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Client-side through production

Get AI into production without a 12-month roadmap.

Most AI programmes stall between the demo and the day shift. You commission a nine-year unit that has already put AI into production at 1,000-person scale, with data and software engineering pedigree from Meta, Spotify, UBS, Starling Bank, S&P Global, Sky, Société Générale and the European Investment Bank.

  • Senior-only unit
  • Vendor-independent
  • Your people keep the keys
  • Stay after go-live
  • Published by Anthropic
Book a 30-minute callSee the practice

Shipped inside

  • EIB

  • 3

    Principals. One working unit.

  • 2 wks

    CFO concept to finance-grade production

  • 40+ yrs

    Combined engineering & delivery experience

  • ~800

    Processes automated, one estate

  • 40

    Subsidiaries, each on its own ERP, unifying onto one NetSuite

The practice

Your first quarter is delivery.

Three principals, one standard. The skills overlap on purpose, so the work is interchangeable. One of us leads on AI architecture. The other two come from corporate data and platform careers. You get that mix on day one, inside organisations that do not buy theatre. If the honest answer is "don't build it", that's the answer you get.

  • Interchangeable by design

    Coverage overlaps on purpose. Any of the three can pick up the others' work, so a stream never hangs off one person. You do not pay for a kickoff quarter of people learning each other's judgement.

  • Chatbots from 2022. GenAI from 2024.

    The data and infrastructure careers came first. Chatbot work from 2022. GenAI from 2024, so the failure modes are already priced in: evaluation at Meta, agent estates at 1,000-person scale, and the platforms those systems sit on.

  • Enterprise-grade is the baseline

    Meta, Spotify, UBS, Starling Bank, S&P Global, Sky, Société Générale, the European Investment Bank, Viasat. A broken pipeline has a real cost there. That is the bar we bring to an AI engagement.

What you are buying

What we do

A forward deployment unit

We embed in the estate, ship to production, and stay after go-live. Shared standards on day one. The people you meet are the people who build.

Traditional consultancies

  • Big-4 engagement

    A partner pitches, juniors build, you get a roadmap. 6–18 months to production, if it gets there.

  • Staff augmentation

    CVs added to your backlog. Output depends on your own architecture and management bandwidth.

The offer

What has to work, or the AI does not.

Adoption, the data platform, and the orchestration underneath. Overlap on purpose, so a workstream never hangs off one person.

  • AI adoption & agent architecture

    Agent estates with verifier gates, platform governance, and the enablement that makes adoption stick: from C-suite advisory through hands-on build. Your people end up authoring the automations, not renting them.

    Recent: founded and now lead the architecture of a ~800 skill, 82-plugin platform; cut per-session token overhead 46%; shipped a production MCP server.

    TM

    Principal Architect

  • Data platform & context engineering

    Memory and semantic layers that give production agents the business's own context, on data platforms engineered to carry them. Agents open a task already knowing the relevant conversations, not a blank prompt.

    Recent: a 12× throughput re-architecture; GenAI evaluation pipelines at Meta; executive KPI rails at Spotify.

    AA

    Principal Engineer

  • Orchestration & infrastructure

    Airflow orchestration, warehouse structure and CI/CD deepened to self-healing. The platform recovers without a war room.

    Heritage: real-time fraud and machine learning at Starling Bank; streaming platforms at UBS, S&P Global and Sky; Basel III reporting across 13 Société Générale entities.

    GM

    Principal Data Engineer

Coverage map · no single point of failure

Scroll the table sideways for all three columns

CapabilityTMAAGM
Custom harness delivery
LLM & agent systems
RAG, memory & context engineering
AI evaluation & verification
Prompt engineering & skills authoring
Claude Code & platform governance
Data platforms & warehousing
CI/CD, cloud & infrastructure
React / front-end delivery
C-suite & stakeholder management

The principals

The people you meet are the people who build.

Three principals, one standard. The skills overlap on purpose, so the work is interchangeable.

  • Principal Architect

    10+ years

    FDE · AI adoption, agent systems & Claude architecture

    Claude Certified Architect (Professional) and Claude Certified Associate. Builds the infrastructure other people's AI runs on: from C-suite advisory down to the compiler, the MCP server, and the hooks that agents cannot talk their way past.

    Verify on Credly

    • Founded and lead the architecture of a company-wide AI skills platform: ~800 skills across 82 plugins, auto-synced from Notion every 10 minutes. Cut per-session token overhead 46%, with discovery held at 95.5% on a blind eval.
    • Routing and context engineering that holds: Claude slide-deck preparation went from 50 minutes to 5, with a 74% efficiency lift estate-wide. Tokens saved because the model opened the task already knowing what to use.
    • Built and shipped a production OAuth 2.0 MCP server in Go, solo: own authorization server, default-deny entitlement gate, unconditional audit trail. Now serving governed org and client context to Claude sessions company-wide.
    • Automated Claude access provisioning for a 1,000-person agency. Independent audit of billed Claude spend, prepared for the Finance Director.
    • Designed the 18-agent, 24-skill operating system a finance data team now runs its Snowflake warehouse with. Surfaced material sandbox data defects before cutover on a NetSuite migration.

    Stack: Python · Go · TypeScript/Node.js · multi-agent systems · RAG · MCP · Snowflake · BigQuery · Cloud Run · Claude/GPT/Gemini

  • Principal Engineer

    14 years · London

    FDE · data platform, context & memory architecture

    The data platform and the context the agents stand on. Shipped inside Meta and Spotify. Nine years in the working unit.

    • A memory and semantic layer so production agents open a task already knowing the relevant conversations, not a blank prompt.
    • Data platforms rebuilt for the load: 12× throughput, ETL that used to take the night now finishes in a fraction of it. Tooling you were renting, brought in-house.
    • At Meta, GenAI quality scored in production. When the model moved, regressions showed up for the engineers who had to act. PII controls that had to stand up to FTC and EU rules.
    • At Spotify, executive KPIs on production pipelines for user counts in the hundreds of millions. Fraud-adjusted, not a private reporting stack.
    • At Viasat/Inmarsat, the enterprise data platform underneath a satellite business.

    Stack: Python (asyncio/FastAPI) · dbt · Airflow · Snowflake · BigQuery · Databricks/Spark · Beam/Scio · GCP/AWS/Azure · Terraform · React/Vue/Next.js · TypeScript

  • Principal Data Engineer

    17 years · London

    FDE · pipeline orchestration, CI/CD & data infrastructure

    The orchestration and the infrastructure the estate recovers on. Shipped inside Starling, UBS, Sky and Société Générale. Nine years in the working unit.

    • Pipelines that self-heal. Airflow, warehouse structure, CI/CD under Claude managed agents. The platform recovers without a war room.
    • Four years inside Sky, one of Europe's largest broadcasters, sitting with the team that ran the estate.
    • At Starling, fraud assessment on live payments, scored as the transaction cleared.
    • At UBS and S&P, real-time market data that lands while the market is still open.
    • At Société Générale, Basel III reporting across 13 European entities. The regulated work was the brief.

    Stack: Airflow · Python · Java · SQL/Postgres · Kafka/Flink/Spark · AWS/Azure/GCP · Snowflake-pattern warehousing · CI/CD · Docker · Teradata/Hadoop heritage

How we deploy

How one workflow becomes something your people run

The same pattern used inside banks and platforms, and inside a 1,000-person agency. Compressed because the mistakes already happened somewhere else.

  1. Embed & map Phase 1

    Sit inside your teams; map workflows, constraints, data and real AI maturity. You leave with a value-ranked use-case backlog you could put in front of the C-suite.

  2. Prove fast Phase 2

    First working proof in production-shaped conditions, with evaluation criteria agreed before we build.

  3. Harden for production Phase 3

    Verifier gates and evaluation frameworks go in before scale, along with access control, identity and governance, so none of it has to be retrofitted later.

  4. Transfer capability Phase 4

    Workshops, playbooks, reusable skills and local champions, so your own people compound the value instead of renting it.

  5. Optimise Phase 5

    Self-healing operations, with cost and latency headroom engineered before it's needed.

Built to be an easy procurement decision

Working arrangements

  • One supplier, one contract, on fixed-scope statements of work, not body-shopping.
  • Senior engineers only, with no bait-and-switch.
  • Commercial terms according to your scaling needs.
  • Built in your estate, under your access controls.
  • One point of contact, one shared standard, mutual cover across workstreams.

What this de-risks

  • Nine years as a working unit, so you are not the pilot for whether we can operate together.
  • A reference you can read, published on Anthropic's site rather than only described in interviews.
  • Consistent standards from day one, from a shared review culture and shared verifier-gate discipline.
  • Continuity: if one workstream surges, the unit rebalances; nothing stalls on a single hire.

Next step

Book 30 minutes with USQ Consulting.

Bring one workflow you actually run. In 30 minutes we will say how we would take it to production, what we would not touch, and whether it is worth commissioning at all. If the honest answer is "don't build it", that is the answer you get.

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