Solutions Architect UK.
Production systems, not pitch decks.
Temisan Gerrard is a London-based Solutions Architect who helps UK businesses move AI projects from working prototype to reliable production system. Seven products shipped across blockchain infrastructure, digital assets, and AI systems.
The Role
What Does a Solutions Architect in the UK Do?
A Solutions Architect UK does far more than help you pick a large language model and wire up an API endpoint. The gap between "we got GPT working in a notebook" and "we have a reliable AI system serving thousands of users at 3am on a Saturday" is enormous — and most teams discover this gap only after their demo fails in production.
A qualified Solutions Architect in London works across the full technical stack: from the core infrastructure that keeps your system running, through state management that gives your AI memory and context, to the execution layer where the AI actually performs tasks. But that's only three of five layers. The remaining two — ops and reliability — are where production systems live or die.
"Your AI works in a notebook. Your AI works at 3am on a Saturday with 500 concurrent users. Those are different systems."
Autonomous Agents
Building AI agents that take real actions without human intervention — buying, selling, scheduling, monitoring — with cost controls and safety boundaries so they don't drift. This isn't prompt engineering; it's systems engineering with AI at the core.
Production Architecture
Designing systems that handle retries, edge cases, and concurrent users without falling over. Your AI demo won't survive contact with real traffic. A machine learning consultant UK ensures the architecture around the model is as robust as the model itself.
Safety & Governance
Putting boundaries around AI outputs so they stay predictable. Semantic firewalls, tool registries, and deterministic checks that catch problems before users do. For UK businesses handling customer data, this isn't optional — it's the difference between a product and a liability.
Data & Retrieval
Connecting AI to your actual data — beyond basic vector search, into retrieval systems that return the right context at the right time, reliably. Your AI is only as good as the data it can access, and most RAG implementations break under production load.
The Framework
The AI
Operating
Stack
Most AI projects stop at "the model works." That's layer three of five. Here's what the other four look like — and why skipping them is why your prototype breaks when real users show up.
The top layer of the stack ensures your AI system recovers without human intervention. For UK businesses running customer-facing products, reliability means the difference between a 2am page you can ignore and a 2am page that costs you revenue. This layer covers automated incident response, circuit breakers, fallback behaviours, and self-healing mechanisms. When your AI agent encounters an unexpected state at 3am on a Sunday, the reliability layer ensures it degrades gracefully instead of crashing spectacularly.
You need to see what your AI is doing at all times. Structured logging, alerting, and admin dashboards give your non-technical team visibility into AI behaviour without calling a developer. Observability is not a nice-to-have — it's the difference between finding a bug in minutes and finding it after your users have already left. This is especially critical for UK startups operating under tight resource constraints where every hour of downtime matters.
The layer most teams build — and the only layer most teams build. This is where the AI model runs, processes inputs, and produces outputs. It includes prompt management, model routing, tool execution, and skill orchestration. Building an AI system that works in controlled conditions is achievable. Building one that produces consistent, high-quality outputs across thousands of interactions requires the other four layers to hold.
AI systems need memory. Not just session memory — persistent state that survives restarts, tracks conversation history, and maintains context across multiple interactions. For UK businesses deploying AI customer service or AI-driven workflows, state management is what turns a chatbot into a system that actually knows what's happening. Without it, every interaction starts from zero, and your users notice immediately.
The foundation everything else runs on. Infrastructure provisioning, deployment pipelines, environment management, database connectivity, authentication, and the hundred other things that keep a system running. The fanciest AI model in the world is useless if the server it runs on can't handle traffic. This layer ensures it stays running.
Engagements
Architecture & Engineering
Every engagement is designed to move your AI project closer to production. Whether you need a full architecture blueprint, a production-ready build, or an independent audit of an existing system, structured engagements cover the complete journey from architecture through production deployment.
AI Architecture Sprint
In 5 working days, you get a clear system diagram, a build-vs-buy analysis for each component, and a risk-flagged roadmap your team can actually execute against. Ideal for UK startups that have a working prototype and need to understand what production looks like before committing to a full build.
Production Build Sprint
A working system deployed to your environment, with documentation and runbooks for the three most likely failure modes — plus 30 days of async support while it beds in. This is the full build: architecture, implementation, testing, deployment, and handover, delivered by a London-based solutions architect.
AI Ops Layer
Structured logging, alerting, and an admin dashboard so your non-technical team can monitor the AI and intervene when needed — without calling a developer at 2am. Perfect for UK businesses that already have an AI system running but lack the observability to manage it confidently.
System Audit
An independent assessment across all five layers of the AI Operating Stack. You get a scored report (1–5 per layer) with the most impactful fixes prioritised first. Ideal for UK businesses that have invested in AI but aren't seeing the reliability or performance they expected.
Work
Case Studies
Every project below is live, has real users, and processes real transactions. These are production systems — verifiable right now.
Autonomous Arena
A multiplayer betting game running live on a blockchain. Players stake real money across five game modes. AI bots keep it running 24/7 without human intervention. 444 commits of production hardening. Not a demo — a system that handles real money autonomously.
EchoCart
Tell it what you want — by text or voice — and it finds the best deal and buys it for you. An AI shopping agent that actually completes purchases, not just recommends them. Users set a budget, fund a wallet, and EchoCart handles the rest from search to checkout.
Peppera
A meal engine that builds from what you already have. Tell it what's in your kitchen, and it generates complete meals with nutrition tracking — turning a handful of ingredients into weeks of variety. Knowledge provenance ensures every recipe suggestion is traceable to its source.
Mira
An AI agent that handles growth operations autonomously — writing content, reaching out to investors, tracking competitors, submitting hackathon entries. It runs continuously without human triggers and has its own incident response process when something goes wrong.
Sectors
Industries Served
Fintech
AI is transforming how financial products are built, monitored, and scaled — from automated compliance monitoring to real-time fraud detection. The challenge isn't building the model; it's building the system around it that handles peak trading loads and keeps transactions flowing. Building real-money systems on Base mainnet gave me direct experience with this.
Blockchain & Web3
The intersection of AI and blockchain is where some of the most interesting production systems are being built. Autonomous agents managing wallets, executing smart contracts, and running decentralised applications 24/7. With hands-on blockchain experience — including the Autonomous Arena running live on Base mainnet — I understand the unique challenges of building AI systems that interact with on-chain data, manage real crypto assets, and maintain reliability in a trustless environment.
E-Commerce
AI is reshaping e-commerce from recommendation engines to fully autonomous shopping agents. EchoCart — an AI shopping agent that completes purchases end-to-end — demonstrates what's possible: intelligent product discovery, personalised pricing, and AI that acts on behalf of the customer rather than just suggesting.
SaaS
SaaS companies in the UK are integrating AI into their products faster than ever — but most are bolting AI features onto architectures that weren't designed for them. The right approach helps SaaS teams embed AI natively: from multi-tenant model serving and cost management, to AI-powered onboarding flows and predictive churn analysis. The goal isn't an AI feature — it's an AI-native product that gets smarter with every interaction while keeping infrastructure costs predictable.
The Case
Why Hire a Solutions Architect?
Most UK businesses don't need a full-time AI team — they need a system that works. Hiring a specialist is often the faster, cheaper, and lower-risk path to production.
You're paying for experience, not experimentation. I've shipped seven production systems in 90 days and hit the edge cases your team will discover three months from now — the model timeout under concurrent load, the state corruption after a deployment, the silent failures in your retrieval pipeline.
Your in-house team has other priorities. If you're a UK startup or scale-up, your developers are already stretched thin building core product features. Adding "AI infrastructure" to their backlog means either shipping core features late or shipping AI that breaks. A specialist engagement gives you a dedicated architect who builds the AI layer while your team stays focused on what they do best.
The five-layer stack isn't obvious until you need it. Most teams build layer three (AI execution) and discover the other four layers only when their prototype fails in production. By then, retrofitting reliability, ops, state management, and core infrastructure into an existing system is far more expensive than building them in from the start. A specialist brings the full stack on day one.
Speed to production matters. In the current market, the difference between "we have an AI demo" and "we have a production AI system" is often the difference between securing your next funding round and not. Someone who has done this before can compress months of trial and error into weeks of focused delivery.
FAQ
Frequently Asked Questions
What does a solutions architect in the UK do?
A solutions architect helps businesses move AI projects from prototype to production. This goes beyond selecting models and writing prompts — it encompasses the full system architecture: designing autonomous agent systems, building production-grade infrastructure, implementing safety and governance frameworks, and creating reliable data and retrieval pipelines. Temisan Gerrard has shipped 7 live products across fintech, blockchain, e-commerce, and AI tooling.
How much does AI consulting cost in the UK?
AI consulting costs in the UK vary by the type and depth of engagement. Temisan Gerrard offers four structured engagements: an AI Architecture Sprint at £8,500 for a complete system blueprint delivered in 5 working days; a System Audit at £4,500 for an independent assessment of your existing AI stack completed in 3–5 days; an AI Ops Layer at £12,000 for structured logging, alerting, and monitoring dashboards delivered in 2–3 weeks; and a Production Build Sprint starting from £18,000 for a full working system deployed to your environment in 4–8 weeks. All engagements include documentation, runbooks, and post-delivery support. Compared to the cost of hiring a full-time AI engineer in London (£80k–£150k+ per year), a specialist engagement delivers focused expertise at a fraction of the annual cost.
Why hire a specialist instead of building in-house?
An experienced solutions architect has already encountered and solved the problems your team is about to face. The 5-layer AI Operating Stack covers reliability, ops, AI execution, state management, and core systems — most teams only build layer three and wonder why their prototype breaks under real load. Hiring a specialist gives you access to production-proven patterns without the 6–12 months of trial and error. It also allows your existing developers to stay focused on core product work while the AI infrastructure is built by someone who has done it before.
What industries do you work with?
Temisan Gerrard's live projects span fintech and blockchain (autonomous betting on Base mainnet, tokenised fund architecture), e-commerce (AI shopping agent completing end-to-end purchases), AI tooling (open-source agent platform with 90+ tools), and growth operations (autonomous GTM agents). Each project is live and verifiable.
How long does it take to get an AI system to production?
Timelines are shorter than building from scratch. You can have a full architecture plan in 5 days (Architecture Sprint), an AI ops layer in 2–3 weeks, or a complete production-ready system in 4–8 weeks (Production Build Sprint). Temisan Gerrard shipped 7 production systems in 90 days. The key is having the five-layer stack as a proven template rather than discovering each layer through trial and error.
About
About Temisan
Gerrard
I'm a London-based solutions architect specialising in moving AI projects from prototype to production. Over the last 90 days, I've shipped seven live products across blockchain infrastructure, digital assets, and AI systems. The Autonomous Arena runs live on Base mainnet. EchoCart completes real purchases. Mira runs growth operations autonomously.
My approach is built around the AI Operating Stack — a five-layer framework that covers everything from core infrastructure to automated reliability. Most teams build layer three (the AI itself) and discover the other four layers only when their system breaks in production. I bring all five layers from day one.
I also built and maintain Hermes Agent — an open-source AI agent with 90+ tools and 5 platform adapters that runs my entire operation. It deploys code, publishes articles, monitors systems, and manages workflows autonomously. Hermes is the proof that the architecture I recommend to clients actually works at scale, in production, every day.
Before consulting, I worked across fintech, blockchain, and AI product development in London. I understand the pressures UK startups face — limited budgets, tight timelines, and the need to demonstrate traction to investors. Every engagement I take is designed to deliver measurable progress toward a production system, not just a strategy document.
Available for Q3 2026 engagements. Based in London, working with clients across the UK and Europe.
Ready to move your AI project to production?
Book a free 30-minute call to discuss your AI project. No pitch deck required — just tell me what you're building and I'll tell you what it takes to get it live.
Available for Q2 2026 consulting engagements.