~/practice $ describe purple-zebra

Deliver code with AI. Keep your engineers in charge.

We modernize legacy systems and deliver features faster with AI — while your engineers stay in charge of the architecture, the review, and the production calls.

Talk to us →See the methodology →
  • 01AI-SDLC consultancyadvisoryweeks
  • 02Legacy modernizationengagementmonths
  • 03Project deliverybuildvariable

01.services

Three ways we deliver.

Pick the engagement model that matches where your team is. All three start with the same diagnostic — we don't onboard blind.

01advisory

AI-SDLC consultancy

Assess and upgrade your team's tooling, review practices, and delivery workflow against where AI-assisted work actually breaks. Land with a sequenced plan and the people to execute it.

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02engagement

Legacy modernization

Move legacy estates forward — monolith decomposition, framework upgrades, dependency migrations — with AI handling the mechanical parts and senior engineers handling the architectural cuts. CI stays green through every step.

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03build

Project-based delivery

Embedded AI-backed delivery teams that deliver features in days, not sprints, when you need outside leverage. We hand back and stay reachable for follow-on work.

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03.by-the-numbers

What this looks like in practice.

One project. AI-driven. Production banking-grade behavioural parity. The methodology turns into measurable proof — at the LOC count, at the test count, at the day count.

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04.methodology

AI-first context-driven development

The methodology we use, teach, and licence inside engagements. Stack-agnostic, tool-agnostic, and built so AI-assisted delivery stays reviewable, auditable, and accountable to the engineers reading the diff.

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05.product

private beta

Ignix67

The platform we built to operationalise Context-Driven Development. Five capabilities in private beta, all driven by the same trust model: bounded autonomy, scoped credentials, every step a pull request.

  • 01PR ReviewAI-assisted code review on every pull request, across Azure DevOps, Gitea, and GitHub from one set of rules. Reads the diff, applies your per-repo review instructions, posts threaded comments and a reviewer vote — running through a predictable, audit-friendly pipeline rather than an open-ended agent loop.
  • 02Work-items intelligenceNatural-language access to your Azure DevOps work items. Query by ID or in plain English — the chatbot translates to WIQL, surfaces missing acceptance criteria, and shows what's blocking what.
  • 03Service catalogA living catalog of who owns what — services, contacts, tech stack — maintained by an agent that reads your code, not a wiki page someone forgot to update.
  • 04Engineering semantic layerYour engineering knowledge — wikis, code, work items, service catalog — woven into a single layer the assistant and other agents query. Updates flow event-driven: a wiki edit, a new PR, a doc change — all surface without a manual refresh.
  • 05Platform assistantA chatbot that knows your platform — work items, code, wikis, service catalog, and your Ignix session history. Each turn is grounded in your engineering semantic layer before it reaches the model, so answers stay rooted in your team's actual context, not the model's training data.
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