Why delivery data never becomes delivery insight
Every engineering org already has the data — it just lives in eight disconnected tools. Jira knows the tickets, GitHub knows the commits, Jenkins knows the builds, SonarQube knows the quality, Datadog knows the incidents. None of them know the whole picture, and no dashboard stitches them into an answer a VP of Engineering can act on.
EDIP fixes the synthesis problem. It pulls the signals into a single delivery knowledge graph, then runs autonomous agents and predictive models on top so the conversation moves from 'pull me a report' to 'here is the release at risk, here is why, and here is the intervention.'
Key pain points
- ✕Delivery signals fragmented across Jira, Azure DevOps, GitHub, Jenkins, SonarQube and Datadog
- ✕No early warning on at-risk releases or overloaded teams until it is too late
- ✕Requirements quality and dependency risk invisible until they cause slippage
- ✕Dashboards that chart problems but never recommend an action
- ✕Leadership questions answered by manual spreadsheet archaeology, not live evidence
What we built
EDIP is a full-stack Next.js 15 (App Router, RSC) application — a single executive command center plus 30 module pages across Delivery Intelligence, Engineering Intelligence, DevOps Intelligence, AI & Automation, Analytics & Reports and Admin. It is AI-native but provider-agnostic: out of the box it runs on a deterministic local reasoner and the relational store with zero external infra, then wires in Ollama/vLLM, Neo4j, Qdrant, Kafka and Temporal through environment-configured adapters.
The intelligence layer is three things working together. The AI Delivery Copilot answers natural-language questions grounded in the live delivery knowledge graph — real evidence, not hallucination. Autonomous Delivery Agents (Release, Requirement, Test, Risk, PMO and DevOps) analyze live state and persist concrete actions. The AI Intervention Engine recommends those actions — add QA, split the release, resolve the dependency — instead of just visualising the problem.
Predictive models score sprint-failure, delay, quality, security and resource risk before they materialise, and DORA metrics give leadership the industry-standard delivery baseline alongside it.
Technology stack
Frontend
Data
Intelligence
Knowledge & Events
Key engineering decisions
A delivery knowledge graph, not another dashboard
Instead of bolting charts onto each tool, EDIP models projects, requirements, dependencies and incidents as a graph (Neo4j). The Copilot and agents reason over that graph, so answers are grounded in linked evidence rather than a single tool's slice.
Recommend actions, not just problems
The AI Intervention Engine is the core bet: every detected risk resolves to a concrete, persisted action (add QA, split release, resolve dependency). Charting a problem nobody acts on is the failure mode we designed against.
AI-native but provider-agnostic, infra-optional
EDIP runs with a deterministic local reasoner and the relational store out of the box — no paid APIs, no external infra. Ollama/vLLM, Neo4j, Qdrant, Kafka and Temporal are environment-wired adapters, so it is verifiable on a laptop and scales to Kubernetes unchanged.
Why this architecture
EDIP runs locally with no external infrastructure and scales to the full Neo4j/Qdrant/Kafka/Temporal stack via configuration. It is built on the same Next.js + Prisma foundation as the rest of the Swenivis suite. If you want one place that answers 'which releases are at risk and what should we do about it' — grounded in your real delivery data — we can walk you through the architecture and the agent runtime.
A note on our approach
We built EDIP because every leadership team we have worked with is data-rich and answer-poor. The tools all hold a piece of the truth and none of them hold the whole. EDIP's job is not to draw another burndown chart — it is to tell you the release is going to slip, why, and what to do today. The local-first design means you can verify every claim on your own machine before you trust it with production data.
Interested in building this?
Every engagement starts with a two-week discovery sprint. We assess your requirements, existing stack, and data readiness — then give you a concrete build plan and cost estimate.
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