Enterprise AI consultancy

Enterprise AI your people use every day.

The people who write the strategy write the code. Every 1AYM engineer is certified or has shipped inside Silicon Valley, banking and government-scale systems, and we scale the team to the contract.

OpenAI Select Partner and Claude certified. We run OpenAI, Anthropic and Gemini models in production, and open-source where it fits, so the choice for your use case comes from evidence rather than a vendor deal. We are headquartered in the UK and deliver across the UK, the Gulf and the US.

OpenAI Select Partner
  • Claude Certified Architect – Professional
    Claude Certified Architect – Professional · Held by Tayyeb Mahmud, founder · Verify on Credly
  • Claude Certified Associate – Foundations
    Claude Certified Associate – Foundations · Held by Tayyeb Mahmud, founder · Verify on Credly

Our engineers have delivered inside

Meta, Spotify, UBS, Starling Bank, S&P Global, Sky, Viasat, Société Générale, EIB, Brainlabs and Edvantage.

Method

How it works.

Three stages, in this order. Each one ends with something written down and handed over, so you can stop after any of them and still own what you paid for.

Discover

Typically two to four weeks with your sponsor and the people who do the job today. We agree what the outcome is worth, what stands in the way, and how it will be judged.

You leave with a ranked list of what to build, the architecture for it, and an investment case with real numbers in it.

Ship a working slice

One real workflow, end to end, in your systems and against your data. A demonstration that skips the hard integration proves nothing, so we do not build one.

You leave with working software in your environment, and evidence of what it does.

Run it, governed

Access rules, approval gates, monitoring and training, then a staged rollout. Your team runs it. We measure adoption, cycle time and cost, so the change is reported rather than asserted.

You leave with a running system, a team that can maintain it, and numbers for the board.

The services behind each stage

Offers

What you can buy.

Five named engagements. Each one has a buyer, a stated output or a clear commercial shape, and paperwork you can put in front of procurement without a rewrite.

  1. For C-suite and transformation leads

    AI Opportunity & Feasibility Sprint

    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.

    You get

    • A ranked shortlist of use cases, with the value and the risk against each one
    • The architecture, the data and integration constraints, and which models fit
    • A delivery roadmap and an investment case your finance team can sign off

    Executive AI discovery

  2. For CIO, CTO, CDO, operations and finance

    Enterprise AI Platform & Agentic Workflows

    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.

    You get

    • Platform architecture: who can see what, and the context an AI system is allowed to use
    • Agents that act on real systems, checked against fixed rules and approved by a human before anything lands
    • Testing, monitoring and an operating model your team owns

    Production AI systems

  3. For engineering leaders

    AI Engineering Transformation (Codex & Claude Code)

    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.

    You get

    • Codex and Claude Code workflows mapped to how your teams actually ship
    • Repository guardrails, CI gates and multi-agent delivery patterns
    • Training, documentation and adoption figures you can report on

    AI harness & platform engineering

  4. For scale-ups and enterprises

    Fractional AI Platform Architect

    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.

    You get

    • Architecture ownership, with the model and vendor decisions written down and dated
    • Delivery governance and technical review of work already in flight
    • Team enablement, so the capability stays when the engagement ends

    Fractional AI architect

  5. For programmes that need senior capacity now

    Embedded Engineers on Contract

    Contract · from three months · priced per day

    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.

    You get

    • A named senior engineer or a team inside your programme, typically within weeks
    • The 1AYM standard, and cover across the stream so no role hangs off one person
    • A clean handover, or conversion to a fixed-scope build once the work is scoped

    Embedded engineers

All services

Proof

In production.

Two client environments depend on this work every working day. Every figure is either the client's published case-study number or our own count or measurement on the systems we run, and each one says which.

  • A PE-backed marketing agency

    We lead the architecture of the enterprise AI platform underneath the agency's organisation-wide AI programme. That platform is featured in a published Anthropic customer case study, which describes the platform.

    What we built

    • The publishing pipeline and release controls that let non-technical staff author AI skills and Claude run them
    • An OAuth-secured organisational-context MCP server with default-deny permissions and a full audit trail
    • Automated access provisioning from the HR system, and reporting on what AI actually costs
    • A re-architecture of the skills estate to the Anthropic SDK's structure, with reference files loaded on demand and MCP servers that inject targeted context in place of whole files, which by our own measurement cut the average cost per session by 60%
    Diagram comparing two structures. Before: every skill is one flat file in Notion, most of them over the description limit, so every session loads the whole estate. A sync and compiler we built turns the Notion source into a Claude plugin end to end. After: the plugin is split the way the Anthropic SDK splits it, into a short SKILL.md description, reference files loaded on demand, and MCP servers that inject targeted context, so a session loads only what it needs and the average cost per session is 60% lower.
    • ~1,000Employees reachedAnthropic customer case study
    • ~400Skills authored in four weeksAnthropic customer case study
    • 91%Adoption in North AmericaAnthropic customer case study
  • A government-backed EdTech

    We hold end-to-end technical ownership of the live production estate of a Middle East education company: the exam platform, the APIs and the data, replatformed onto Postgres 16 in Google Cloud's Doha region to meet Gulf data-residency requirements. Its IELTS speaking assessment is not one model call wrapped in a product. It is a measuring instrument, built to Ministry of Education evidence standards and in daily use by the client's own team.

    What we built

    • Whole-interview speaking assessment: the official IELTS band descriptors applied in ordinary code, so every band can be reproduced and inspected line by line
    • Five layers of evidence, from deterministic acoustic measurement to a phoneme-level pronunciation model, with the AI never doing the arithmetic
    • Confidence routing to a human examiner, and accent fairness and agreement with examiners as release gates the build has to pass before it ships
    Diagram: the whole interview, eleven to fourteen minutes of it, passes through five layers of evidence in order. Capture keeps word-level timestamps and hesitations; acoustic measures are taken deterministically in code; a phoneme-level pronunciation model scores delivery; an examiner model applies the official band descriptors three times and the median is taken; and the scoring and guardrails are plain code rather than anything the AI does. A confidence gate then routes roughly 15% of interviews to a human reviewer, with a further 5% audited at random, and both routes arrive at the same band and its evidence. Reviewed cases return as training data for the next version.
    • 3,100+Students on the platformProduction database, 21 August 2026
    • 1,000+Added in the last 90 daysProduction database, 21 August 2026
    • 1,400+Automated tests, from ~300Our count, in CI

    Read the engagement file

Measured outcomes, client unnamed

  • £2–4MForecast annual savingOur model of token consumption, reviewed by the client's finance team
  • 46%Faster first responseOur measurement · client unnamed
  • In the meetingGoverned finance answer, from ~2 hoursOur measurement · client unnamed
  • 82%Less manual triageOur measurement · client unnamed

Comparison

1AYM vs the alternatives.

There are four ways to buy this kind of work. They differ in who writes the code, how long it takes to reach production, and what you still own once the invoice is paid.

How 1AYM compares with a big-4 systems integrator, staff augmentation and strategy-only advisory.
Dimension1AYMBig-4 / systems integratorStaff augmentationStrategy-only advisory
Who does the workCertified engineers, or engineers who shipped inside Meta, Spotify, UBS and Starling Bank. Senior, hands-on, no bench of juniors.A partner sells it; a large mixed-seniority team delivers it.People you brief, manage and direct yourself.Advisers who hand the build to somebody else.
Time to first production sliceTypically weeks. One real workflow, end to end, in your environment.Months, after a discovery phase and a mobilisation phase.As fast as the plan you give them, and no faster.None. The output is a document.
Model choiceFrom production evidence across OpenAI, Anthropic, Gemini and open-source, not from a vendor deal.Usually shaped by the vendor alliance and reseller economics the firm already has.Whatever the people you hired happen to know.A recommendation, with nothing in production behind it.
GovernanceBuilt as platform controls: default-deny access, permission-aware context, deterministic checks, human approval, evaluation gates in CI.A governance workstream, usually a policy set and a steering committee.Yours to define, enforce and audit.A framework for somebody else to implement.
What you are left withRunning systems, the architecture written down, and a team that can extend both.A system your team did not build, and a change-request route into it.Whatever got built. The knowledge leaves when they do.A roadmap, and the build still to buy.
Commercial shapeFixed-scope statements of work, a retained architecture engagement, or embedded engineers from three months. One supplier, one contract.Multi-phase programme against a rate card.Priced per person, per day, open-ended.Fixed fee for the report.

Why it works

Six things we do not trade.

None of them is a methodology. They are the constraints we hold on every engagement, and the reason the work is still running after we leave.

Certified or proven

Every engineer is certified on the platforms we build on, or has shipped inside a top-tier engineering organisation.

Fixed scope

Defined outputs, a defined price and acceptance criteria in the statement of work, or a clear contract.

Governed from day one

Access control, approval gates and evaluation in CI are part of the build, not a later project.

Your people keep the keys

Architecture, code and operating model are documented and handed over. Nothing depends on us staying.

No vendor bias

Chosen per workload across OpenAI, Anthropic, Gemini and open-source, on production evidence rather than vendor loyalty or a vendor deal.

Overlapping coverage

The engineers on your work cover each other's streams, so one person's calendar is never the critical path.

Team

Certified, or proven at the top.

Every engineer here is either certified on the platforms we build on, or has shipped inside a top-tier engineering organisation: Meta, Spotify, UBS, Starling Bank, S&P Global, Sky. We scale the team to the contract.

The senior engineers you will meet first

Tayyeb Mahmud

Founder and Principal Architect

10+ years

AI adoption, agent systems, and Claude and OpenAI platform architecture. Sits with the sponsor, sets the standard, and still writes the code.

Certification

Holds both Anthropic Claude certifications, Architect – Professional and Associate – Foundations, verifiable on Credly.

In production

A PE-backed marketing agency · a government-backed EdTech

Principal Engineer

14 years · London

Data platform work, and the context and memory architecture that decides what an AI system is allowed to see, keep and recall.

Shipped inside

Meta · Spotify · Viasat/Inmarsat

Principal Data Engineer

17 years · London

Pipeline orchestration, CI/CD and data infrastructure: the layer most AI programmes are actually blocked on once the model choice is settled.

Shipped inside

Starling Bank · UBS · S&P Global · Sky · Société Générale

We name colleagues only with their permission, and we do not name clients. 1AYM delivers the work, and the role, the years and the organisations on each card are the standard being met. You meet the engineers before you sign anything.

About 1AYM

1AYM is an enterprise AI and platform consultancy and an OpenAI Select Partner, founded by Tayyeb Mahmud. Headquartered in the UK, we deliver across the UK, the Gulf and the US. We take organisations from AI ambition to governed production systems their people use every day. Two client environments depend on that work today.

Questions

Before you book.

Who actually does the work?

Certified engineers, or engineers who have shipped inside Meta, Spotify, UBS, Starling Bank, S&P Global and Sky, and the same people who scoped the work. There are no juniors on it. Our principal architect leads the architecture and holds the Claude Certified Architect – Professional and Claude Certified Associate – Foundations certifications, both verifiable on Credly. A principal engineer of fourteen years takes the data platform and the context and memory layer; a principal data engineer of seventeen takes pipeline orchestration, CI/CD and data infrastructure. They cover each other's streams, and we scale the team to the contract. 1AYM delivers the work rather than any one person, we name colleagues only with their permission, and you meet the engineers before you sign anything.

How do engagements start?

With a 30-minute call to work out whether your problem is one we should take. If it is, the first paid piece is fixed-scope: usually a two-to-four week opportunity and feasibility sprint, or a first production slice against one real workflow. We will say plainly when the answer is that you do not need us. Expect weeks rather than quarters to a first working slice, because the delay on these projects is almost never the model. It is access, data quality and the approval path, so we start those on day one.

How do you contract?

Three shapes. Most work is a fixed-scope statement of work with defined outputs and a defined price: a sprint, an architecture and build, or an enablement programme. The second is a retained architecture engagement, typically two to three days a week, under a single statement of work. The third is embedded engineers, a senior engineer or a team inside your own programme on a contract from three months, where the constraint is capacity rather than a scoped deliverable. In every case it is one supplier and one contract. A statement of work carries the outputs, the price and the acceptance criteria; an embedded contract carries the roles, the minimum term and the review cadence. Where a programme needs more engineers we scale the team to the contract, and every one of them is held to the same standard.

What does “production” mean to you?

A system real people depend on in their working day. That means identity and access control, monitoring, deterministic checks where the output matters, a human approval path for anything high-risk, and a named owner inside your organisation. Something that runs on an engineer's laptop is a prototype. Two client environments meet that definition today.

How do you handle governance and data?

Governance is built as controls rather than written as a policy: least-privilege and default-deny access, permission-aware context so an AI system sees only what the user is already entitled to see, audit trails that record refusals as well as answers, deterministic validation, human approval sized to risk, and evaluation gates in CI. We work to UK GDPR and the Data Protection Act 2018, plus whatever your sector adds. Where data must stay in a region we architect for it: one production estate runs on Postgres in Google Cloud's Doha region. We hold no ISO or SOC certification.

Which models do you use?

Whichever one the workload argues for. The choice comes from what we run in production across OpenAI, Anthropic, Gemini and open-source, not from a vendor deal. We are model-selective across OpenAI, Anthropic, Gemini and open-source components, chosen per workload rather than by vendor loyalty. In production today: OpenAI audio models for speech assessment with a Gemini fallback, Claude for enterprise platform and agent estates, Codex and Claude Code for engineering work. Where a model is a poor fit for a job we will say so and price the alternative.

Next step

Let’s put one workflow into production.

Bring a workflow that is costing you money, or a plan you are not sure about. Half an hour is enough to tell you whether we can help, what it would take, and roughly what it would cost.

Book a 30-minute call

Last reviewed · 1AYM