Turn any business website into a clean, structured company profile — and a quick read on where the opportunities are.
Point it at a URL and get back the company's details (description, services, contact info, social presence) plus the tools and capabilities its site exposes. It also tells you how many opportunities a given lens (automation, marketing, sales…) would surface.
Want the full intelligence? The hosted Autonitia Intel API turns these profiles into verified signals, fit/opportunity scores, live industry research, and account briefings. See pricing →
pip install autonitia-intel
playwright install chromium # only needed for JavaScript-heavy sites
export OPENAI_API_KEY=sk-... # or pass api_key in the configfrom autonitia_intel import ProfileGraph
config = {
"llm": {"model": "gpt-4o-mini"}, # add "api_key": "sk-..." or use the env var
"lens": "automation", # automation | marketing | sales | …
"verbose": True,
}
graph = ProfileGraph(source="https://example.com", config=config)
result = graph.run()
print(result.model_dump_json(indent=2))Prefer the command line?
python run.py https://example.com --lens marketing --json{
"target_company": {
"name": "Example Co",
"industry": "Real Estate",
"description": "...",
"location": "Dubai, UAE",
"contact": { "phones": ["..."], "emails": ["..."], "addresses": ["..."] }
},
"digital_presence": { "social_media": { "linkedin": "...", "instagram": "..." } },
"capabilities_present": ["phone", "whatsapp", "online_booking"],
"pro_features": { "lens": "automation", "opportunities_found": 2 }
}It fetches the site politely (respecting robots.txt, with retries and a real-browser fallback for JS-heavy pages), uses one LLM call to read out the company profile, and runs fast local checks to spot the tools and capabilities present. The opportunity count for a lens is computed locally — no guessing.
A lens is the perspective you analyse a site through — automation, marketing, sales, and more. Lenses and the signals they look for are defined as simple YAML packs in autonitia_intel/signal_packs/, so you can add a new lens or industry pack without touching the Python.
Contributions welcome — the easiest place to start is a signal pack: drop a YAML file under signal_packs/lenses/ or signal_packs/industries/ and open a PR. Run the tests with pytest -m "not integration".
This open-source engine gives you the profile and the opportunity count. The hosted Autonitia Intel turns those opportunities into verified, ranked, outreach-ready intelligence over a REST API.
→ Docs & access: autonitia.ai/intel
Free — autonitia-intel |
Hosted — Autonitia Intel | |
|---|---|---|
| Company profile + contact + socials | ✅ | ✅ |
| Tool & capability detection | ✅ | ✅ |
| Opportunity count | ✅ | — |
| Verified capability analysis | — | ✅ |
| Pain signals with evidence | — | ✅ |
| Scoring (fit / opportunity / confidence) | — | ✅ |
| Offer matching + ranked opportunities | — | ✅ |
| Account briefings (12-question business brief) | — | ✅ |
| Live industry & competitor research (cited) | — | ✅ |
| Industry benchmarks | — | ✅ |
| External enrichment (founders, HQ, funding) | — | ✅ |
| REST API, async jobs, webhooks, CRM export | — | ✅ |
MIT — see LICENSE.