Turn any business website into a clean, structured company profile — a graph-based extraction engine.
Project description
autonitia-intel
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 →
Install
pip install autonitia-intel
playwright install chromium # only needed for JavaScript-heavy sites
export OPENAI_API_KEY=sk-... # or pass api_key in the config
Use it
from 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
What you get
{
"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 }
}
How it works
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.
Lenses
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.
Contributing
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".
Hosted version
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 | — | ✅ |
License
MIT — see LICENSE.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file autonitia_intel-0.2.1.tar.gz.
File metadata
- Download URL: autonitia_intel-0.2.1.tar.gz
- Upload date:
- Size: 32.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
67038ce5806ea4af61d1305494e6f40d878c61df6aef99d8df43a9d7c2a66810
|
|
| MD5 |
7a5c50ceac25e5cc92761f43509309ca
|
|
| BLAKE2b-256 |
d98694af702dcb47527a3b0105f2950d3e7c8bae19e0c8c9afe5a832a4c908d0
|
File details
Details for the file autonitia_intel-0.2.1-py3-none-any.whl.
File metadata
- Download URL: autonitia_intel-0.2.1-py3-none-any.whl
- Upload date:
- Size: 35.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1746398a4e07fd94999776badb0076fcc4ce4e113c42aab7f1570571a479af17
|
|
| MD5 |
a32fc1492734bd677869aa470ad5e727
|
|
| BLAKE2b-256 |
c5e94555ced95a5d2ae0caa8e56841730c0c6ec56ab8a922d46e27b0e750f65b
|