AI & Software Development Studio
We design and develop custom software, AI-powered solutions, and digital experiences that help businesses solve complex challenges, work smarter, and move forward.
01What we do
Four disciplines, one engineering standard. Most projects draw on more than one — an AI feature still needs a solid backend underneath it.
Explore our services02Selected work
Production systems and independent builds from our founder's engineering background — each labelled clearly, so you always know whose work you're looking at and where it was built.
Showing 3 projects
4h → 10m
Research workflow automated
A multi-agent LangGraph platform that plans, retrieves and generates structured market analysis reports for enterprise clients.
4 services
One identity, independent deploys
Microservices backend and AI-coaching integration for an athlete training platform — four services, independently deployed to AWS EKS.
3-signal
Confidence scoring
A self-grading document Q&A agent for compliance-critical workflows, where a confident-sounding wrong answer is worse than no answer.
6 case studies, from multi-agent research systems to microservices platforms and independent AI builds.
Explore All Projects03How we build
Four stages, each ending in something you can review — so decisions are made with you, not handed to you.
01
Understand your business, challenges, objectives, and technical requirements.
A shared understanding of the problem and a prioritised scope.
02
Define the solution architecture, experience, and implementation plan.
An architecture and plan you can review before code is written.
03
Engineer the software using technologies appropriate to the project.
Working software, delivered in reviewable increments.
04
Test, deploy, document, and support the solution.
A deployed, documented system — and support while it settles in.
04Technical expertise
We choose tools for the problem, not the trend. Everything listed here has been used in shipped work or in the projects on this site — nothing more.
05Why Code Velocity
We approach technology through the problems it needs to solve.
In practice
The AI Market Research Platform was built around one measurable workflow — and took it from four hours to under ten minutes.
See the work for Business-First ThinkingWe build practical solutions around real requirements.
In practice
A production connection-pool exhaustion incident was solved by separating API, worker and scheduler processes — not by buying more hardware.
See the work for Purposeful EngineeringTransparent communication throughout planning and development.
In practice
You see the architecture and its trade-offs before code is written — the same kind of design decisions documented in every case study on this site.
See the work for Clear CollaborationSolutions designed with maintainability and future expansion in mind.
In practice
Single-responsibility agents, independently deployed services, and access control designed in from day one — so systems can grow without rewrites.
See the work for Built for Growth06About Code Velocity
Code Velocity is a new, deliberately focused studio for AI and software development. Our mission is simple: help ambitious businesses turn complex problems into dependable software — and put AI to work where it genuinely helps.
What we bring is a technical foundation proven in production: our founder's years of building backend platforms, multi-agent AI systems and RAG pipelines, plus independent work that shows how we think. We're early, and we don't pretend otherwise.
Figures describe the founder's professional background, not Code Velocity engagements.
07Frequently asked
Custom AI agents, retrieval-augmented (RAG) assistants grounded in your own data, AI chatbots and automation, and the full-stack software — backends, APIs, dashboards and SaaS platforms — those systems run on. Most projects combine more than one of these, since an AI feature still needs a solid backend underneath it.
It depends on scope — a focused RAG assistant is a different project from a multi-agent platform with billing and multi-tenant access control. We scope cost and timeline together during the Discover stage, before any commitment, based on your actual requirements rather than a generic package price.
RAG grounds an AI system's answers in your own documents or data instead of the model's general training, so it can cite real information rather than guess. It matters most anywhere a confident-sounding wrong answer is worse than no answer — compliance, customer support, internal knowledge bases — which is why several of our case studies use it with added confidence scoring.
A focused assistant or automation can move from Discover to a working first version in weeks, not months, when the scope is kept tight. Larger platforms with multiple services, billing or multi-tenant access take longer. We sequence milestones so you see and can test working software early rather than waiting for one big reveal.
Both — the same engineering standard applies regardless of size. What changes is scope: a small business might need one well-built AI feature or automation, while a larger team might need a full platform with multiple services. We'd rather scope something you'll actually use than oversell a bigger build.
Yes — we work remotely with clients across the US, Europe and elsewhere, and our process (Discover, Design, Develop, Deliver) is built around async, written updates and scheduled calls rather than requiring co-location.
Whether you're developing a new product, improving an existing system, or exploring AI automation, we'd love to understand what you're building.