Software development, rebuilt AI-native.

The software development life cycle – the whole path from idea to running software – rebuilt AI-native. From a plain-language request, the system builds, tests, reviews its own work, ships, and fixes what breaks. You set direction; the work holds to the standard regulated environments demand.

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A software-lifecycle loop. Business need, Technical design, Build spec, Code and Auto-test run left-to-right across the top rail. The flow turns down the right edge and runs back along the bottom rail through Code review, Deploy, Monitor and Investigate, then a return curve carries the fix into a new Build spec, which re-enters the pipeline. At the centre sits a glowing hub, Living documentation – one source of truth that every design, build and verify step reads from and writes back to, kept current automatically by AI agents. A slow signal travels the whole loop. Living documentation one source of truth Business need Technical design Build spec Code Auto-test Code review Deploy Monitor Investigate
◆ The centre
01 Build & ship
02 Operate & self-heal
Hover a step to inspect

A build path that flows into a self-maintaining loop – all of it reading from one living source of truth.

§ 02 · The lifecycle 01 / 05

An AI system that improves an AI system.

The same lifecycle handles a one-line request or a multi-year programme. Beyond the trivial, experts and agents engineer intent into precise requirements and a sequenced roadmap – and people steer delivery as it evolves.

Plan, code, and the decisions behind them stay in one always-current source of truth. As each requirement firms up, the system builds, tests, reviews, and ships it – then heals and sharpens its own work, always to the direction you set.

It resolves a trade-off the industry treated as fixed: higher output and higher quality at once.

Observed
>10×
Output per engineer-week
Any size
One flow
Request to programme
Always
In sync
Plan, docs & code
Every change
Traceable
Reviewed and tested

Numbers we have seen. Yours depend on the codebase, the team, the cloud, and the practice in place before we arrive.

§ 03 · Idea to software 02 / 05

From a business requirement to working software.

A requirement in plain business language becomes working software – clarified, designed, built, tested, reviewed, and shipped. Every step is held to the standards that govern your business: the architecture it must fit, the strategy it serves, the security and compliance it has to meet – all read from the living documentation the agents work against. You set direction and weigh the trade-offs; each handoff is auditable, each artefact reviewed.

Intent · Message in context

Sarah Hoffmann · Retail Ops 09:14

hey – can we give our long-tenure customers (>2yr) a discount on the new collection? not the outlet stuff. and i need reporting on it.

Requirements Engineering Agent 09:14

Happy to. Two quick things to pin down:

Discount10% – configurable
ReportingMonthly

Requirements Engineering Agent 09:15

Got it – drafting REQ-LOY-001: 24-month tenure, full-price only, 10% (configurable), monthly reporting per channel. Routing the spec for review.

Specification · Structured requirement
REQ-LOY-001 · Loyalty-tier discount
Audience
customers with active tenure ≥ 24 months
Scope
full-price SKUs only (excludes outlet, sale)
Discount
10% at checkout · configurable
Reporting
monthly cost-of-discount per channel
Acceptance criteria
4 scenarios specified
Requirements · 412 lines · 8 sections · 6 acceptance criteria
Structured from living documentation
Build · Code, build, review, test
// REQ-LOY-001 · Loyalty-tier discount
export function applyLoyaltyDiscount(
  customer: Customer,
  cart: Cart
): Cart {
  if (customer.tenureMonths < 24) return cart;

  const eligible = cart.items.filter(
    i => i.category !== 'outlet'
      && i.category !== 'sale'
  );
  const discount = sum(eligible) * 0.10;
  return { ...cart, loyaltyDiscount: discount };
}
Unit tests · 84 · Coverage · 94%
Built from spec · synced to living docs
Live system · Deployed
Cart · Checkout
Cashmere overshirt$189.00
Wool trouser, navy$56.00
Subtotal$245.00
10% loyalty discount applied −$24.50
Total$220.50

Estimated development cost of this feature $3.84 Right-sized per task. The specification runs on a mid model (Sonnet 4.6), code generation on a frontier model (Opus 4.8), and code review on a light model. Building and running the tests are deterministic – they don’t call a model, so they add nothing. Cached context billed at ~0.1×. Illustrative.

Near-zero marginal cost on your own hardware. Sovereign LLM →

§ 04 · Three shapes 03 / 05

Three shapes. One methodology.

Fits your existing stack, architecture, and development standards.

The same methodology, standards, and outputs. The shape changes with how much operating burden you keep.

S · 01

Methodology Pack

A workshop-driven embed. Agents, skills, templates, conventions – transferred to your team. Your tech leads run it from there.

Typically 4–6 weeks.
S · 02

Cloud Deployment

The stack installed into your AWS, GCP, or Azure tenant. Frontier models stay behind your keys. A pilot, then hand-over with a support tail.

Typically 6–10 weeks to first production deployment.
S · 03

Managed

We host and run the AI-SDLC for you. Cadence, agent runs, throughput – under our SLA. For teams that want the outcome, not the operating burden.

Ongoing engagement.

Running it entirely inside your own perimeter? The whole stack – hardware to interface, on a model you own – is the Sovereign LLM

§ 05 · Adoption 04 / 05

How the system enters your organisation.

Two valid shapes – a pilot on a single team, or a full engineering function from the start. Both compound.

§ 06 · Get in touch 05 / 05

Built for AI-native delivery.

Tell us what your teams are trying to ship. We stand up the delivery pipeline – agents on the build, your people on the decisions that matter.

Get in touch