Platform
One compiled substrate beneath every workbench.
Graph, policy, evidence and governed AI — shared by every discipline, owned by none of them.
The category
What is Engineering Intelligence?
Engineering Intelligence is the compilation of an organization's engineering artifacts — requirements, architecture, code, verification, decisions — into a living model that answers engineering questions with evidence.
Not search, which finds documents. Not generation, which produces text. Answering: What is unproven? What does this change reach? Where has implementation drifted from intent? Why was this decided — and what breaks if we revisit it?
Every answer is computed deterministically from your repository, and every answer cites the artifacts — and the commit — behind it.
Business Intelligence made business data answerable, and no serious company now runs without it. Engineering Intelligence does the same for engineering knowledge — at the standard of proof engineering demands.
- Knowledge
- Compiled from your artifacts into a typed graph — never stored in another silo.
- Reasoning
- Deterministic engines that answer the same question the same way, every run, on any machine.
- Evidence
- Findings that regenerate byte-identically at a commit. That property is what makes them evidence.
- Governance
- Rules and rigour compiled once, enforced uniformly — never re-interpreted at the point of use.
- Memory
- Decisions carry their rationale and their rejected alternatives, so knowledge outlives tenure.
- Traceability & impact
- Every artifact knows what it reaches and what reaches it — across disciplines and across time.
How it works
One compiled pipeline, closed through your repository.
Everything is a file in Git. Ejadah compiles those files into facts, facts into a typed knowledge graph, and runs deterministic analysis over it. Findings carry their own provenance. AI sits at the rim — proposing edits that return to the repository as ordinary commits a human reviews.
- 01
Repository
your artifacts, in Git — the only source of truth
- 02
Facts
what the sources say, extracted 1:1, uninterpreted
- 03
Knowledge
roles become typed relationships in one compiled graph
- 04
Evidence
deterministic findings and metrics, with provenance
- 05
Intelligence
questions answered, impact traced, drift detected
- 06
Git edits
AI proposals return as commits — reviewed, then part of the truth
The loop closes: step six returns to step one. Nothing enters the truth except through the repository.
- 01
Requirements
intent, captured
- 02
Architecture
structure, allocated
- 03
Implementation
code, linked
- 04
Verification
claims, tested
- 05
Certification
evidence, assembled