Requirements, design, build, test, maintain, observe, and security scans — traced end to end, with agentic AI doing the work and a human approving every gate.
From the first requirement to the security scan before release — ADIS is the intelligence layer over how software actually gets built.
Every stage feeds the same grid — a requirement traces through design, build, test, and security evidence without leaving the system. Click any stage to see how it works.
The Orchestrator routes every new requirement through a pipeline of specialized agents. Before a single line ships, a human approver signs off.
Requirement, discovery, and coder agents work the pipeline under human approval — generating code and reporting status without a stand-up.
ADIS is the system. A spurtree POD is the dedicated team running your requirements through it — every stage, every week.
Requirements, design review, build, testing, maintenance, observability, and security scans — delivered by a dedicated spurtree team working directly in ADIS, not bolted on from outside it.
Field notes on the delivery lifecycle — the problems every engineering team hits, and how ADIS is built to keep them in check.
The questions that come up in every evaluation — answered directly, including the ones about control, cost, and what happens if you leave.
ADIS (Active Delivery Intelligence) is a software delivery platform that runs the full lifecycle — requirements, design, build, testing, maintenance, observability, and security scanning — as a single system. AI agents do the work at each stage, and a human approves before anything ships. It is built by SpurTree Technologies, a software consultancy founded in 2010, which runs its own client delivery on it.
Seven stages: requirements, design, build, testing, maintenance, observability, and security scanning. Each stage writes into the same traceability grid, so a single requirement can be followed through design approval, the code that implemented it, the tests that cover it, and the security findings against it without leaving the system.
Security scanning runs OpenGrep, SonarQube, and CodeQL automatically. Incremental fingerprinting means only new findings resurface, rather than the full backlog on every run.
No. ADIS prepares changes; people ship them. Every change requires a human-approved plan before an agent starts work, and a human-reviewed pull request before it merges.
Before a reviewer ever sees that pull request, the change has already passed compile verification, independent static analysis, secrets scanning, and smoke tests in an isolated environment. The approval gates are yours to configure per environment tier.
A coding assistant makes an individual developer faster inside their editor. ADIS operates at the level of the delivery organisation: it holds the requirements, the approvals, the test evidence, and the audit trail — none of which survive an editor session.
The two are not mutually exclusive, and most teams run both: assistants for exploratory and novel work, ADIS for the recurring delivery pipeline that has to be traceable afterwards.
The same engineers who maintain the rest of the codebase — which is why ADIS generates against your coding standards rather than a general model of how code usually looks. Standards are enforced on every change, plans are grounded in the actual repository, and each change traces back to the requirement that caused it.
The practical difference is not that the code is machine-written; it is that the reasoning behind it is still on record three years later.
No. ADIS works against your existing repositories and synchronises requirements with Jira and Azure DevOps, so it sits on top of your current stack rather than replacing it.
It also runs standalone. If you would rather not carry a separate tracker, ADIS holds requirements, approvals, and delivery status itself — the integration is there for teams that already have a tracker they intend to keep.
Access is scoped to a single project on every query, and each project has its own knowledge index, so AI context never crosses between projects. Each run executes in an isolated worktree with its own per-source credentials.
Every API call, model call, and synchronisation is logged with a defined retention period, and the security architecture is available for review before you commit to anything.
A spurtree POD — a dedicated team running your roadmap through ADIS across all seven stages — starts at $20,000 per month for new product development. Engagements focused on maintaining an existing codebase are typically priced significantly lower, because the work is scan-driven and more of it runs through the pipeline.
Usage is metered per project, with token cost reported per change against quotas you set, so the cost of a given change is visible rather than absorbed.
You keep everything it produced. Code and pull requests live in your repositories, requirements sync to your tracker, and reports export — all in standard formats, in systems you already control. If you turn ADIS off you lose the engine, not the output.
Yes. A typical pilot scopes one project with scanning and the fix queue only, running through your own approval gates, over 30 days, measured against your existing delivery baseline.
A spurtree POD is a dedicated SpurTree team that runs your requirements through ADIS end to end — requirements, design review, build, testing, maintenance, observability, and security scanning — working inside the platform rather than alongside it. PODs are scoped to your roadmap.
Log in if you're already on the platform, or talk to us about a spurtree POD to run the whole lifecycle for you.