(opens in a new tab)OpenAI Select Partner
Union Square Consulting is an OpenAI Select Partner in the OpenAI Partner Network.
OpenAI Partner Network (opens in a new tab)Client-side through production
Get AI into production. Without a consultancy programme.
Three senior engineers working in the systems you already have: adoption and agent architecture, data and context, and the infrastructure beneath them.
Production AI is built inside your estate and carried through go-live. The result is designed for your team to run without a permanent consultancy retainer.
- Senior-only unit
- Vendor-independent
- Your people keep the keys
- Stay after go-live
- OpenAI Select Partner
- Published by Anthropic
Shipped inside
- EIB
Credentials
OpenAI partner. Anthropic case study.
OpenAI has named us a Select Partner. Anthropic has published our Brainlabs work. We choose models on technical, operational and commercial fit.
(opens in a new tab)Third-party case
Published on Anthropic's site
Anthropic published the Brainlabs case study, including the four-week rollout and 91% adoption in North America.
The published Anthropic customer story (opens in a new tab)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
Production AI needs more than a model.
It needs adoption, a data platform that can carry the work, and infrastructure that will keep running. We cover all three.
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
| Capability | TM | AA | GM |
|---|---|---|---|
| Custom harness delivery | Core strength | Core strength | Core strength |
| LLM & agent systems | Core strength | Core strength | Core strength |
| RAG, memory & context engineering | Core strength | Core strength | Core strength |
| AI evaluation & verification | Core strength | Core strength | Core strength |
| Prompt engineering & skills authoring | Core strength | Working capability | Core strength |
| Claude Code & platform governance | Core strength | Working capability | Working capability |
| Data platforms & warehousing | Working capability | Core strength | Core strength |
| CI/CD, cloud & infrastructure | Working capability | Core strength | Core strength |
| React / front-end delivery | Core strength | Core strength | Core strength |
| C-suite & stakeholder management | Core strength | Core strength | Core strength |
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
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
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
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.
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.
Prove fast Phase 2
First working proof in production-shaped conditions, with evaluation criteria agreed before we build.
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.
Transfer capability Phase 4
Workshops, playbooks, reusable skills and local champions, so your own people compound the value instead of renting it.
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.
- Named an OpenAI Select Partner, so the OpenAI relationship is a credential your procurement team can check, not a claim we made on a slide.
- 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.