verified:

The MCC checks are drafted against each site's title and description, and not yet confirmed by a person.

A sure answer is one nobody checks, so the costliest mistake is a sure answer that's wrong. How often that happened, counted with python eval/calibration.py:

About 3 model calls a site, almost all to Jev: under a tenth of a cent a site at list prices, and nothing on the free tiers.

Install

pip install "business-insights[mcp]"     # or: uv tool install "business-insights[mcp]"
playwright install chromium

Add a key, or both. Without one, bi2 uses its rules alone.

bi2 keys set jev          # saved in the system keychain; or set JEV_API_KEY
bi2 keys set gemini       # or set GEMINI_API_KEY
bi2 doctor                # checks the keys, the models, the browser and the free services

Free keys work. bi2 keeps every model under its free tier's limits per minute and per day, counts each day's requests across runs, and keeps every answer on disk, so asking about the same site twice asks no model twice.

Choose the LLM

Gemini is the default, since its free tier is enough to try bi2. Any of these can take its place or be its backup, named as provider:model:

A model bi2 lists can go by its name alone, like claude-haiku-5-5, and so can one whose name says whose it is, as in v1: gpt-…, claude-…, gemini-…, mistral-…, grok-…. Any other server that speaks OpenAI's chat API can be added by its address.

bi2 config walks you through it with the arrow keys: the provider, then the model, listing the ones your key can use, then any settings, like the temperature, a backup model or the calls a site may make, or the defaults. It saves them in a bi2.toml, yours for every folder or this folder's for a project, and the key in the system keychain, never in the file.

Or set it wherever bi2 runs:

[ai]
llm_model = "anthropic:claude-haiku-5-5"
backup_model = "gemini-flash-lite-latest"  # asked when the first is down or out of requests; "none" for none
search_model = "gemini-3.5-flash"          # web searches for head offices; only Gemini searches here
temperature = 0.0

[providers.ollama]
base_url = "http://gpu-box:11434/v1"       # another machine's Ollama

[models."openai:gpt-5.4-mini"]
per_minute = 5000                          # a paid key's limits, in place of the free tier's

Each layer overrides the one before it: the defaults, your own bi2.toml, this folder's (or the file BI2_CONFIG names), environment variables like BI2_AI__TEMPERATURE=0.2, then a command's flags. bi2 config show prints every setting and where it came from, and bi2 doctor checks that each provider takes its key and offers the model you named. A model bi2 doesn't list runs with modest limits and no price, until a [models."provider:model"] table gives it its own.

Use

As an MCP server

Add bi2 to Claude Code, Claude Desktop or any MCP client:

{
  "mcpServers": {
    "business-insights": { "command": "bi2", "args": ["mcp"] }
  }
}

Its tools: analyze_website, explain_field (how one field was decided, step by step), find_mcc and model_usage. Add "--model", "anthropic:claude-haiku-5-5" to the args for another LLM; the server reads keys from the keychain or the environment, like the terminal app.

With Docker

docker run --rm -e JEV_API_KEY -e GEMINI_API_KEY -v "$PWD:/data" ghcr.io/haneeshraj/business-insights-v2 analyze https://example.com

The container reads a bi2.toml in the folder mounted at /data, or the model from the environment: -e BI2_AI__LLM_MODEL=openai:gpt-5.4-mini -e OPENAI_API_KEY. For an Ollama on the same machine, add -e BI2_PROVIDERS__OLLAMA__BASE_URL=http://host.docker.internal:11434/v1.

In CI

Set the model and the provider's key in the job's environment, with the key from the CI's secrets:

- run: bi2 analyze --file sites.txt --out results
  env:
    BI2_AI__LLM_MODEL: anthropic:claude-haiku-5-5
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

This repository's nightly eval (.github/workflows/eval.yml) takes its models from the repository variables BI2_LLM_MODEL and BI2_BACKUP_MODEL, and a run started by hand can name its own.

Docs

• Architecture: what v2 does and why

• Costs: what a site costs on each provider's models, and how long it takes

• Codebase guide: how the code is organized

• Evaluation: the gold set, the test set and how answers are judged

• Contributing: setup and checks

License

MIT. See LICENSE.