AI at Quod
At Quod, our engineering organisation is structured around AI — not the other way round. Our workflows, tooling, team practices, and delivery processes are all designed with AI at the centre.
People set direction, make judgements, and own outcomes. AI handles the heavy lifting in between. The result is a team that moves faster, misses less, and builds more reliably than a conventionally structured engineering organisation could.
"We are reaching the end of the software industry's Cretaceous period. Only those who adapt quickly will survive. Those who do not will leave their clients stranded on systems that no longer evolve."
Ali Pichvai, Co-Founder, Quod Financial · 2026
The Quod Code model
Our engineering is organised around what we call "Quod Code" — domain-expert subgroups that replace large cross-language Scrum teams. Each subgroup encodes decades of institutional trading knowledge into machine-readable specifications. AI generates the code; experts review and validate; automated testing runs the full suite before any commit.
The bottleneck is no longer headcount. It is the quality of the requirement.
10–20× engineering velocity
Compared to a traditional engineering organisation
100% automated testing
in the CI/CD pipeline — zero manual gates.
This is not a future ambition. Boot camps are running. Daily builds are the target. Early wins are emerging. We are executing now.
AI-assisted development
AI that writes code the Quod way!
Our engineers work inside an AI-native coding environment powered by leading AI models. Unlike a generic AI assistant, this environment is codebase-aware — it understands Quod's architecture, coding standards, and established patterns before generating a single line of code.
This is enforced through a structured rules system committed directly to our repositories. These rules define exactly how the AI must behave: which frameworks to use, which patterns are required, how components should be structured. The result is AI that writes code the Quod way — consistently, across every engineer and every team.
We maintain a shared library of reusable AI prompts for common engineering tasks — generating unit tests, scaffolding new features, running migrations — version-controlled and evolving alongside the codebase. This approach covers our full frontend stack across web and mobile.
The MCP server layer
What powers our operational AI capabilities
The real power of our AI setup lies in its connections. Our development environments are equipped with Model Context Protocol (MCP) servers, a standard that allows AI to securely read external data sources in real time.
In practice, this means an engineer can ask their AI environment to read a live ticket, understand the acceptance criteria, and generate code that directly satisfies those requirements, all without leaving the IDE. Architectural decision records, API contracts, and design specifications are equally accessible to the AI as live context.
The MCP layer is also what powers our operational AI capabilities — connecting AI to our monitoring systems and infrastructure. It is the connectivity layer that makes the rest of our AI work possible.
AI in operations
AI handles the investigative groundwork; people handle the judgement.
Our operations team is supported by AI that works alongside engineers to identify and resolve issues faster. When a problem arises, AI assists with investigation, surfaces the relevant context, and helps prepare a response — all before a human needs to spend time manually searching through logs or switching between systems.
The engineer remains in control throughout: reviewing, refining, and confirming before anything reaches a client. AI handles the investigative groundwork; people handle the judgement.
The same approach extends to infrastructure monitoring, giving our team real-time visibility across our global operations from a single point of view.
Building AI capability across the team
Working effectively with AI as an engineer
Technical tooling is only as good as the people using it. Quod runs an internal AI training programme, a structured course covering the foundations of working effectively with AI as an engineer.
"Most teams adopt AI tools and hope engineers will figure it out. We did the opposite. We structured the adoption, tracked where it was actually delivering, and fed the learnings back continuously. AI integration sits at the top of our engineering priorities.”
- Ben Ernest-Jones, Chief Product Officer
What this means for clients
Making every engineer more effective
Every improvement to how we engineer translates directly into the platform our clients run. Faster iteration cycles, more consistent code quality, and better-instrumented operations all mean a more reliable, more capable system.
| Legacy platform reality | Quod AI-engineered reality | |
|---|---|---|
| New feature delivery | 12–18 months on a backlog | Days to weeks from prompt to production |
| New asset class integration | 18–24 month programme | 4–8 weeks · same platform, no migration |
| Regulatory adaptation | 12 months after rule publication | Same quarter · automated build and test |
| Custom workflow | Bespoke project · high cost | Standard request · weeks to deliver |
| Software cost trajectory | Rising · legacy cost structures persist | Falling · AI efficiency passed to clients |
| Competitive position | Constrained by vendor capacity | Limited only by quality of requirement |
Our AI engineering investment isn't about replacing engineers with automation. It's about making every engineer more effective, and every system more observable. The institutions that move first to an AI-engineered platform gain a compounding capability advantage. Every month on a system evolving at 10–20× speed produces more differentiated functionality.
Interested in how AI features appear in the Quod platform itself?
See QuodIQ
against your own data
Proof of concept deployed against a replica of your production data. Real queries, real execution history, no synthetic data, no mock environment.