Engineering Intelligence Platform · Deterministic Reasoning

AEGIS — AI engineering intelligence platform

One canonical, evidence-backed graph of your entire software lifecycle — that tells you what will break, why it broke, and who owns it. Deterministically, reproducibly, agentlessly.

The product

What AEGIS does, and who it is for.

The problem

Engineering knowledge is scattered across code, git history, decision docs, APIs, runtime and past incidents — and lives mostly in people's heads. Root cause takes hours of tribal knowledge, nobody knows a change's blast radius, and the AI copilots teams now depend on hallucinate because they have no trustworthy source of truth.

The solution

An agentless platform that compiles your whole software lifecycle into one canonical, evidence-backed graph, reasons over it deterministically, and serves the result to your team and your AI tools. Ask what breaks if you touch a module and get the exact blast radius, tests at risk, owners and governing decisions. Every answer is a graph computation — not an LLM guess — so it is deterministic, reproducible and signed.

How it works

1

Connect your estate agentlessly — read-only, allowlisted access to servers, git, code, ADRs, APIs and telemetry.

2

The Engineering Knowledge Compiler builds one canonical, bi-temporal, cross-layer graph, every node backed by evidence.

3

The deterministic engine answers blast radius, change impact, root cause, ownership and knowledge gaps.

4

Serve it through APIs, a console and MCP — and sign a tamper-evident Truth Dossier anyone can verify offline.

Key capabilities

Cross-layer engineering compilerDeterministic change-impact & blast radiusEvidence-cited root-cause reasoningOwnership & temporal coupling from gitKnowledge health & truth-maintenanceSigned, offline-verifiable Truth Dossier

Where it fits

AEGIS is the intelligence layer that EADD plans and reviews from, so the two are usually deployed together. If the thing you need to make reliable is a business process rather than a codebase, start with Source Flow instead. Browse the rest of the AI catalogue, or see the work we have delivered in Qatar. If you are not yet sure where AI would pay off first, take the free AI audit.

Best for

Platform / DevOpsSRE & IncidentArchitectsEngineering leaders / CTOAI-copilot builders

Industry fit

GovernmentBankingTelecomRegulated / OTLarge enterprise IT

Integrations & sources

Git / GitHubSSH (agentless)MCP · Claude / Cursor / CIOpenTelemetrySAP · Oracle · OdooREST · SQL / NoSQL

Tech stack

Deterministic reasoning coreEngineering Knowledge CompilerNormalized graph storeEd25519-signed dossiersPython · FastAPI

Deployment & security

CloudOn-premisePrivate cloudAgentless & read-onlyYour data stays yours

What it delivers

More
root causes localised
Instant
change-impact query
Always
deterministic & reproducible

Proof / status

Stage: available for scoped pilots and trial deployments.

What we can show today: AEGIS runs continuously on its own codebase, and has been validated against design-partner estates. Every capability carries its recorded limitations.

How you check it: a scoped pilot on your own estate, measured against your current baseline before you commit.

See AEGIS on your own repositories.

Point it at one service and see the graph it builds — what breaks, why, and who owns it.

No obligation · a real answer within one business day · your data stays yours