the token
Engagements AI-SDLC

AI-SDLC

The complete software development lifecycle in which AI agents do the work and humans set direction. From business intent to working code, continuously. Deployed inside heavily regulated environments, where correctness is not optional and observation is not the same as opinion. Available in four shapes.

See the transformation Get in touch
§ 02 · The transformation 01 / 05

From intent to deployed system, end to end.

A short prompt from the business is the starting point. Behind that prompt sits an agentic loop with comprehensive knowledge of the company — the data, the workflows, the systems, the prior decisions. The loop reads, clarifies, structures, writes, tests, reviews, documents, deploys, and maintains. Each stage carried by an agent with a precise role; each handoff auditable; each artefact reviewed. Humans set direction and weigh trade-offs.

Intent · Message in context

Sarah Hoffmann · Retail Ops 09:14

hey can we add the thing where customers with us for >2yr get 10% off on the new collection — not the outlet stuff. needs monthly reporting. should work like the loyalty thing X did but better.

Requirements Engineer · Agent 09:14

Got it. Drafting REQ-LOY-001 — loyalty-tier discount, 24-month tenure, full-price SKUs only, monthly cost reporting per channel. Structured spec for your review shortly.

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% applied at checkout
Reporting
monthly cost-of-discount per channel
Acceptance criteria
4 scenarios specified
Requirements · 412 lines · 8 sections · 6 acceptance criteria
Build · Code + tests, reviewed
// 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%
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

The result breaks the trade-off the industry treated as a law of nature: higher output AND higher quality, simultaneously. The output gains we have observed are routinely north of ten times. Documentation stays in sync as a property of the pipeline, not as a discipline anyone has to remember. Every change reviewed, tested, and traced. The pace and scale of these gains depend on the codebase, the team, the cloud, and the state of the practice that exists before we arrive; the pattern is consistent.

Observed
>10×
Output per engineer-week
Always
In sync
Documentation
Every commit
Reviewed
Tested and traced

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

§ 03 · Four shapes 02 / 05

Four shapes. One methodology.

The methodology slots into your existing technology stack, enterprise architecture, and development standards.

Same agents, same conventions, same artefacts. The shape changes with how much of the operating burden you want to keep — and where your data and model need to live.

S · 01

Methodology Pack

A workshop-driven embed. The methodology — agents, skills, templates, conventions — transferred to your team. Your tech leads run it from there.

Typically 4–6 weeks.
S · 02

Deploy-to-your-cloud

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

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

Sovereign / On-prem

The full vertical stack inside your perimeter. Hardware, open-weight model, agentic layer, tools, integrations. Your data never leaves the jurisdiction. Independent of OpenAI, Anthropic, anyone else.

Typically 8–14 weeks; longer if hardware procurement extends.
S · 04

Managed

We host and operate the AI-SDLC for you. Sprint cadence, agent runs, throughput — under our operational SLA. For teams that want the outcome and not the operating burden.

Ongoing retainer.
§ 04 · Adoption 03 / 05

How the system enters your organisation.

Adoption is its own decision, with two valid shapes. Some engagements begin as a pilot on a single team — a focused scope, a measured rollout, a clear point at which to commit further. Others deploy across an entire engineering function from the start, where the appetite and the readiness already exist. Both paths converge on the same property: a self-reinforcing pipeline. The longer it runs, the more comprehensive the agents’ understanding of your codebase becomes, and the less ceremony each subsequent change requires.

§ 05 · Get in touch 04 / 05

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