Methodology — How AIStackIntel Works
Effective: August 9, 2026
AIStackIntel is an AI adoption intelligence database. This page explains how we collect,
score and present the data you see. It is important to us that the intelligence is
evidence-based and auditable — not guesswork presented as fact.
1. What we track
For each company we record the AI-related technologies and adoption signals we can observe
from public sources:
- LLM APIs & model providers — e.g. OpenAI API, Anthropic/Claude, Hugging Face
- Agent & RAG frameworks — e.g. LangChain, LlamaIndex, vector databases like Pinecone
- AI product features — chat widgets, copilots, AI features announced publicly
- Infrastructure — hosting, CDN, framework and cloud provider (context, not AI itself)
- Hiring signals — AI-related roles (AI Engineer, ML Engineer, RAG Engineer, LLM Engineer…)
collected from careers pages and public ATS feeds (Greenhouse, Ashby, Lever).
2. Data sources (all public)
- Company websites and public pages
- Public documentation (docs subdomains, API references)
- Job postings and public ATS feeds
- Public GitHub repositories and engineering references
- Product launches and public announcements
We do not access private accounts, private repositories, or non-public information.
3. Signal collection
- Each domain is probed with multiple signal layers: HTTP/DNS headers, cookies,
HTML/JavaScript framework markers, documentation subdomains, hiring signals and public references.
- Pages protected by bot defense (e.g. Cloudflare) may yield fewer signals — we report
lower confidence instead of guessing.
4. Confidence scoring
- Every detection carries a confidence score (0–100%) based on signal strength and corroboration.
- A single mention on a careers page counts less than an API integration referenced in
documentation plus a GitHub repository.
- We never claim 100% accuracy. Absence of evidence is not evidence of absence:
a low score may simply mean "no public signals detected yet".
5. AI Adoption Score™ (0–100)
The Adoption Score combines evidence-based dimensions:
- Technology Adoption — detected LLM APIs, frameworks and AI infrastructure
- AI Talent — AI-related hiring signals
- Product Integration — AI features and widgets on the product surface
- AI Infrastructure — vector databases, model platforms, agent tooling
- Public Signals — documentation, engineering references, announcements
Representative component weights (full scoring visible on each profile):
| Dimension | Weight |
|---|
| Technology Adoption | ~30% |
| AI Talent (hiring) | ~25% |
| Product Integration | ~25% |
| Public Signals | ~20% |
The score is designed to be conservative: we would rather under-claim than over-claim.
6. Freshness
- Technology signals are re-collected on a weekly basis; hiring signals monthly.
- Every profile page shows its last updated date.
7. Corrections
See something wrong? We audit evidence on request. Email [email protected]
with the company and the signal you believe is incorrect, and we will re-check and correct it.