Forward deployed engineers who build Claude-native agent systems into your infrastructure, then stay and run them. Built by a former CTO who shipped AI in regulated financial services. We write code, not slide decks.
The AI landscape is moving faster than any technology shift in the last decade. Every vendor is pitching AI capabilities, your board is asking for an AI strategy, and your team is experimenting with ChatGPT in ways that may or may not be secure. You need engineers who have actually shipped production AI — not consultants who repackage API documentation as expertise.
Ricky Thomas was CTO at IG Group, building and scaling technology platforms in regulated financial services. Our forward deployed engineers have built AI pricing engines processing millions of transactions, customer-facing agents handling live queries, and data platforms that connect fragmented systems into coherent intelligence layers. We build on Claude (Anthropic) by default — it consistently outperforms on enterprise agent use cases — with abstraction layers for model portability.
We work in your repos, your CI/CD pipeline, your deployment processes. Every system we build, your team can see, review, and learn from. We pair with your engineers throughout — not as a knowledge transfer exercise at the end, but as the way we work. Architecture Decision Records document every major choice. Code reviews go both directions.
Production-grade agents with guardrails, evaluation frameworks, and monitoring. Built on Claude with abstraction layers for model portability. Deployed in your security perimeter, not ours.
Explore AI agents →30-50% cloud spend reduction through right-sizing, reserved capacity, and architecture cleanup. We've done this across AWS, GCP, and hybrid environments. Payback in weeks.
Explore cloud costs →Unify fragmented data sources into a coherent intelligence layer. Connect CRM, analytics, transaction data, and behavioural signals into a platform your AI agents can actually use.
Explore CDPs →Category-level elasticity modelling across your full product catalogue. Our engine priced 37,800 products processing 3.6M transactions — delivering +77% revenue uplift by week five.
Explore pricing →Real-time recommendation and personalisation across web, email, and app. In-house systems that beat third-party solutions on both latency and conversion.
Explore personalisation →Practical framework for which AI capabilities to build in-house and which to buy. Based on competitive differentiation, maintenance cost, and your team's current capability.
Get started →Discovery call. We understand your stack, your pain points, your team's capacity. No month-long assessment phase. We identify the quick wins — report automation, joining up disconnected systems, dashboards your team actually needs — and we start building.
Results in two weeks. Our engineers work in your repos, your CI/CD pipeline, your deployment processes. ADRs document every major choice. Code reviews are bidirectional. We don't set up a separate environment and hand things over — we build inside your house, fast.
We stay and operate. Monitoring, optimization, incident response — that's on us. Dedicated Slack Connect channel, on hand 24/7. As we roll out AI across teams, we help your people understand the benefits and build confidence. Your team learns by working alongside us. The agents get smarter. Your margins keep improving.
AI Models: Claude (Anthropic) as our default for agent systems — best-in-class reasoning, instruction following, and safety characteristics. Model-agnostic architecture with abstraction layers for portability. We've shipped on GPT-4, Gemini, and open-source models where the use case demands it.
Agent Architecture: Production guardrails including input validation, output filtering, PII detection, and audit logging. Evaluation frameworks that test agent behaviour before and after deployment. 15-minute monitoring cycles across all live systems.
Infrastructure: We deploy within your security perimeter. Data never leaves your environment. For regulated industries: role-based access, data classification enforcement, compliance-ready audit trails. Built by engineers who shipped AI systems in FCA-regulated financial services.
We've built production systems on Claude, GPT-4, Gemini, and open-source models. Claude consistently outperforms on tasks requiring nuanced reasoning, instruction following, and structured output — which covers most enterprise AI agent use cases. That said, we design with abstraction layers so you can swap models as the landscape evolves. The right model depends on the use case, latency requirements, and cost constraints.
Every AI system includes guardrails by design — input validation, output filtering, PII detection, and audit logging. We deploy within your infrastructure and security perimeter, not in our cloud. Data never leaves your environment. For regulated industries, we add role-based access, data classification enforcement, and compliance-ready audit trails.
Depends on whether AI is a competitive differentiator or a commodity capability. Commodity use cases (chatbots, document processing, basic automation) — buy or use hosted APIs. Differentiating use cases (pricing engines, custom agents, proprietary analytics) — build, because the model's understanding of your specific data IS the competitive advantage. We help you make this call for each use case.
We design for integration from day one. Most deployments connect via REST APIs, webhooks, or message queues. We've integrated with Shopify, Salesforce, SAP, custom ERPs, and legacy systems with no API at all. Typical timeline is 2-3 weeks per system, including testing and validation. Everything is documented.
We do. That's the forward deployed model. We build production AI and then stay to run it. Your team has full visibility and we pair with them throughout, but ongoing monitoring, optimization, and incident response is on us. Everything we build is yours — code, models, infrastructure. We stay because we keep delivering.
Start with a technical assessment — we'll review your stack, identify the highest-impact AI opportunities, and map a realistic deployment plan.