You did everything right. You adopted microservices. You embraced cloud-native. You built event-driven systems with Azure Functions, Docker containers, Service Bus, Event Grid, and APIM. Some teams still run on battle-hardened Azure DevOps classic build and release pipelines that no one wants to touch. Others integrated with ERP systems, EDI trading partners, third-party APIs, and supplier feeds that exist entirely outside your own repositories. Your teams could deploy independently. The architecture was modern — and deeply interconnected with the world outside it.
Then time passed. The estate grew to dozens of repositories, hundreds of Azure resources, a mix of YAML and classic pipelines, and a web of internal and external integrations that no single person fully understands anymore.
That architect who knew how the inventory sync actually worked — which classic release pipeline deployed the Function App, which Service Bus topic carried the supplier feed, which external API sat in the middle — gone. The wiki page that explained the event flow — 18 months out of date. And now when production breaks at 2 AM, your on-call engineer spends the first hour just figuring out which system is actually responsible — before the real debugging begins.
This is not negligence. This is the structural consequence of building loosely coupled systems — internal and external — without anything to bind the picture together.
average time to identify the right repo during a production incident
typical age of the last architecture diagram that anyone trusts
who truly understands how the most critical business flows work end to end
These aren't edge cases. They are the predictable outcome of every enterprise that scales cloud-native without a knowledge layer to match.
An alert fires. You know which app is throwing, but not which repo owns the behavior, which pipeline deployed that version, which upstream event or API triggered the path, or which downstream service is now degraded. The first hour of every incident is context reconstruction — not fixing.
Your most experienced engineer knows how the inventory sync actually works — which Function App fires first, which Service Bus topic carries the message, which downstream systems receive the event. When they leave, that mental model leaves with them. And someone always leaves.
The architecture diagram was accurate when it was drawn. Since then, three new Function Apps were deployed, two APIM routes were added, and a supplier integration was rewired. No one updated the wiki. The docs are a historical artifact, not operational truth.
"Which apps still run on .NET 4.8?" "What third-party APIs do we call and who calls them?" "What breaks if we deprecate this endpoint?" Each question triggers a manual investigation across GitHub, Azure Portal, Azure DevOps, APIM, and Confluence. The answers should be instant.
Loose coupling was the whole point — independent deployability, team autonomy, resilience. But loose coupling means there is no single place where the connections are visible. An event fires. Something subscribes. What subscribes? Who owns it? What does it do next? Unknown.
Before any significant change — a schema migration, a new event type, a decommissioned queue — teams need to know the full blast radius. Without a complete dependency picture, every release carries hidden risk. You ship and hope. That is not an engineering posture.
Your estate does not end at your own repositories. ERP systems, EDI trading partners, supplier feeds, payment processors, and third-party SaaS APIs are woven into your business flows. When a supplier shows in-stock but your storefront shows out-of-stock, the answer lives somewhere across that external boundary — and no one has a map.
Many enterprises still run on Azure DevOps classic build and release pipelines — and for good reason. But classic pipelines don't appear in any graph, don't show up in YAML searches, and their deployment history is buried in ADO release logs. When something breaks, knowing whether it was a YAML or classic pipeline that deployed the system is step one — and it's rarely obvious.
The Solution
We ingest your repositories, YAML and classic ADO pipelines, Azure resources, APIs, event subscriptions, external system integrations, and business context — then connect them into a living knowledge graph that answers questions in plain English, backed by evidence from your actual systems. Not documentation. Not diagrams. The real thing, kept current automatically.
Connected
Every repo, pipeline, resource, API, and event linked to everything it touches.
Queryable
Ask in plain English. An AI agent chains graph lookups and cites every answer.
Always Current
Hourly enrichments, daily full rebuild. The graph stays in sync as your estate evolves.
Five layers that turn raw cloud sprawl into operational intelligence — all read-only, all evidence-backed.
Read-only connectors harvest your full estate: GitHub repositories and Actions, Azure DevOps YAML and classic pipelines, Azure Resource Manager, APIM, Event Grid, Service Bus, Key Vault, Application Insights. Nothing is written to production systems.
Source code analysis goes beyond inventory. The pipeline reads HTTP client calls, Event Grid publishers, Service Bus senders and receivers, Azure Functions triggers, controller entry points — extracting behavioral edges, not just resource lists.
Normalized artifacts and relationships persist into a SQL metadata store and a graph/vector search layer. Every fact is evidence-backed: source file path, line context, confidence score, and originating data source.
Business context layers sit on top of the technical topology: capabilities, process flows, supplier integrations, application ownership, event semantics, system-of-record designations. This is what lets the graph answer how a business process works end to end — not just what exists.
A multi-tool AI agent (Claude or configurable LLM) autonomously chains graph lookups to answer your question. Every response cites the artifacts, evidence files, and relationship paths it used. No hallucinations — only grounded answers.
Ask in plain English. Get answers grounded in evidence from your actual estate — not from a diagram someone drew two years ago.
“How is [app-name] deployed — YAML pipeline or classic release?”
“Show me all related artifacts and likely investigation paths for this Application Insights exception.”
“Which repos and configs depend on this APIM endpoint?”
“What third-party APIs do we use and which apps call them?”
“Which apps are still on .NET 4.8 or .NET 6?”
“How does inventory propagate from supplier feed through to the storefront?”
“What changed between the last known-good and the failed deployment?”
“Which systems publish to or subscribe from this Event Grid topic?”
“What is the full lifecycle of a customer order from checkout to fulfillment?”
“Which internal applications are customer-facing and who owns them?”
“What downstream systems break if we decommission this Service Bus queue?”
“How many Function Apps are in this resource group and what do they each do?”
Read-only connectors across your full cloud estate. Production systems are discovery sources only — we never write to them.
External integrations are catalogued as named artifacts and linked to the internal systems that call them — so you can see the full end-to-end flow, not just what's inside your own estate.
Meet your team where they work — browser, terminal, API, or IDE.
Chat interface with live tool-call visibility. D3.js force-directed graph explorer for neighborhood traversal and dependency visualization.
Programmatic access to inventory search, artifact detail, neighbor expansion, and grounded NL query. Integrate with your existing tooling.
Run collect, sync, rebuild-graph, and ask from the command line. Automate inventory snapshots in CI/CD or scheduled jobs.
Query the graph directly from Claude Code or Cursor. Search artifacts, get neighbors, and ask questions without leaving your IDE.
Azure is generally available today. AWS and GCP connectors are on the roadmap — your engagement can include a commitment to multi-cloud coverage as part of the scope.
Generally Available
On Roadmap
On Roadmap
Delivered as a structured consulting engagement with full customization to your estate, toolchain, and query priorities. You own everything we build.
1–2 weeks
4–6 weeks
2–4 weeks
Flexible
Read-Only Safety
We never write to your production systems. Every connector is read-only by design and audited.
You Own the Graph
The knowledge graph runs in your environment. No SaaS dependency, no third-party data sharing.
Always Current
Hourly enrichments and daily full rebuild keep the graph in sync as your estate evolves.
We start with a no-commitment assessment of your estate, define your top questions, and scope the engagement together. Most clients have a working graph within 6–8 weeks.