Capability areas
Services
1AYM sells nine capability areas: AI Opportunity & Feasibility Sprint, AI harness and platform engineering, production AI systems, agentic workflow design, AI governance implementation, data platform and AI enablement, enterprise integrations and automation, a fractional AI platform architect, and embedded engineers on contract. The work is bought as one of five named engagements: a fixed-scope statement of work, a retained architecture engagement, or engineers embedded in your own programme.
Nine areas, one thread: moving organisations from isolated AI experiments to reusable, governed, production capability. Every engineer on the work is certified on the platforms we build on or has shipped inside a top-tier engineering organisation, and we scale the team to the contract. We are headquartered in the UK and deliver across the UK, the Gulf and the US. Most areas are bought as one of the five named engagements on the homepage.
C-01
AI Opportunity & Feasibility Sprint
Turning board-level AI ambition into a plan someone can actually build and fund: leadership sessions, a map of where the value sits, an honest feasibility check against your data and systems, build versus buy, a roadmap and a governance review. The scarce skill is translation, and it is where most AI programmes stall.
C-02
AI harness & platform engineering
Your engineers are already using vendor harnesses: Codex, Claude Code, Cursor. This engagement puts architecture, skills packages, guardrails and CI around that, so the code those tools write reaches production the same way every other change does, and the company is not locked to one vendor's stack.
C-03
Production AI systems
Systems that run against real data every working day rather than demonstrate well once: internal copilots, workflow agents, voice agents and the middleware between them. The design work sits in access, permissions, auditability, failure modes and human review, which is what decides whether the thing survives contact with your operations.
C-04
Agentic workflow design
One operating pattern: the agent proposes, a verifier decides. AI does the flexible reasoning; deterministic rules, schemas, tests and human review decide whether anything is allowed to proceed. Built for finance, data migration and compliance work, where mostly correct is another way of saying wrong.
C-05
Data platform & AI enablement
Connecting AI to the numbers the business already trusts: warehouse integrations, semantic-layer work, data contracts, reconciliation and reporting an AI system can read. The test is simple. An answer from the assistant should match the answer the finance team would have given, definition for definition.
C-06
Enterprise integrations & automation
The middleware, syncs and APIs that put AI inside the tools your teams already use, with the unglamorous parts done properly: idempotent writes, dry-run modes, rollback paths, audit logs, rate-limit handling and daily runs that finish.
C-07
Fractional AI platform architect
Retained senior architecture and delivery ownership for a programme that has the budget and the mandate but nobody who owns the technical decisions. Discovery, target architecture, vendor and model choices, delivery governance, and the enablement that hands all of it to your own team. Typically two to three days a week, under one statement of work.
C-08
Embedded Engineers on Contract
A senior engineer, or a team, inside your programme on a contract from three months. You direct the work day to day. Every engineer meets the same hiring standard as the rest of the practice, and 1AYM holds the cover, so a stream never hangs off one person.
C-09
AI governance implementation
The work of making an AI policy enforceable: permissions the system checks before it acts, verifier gates on the actions that can cause harm, evaluation in CI, an audit trail the gates produce themselves, and data residency architected in where a region is a requirement. It is done during the platform build, which is the only point at which it is cheap.
How it is bought
Five named engagements
The work above is bought as one of these: a fixed-scope statement of work, a retained architecture engagement, or engineers embedded in your own programme.
| Dimension | Who buys it | Commercial shape | What you get |
|---|---|---|---|
| AI Opportunity & Feasibility Sprint | C-suite and transformation leads | Fixed scope · typically 2–4 weeks | Find out which two or three workflows are worth building, what each is worth and what it would cost, before you commit a budget to any of them. |
| Enterprise AI Platform & Agentic Workflows | CIO, CTO, CDO, operations and finance | Fixed-scope architecture, then a production build | Turn scattered pilots into one governed place for AI to run, then put real workflows on it. Access rules, checks and human approval are built in, so the second workflow does not start from scratch the way the first one did. |
| AI Engineering Transformation (Codex & Claude Code) | engineering leaders | Typically 6–12 weeks · enablement, guardrails and CI | Your engineers are already using AI tools, with or without a policy. This puts review, guardrails and CI around that, so the code it writes reaches production the same way every other change does. |
| Fractional AI Platform Architect | scale-ups and enterprises | Retained · typically 2–3 days a week · one statement of work | Senior architecture and delivery ownership without hiring a permanent AI platform leader. We hold the technical decisions, and the standard the work is judged against. |
| Embedded Engineers on Contract | programmes that need senior capacity now | Contract · from three months | A senior engineer, or a team, inside your programme on contract. You direct the work day to day, and we hold the standard every 1AYM engineer is hired against, plus the cover behind them. |
The vocabulary
Two terms, defined in full
Both come up across every area above, and both have their own citable page.
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Last reviewed · 1AYM