AI-Augmented Software Development
Ship production software with Agentic Engineering for regulated industries.
Not "vibe coding".
We build production software using Claude Code, Cursor, and GitHub Copilot inside an engineering practice that delivers a new way of building software where clear requirements, architecture decisions, AIOps, governance, security, and product thinking become the foundation for AI-assisted delivery - all powered by a strong heritage as one of Europe's top Python development companies.
3-5× Faster Delivery Cycles
60%+ Reduction in Rework
40% Faster Onboarding
70% Faster Test Authoring
400+ Engineers AI-trained
Built for your use case, on your stack
Claude Code
GitHub Copilot
Cursor
Codex
Half-day digs to trace what a function does.
Single-person dependency on the engineer who wrote a module five years ago.
Nobody touches the unfamiliar parts because the cost of getting it wrong is too high.
- AI-explained legacy logic on demand, no more half-day digs to understand a module
- Auto-generated documentation from the codebase, replacing the tribal knowledge tax
- Role-scoped agentic engineering rules built from the team's existing tests and conventions
- AI-assisted PR review that catches AI mistakes before they reach main
The result: 30-50% less time searching and understanding code. 40% faster onboarding for new engineers, 70% less senior engineer dependency.
You are building from zero. Every architectural decision now will shape velocity for years. The risk is shipping fast, then drowning in technical debt and rework before the product ever finds its shape.
- PRDs and ADRs as the foundation. AI produces production-ready output only when the inputs are structured. We start with the artifacts, not the prompts.
- Logic prototyping with AI before any production code. Feasibility validated in hours, not sprints. Only proven approaches reach the codebase.
- Test rules and Cursor rules built from your conventions on day one, so AI output matches your quality bar from the first PR forward.
- AI-assisted PR review prevents the common AI failure modes (hallucinated APIs, drift from project conventions) before they compound into rework.
- Senior-level throughput from a smaller, focused team.
The result: 40% faster delivery on routine work, 50% faster debugging through root cause identification, ~30% end-to-end velocity gain.
Infrastructure-as-code work is boilerplate-heavy. CI/CD pipelines need constant tuning. Senior DevOps time gets eaten by the tasks AI handles best, leaving the actually-hard problems waiting.
- Terraform module generation and review, with engineering oversight on every change
- CI/CD pipeline adjustments and environment configuration for end-to-end pipelines
- n8n automation workflows for internal tooling, runbooks, and on-call helpers
- Governance baked in: secrets and .env files never transmitted, all output reviewed
The result: Senior platform engineers stop firefighting boilerplate. The hard architectural work gets the attention it needs.
AI sits across the entire Software Delivery Lifecycle (SDLC)
Six places where AI delivers measurable gains in our active client engagements. Each one configured for your codebase, your conventions, and your engineering bar.
Discovery & PRDs
AI breaks features into structured tickets, flags edge cases before sprint planning, and helps refine the PRD. We work on real scenarios with structured inputs, not just prompts.
Architecture & ADRs
Multi-document knowledge synthesis across ADRs, requirements, and architecture docs. Dependency and impact mapping before any edit. Logic prototyping before production code.
Definition of Ready for AI
Tickets are only AI-ready when user behaviour, edge cases and acceptance criteria are explicit. Static analysis, custom lints and security gates run on every AI-generated diff before a human ever sees it.
Testing & QA
Component test rules generated from your team's existing tests. Cypress do/don't guidelines organised by seniority. RTL migration paths embedded in the rules.
DevOps
Terraform module generation. CI/CD pipeline adjustments. Environment configuration for end-to-end pipelines. Engineers retain final review on every change.
Evals & Golden Paths
We benchmark prompts and agents against a regression suite of real production scenarios. AI-assisted code review that catches AI-generated mistakes and suggests cleaner alternatives. Auto-generated docs from code.
Measurable impact from client projects
We see up to 55% gains in task-level execution speed. End-to-end delivery is closer to ~30% once planning, review, and cross-team coordination are factored in. We tell clients the realistic number.
Beyond productivity, a maturity shift
What our clients observed across six bootcamps and dozens of AI-assisted client engagements. The most important outcome is not the speed gain.
AI shifts the developer's centre of gravity from coding to defining. ADRs and PRDs become the unit of work.
User behaviour, edge cases and end-to-end flows now sit at the centre of delivery because AI depends on them.
Engineers engage more deeply with business context, becoming consultative - closer to stakeholders, further from ticket factories.
Non-developers; Scrum, product, and analysts onboard quickly and contribute effectively when code writing is not the limiting step.
Trusted by engineering leaders shipping AI-augmented work today
Legacy Codebase Modernization and AI Bootcamp for Development Team
STX Next's AI bootcamp transitioned EV’s engineering team from utility prompting to a high-impact AI-augmented development workflow using Cursor and GitHub Copilot. By mastering MCP servers and shifting context windows, developers adopted a "plan-first" approach, utilizing AI to generate structured ADRs, PRDs, and automated testing guidelines before execution.
This methodological shift triggered a massive productivity spike, with frequent AI usage in test development jumping to 87.5%. Consequently, the team now tackles large-scale problems with much greater precision, ensuring high code quality and seamless knowledge sharing across the entire software lifecycle.
United Kingdom
A US HealthTech platform: navigating a massive codebase
Large-scale platform with multiple interconnected ADRs and dense requirements documentation.
Day-to-day navigation would have been cumbersome without AI tooling. Engineers prompt by behavior or purpose to locate functions across a file structure too large to navigate manually.
AI synthesises answers across multiple folders without manual cross-referencing. Logic prototyping happens before any production code.
United States
A US EnergyTech: AI embedded across the entire SDLC
A mixed team needed to ship a production-grade platform with the maturity of a much larger engineering org. AI breaks down feature ideas into structured tickets, assists architecture decisions, generates scaffolding, and flags edge cases before sprint planning.
Discovery, architecture, coding, testing, DevOps, docs, and PRs all run through the same workflow. The compounding effect is that even routine work ships at a higher product bar.
United States
A reliable partner for engineering leaders
Even though we believe that our work speaks for itself, we are always grateful for words of appreciation from our clients.
Client
testimonial
STX Next's development efforts have been so successful, and the DevOps team is incredibly proactive and communicative.
BuildFax (Verisk)
Client
testimonial
They are able to translate complex challenges into clear, executable plans.
STX Next combines deep expertise and reliability with a strong ability to apply artificial intelligence in a practical way to improve business processes.
Head of Bancassurance & Affinity Sales,
UNIQA
Client
testimonial
We gave them a very high-level brief and left the rest in their hands. The app works perfectly, and they came in on time, on budget, with no outstanding issues.
Hartford Consulting
We Adapt to Your Environment
Claude Code
GitHub Copilot
Cursor
Codex
Agentic Engineering
Cursor and Claude Code for primary development. GitHub Copilot for inline suggestions. n8n for internal automation. Tool selection adapts to your security and procurement constraints.
Workflow Integration
We integrate with your existing tooling: Jira, Linear, GitHub, GitLab, Bitbucket. We work inside your conventions, not around them. We adapt to you, not the other way around.
Governance & Security
No proprietary data shared externally. Secrets and .env files never transmitted. On-premise or VPC-isolated tooling for sovereignty-conscious clients. ISO/IEC 27001:2022 across the company.
Ready to bring AI inside your engineering practice?
We will look at where your team is right now and where the next 90 days of AI-augmented delivery would matter most.
FAQ
How is this different from "vibe coding" or letting developers loose with Cursor / Claude Code / GitHub Copilot?
The frameworks, expertise, regulatory knowledge, and best practices for security and governance. Vibe coding produces unpredictable output because there is no governance layer. Our engagement starts with the structured artifacts (PRDs, ADRs, role-scoped rules, AI-assisted PR review) that turn AI from a guesser into a reproducible contributor. Most teams that try agentic engineering tools on their own get a 2-week bump in velocity, then a hard slowdown when AI-generated code starts breaking things. The framework prevents that.
What does a typical engagement look like?
A discovery sprint maps your codebase, existing artifacts, and the highest-impact areas for AI. From there we either embed alongside your team or run the workflow on a defined scope (a feature, a migration, a modernization) with knowledge transfer built in. The bootcamp can be a complement or a standalone, depending on whether you want us to do the work, train your team to do it, or both.
What about IP, data security, and proprietary code?
Strict governance from day one. No proprietary data shared externally outside the agreed AI tools and their contracted retention policies. Secrets, .env files, and credentials are never transmitted. On-premise or VPC-isolated tooling is the default for clients with sovereignty requirements. ISO/IEC 27001:2022 certified across the company.
Will my team's existing seniors lose relevance?
The opposite. AI compresses implementation work, which expands the time seniors spend on architecture, decision-making, and mentoring. Senior engineers move from being a bottleneck (juniors needing them for context) to being a multiplier. The 70% reduction in senior dependency we measured is reduction in blocking dependency, not reduction in senior value.
What if our requirements are messy and our docs are out of date?
That is the most common starting point, and it is where the engagement compounds fastest. AI forces structured inputs, so part of the early work is bringing your PRDs, ADRs, and architecture docs into a state where AI can use them. That cleanup pays dividends regardless of AI, because it is the same work that improves any team's delivery quality.
How do you measure success?
Concrete metrics agreed upfront. Typical KPIs: time-to-first-PR for new joiners, cycle time per feature, defect escape rate, test coverage trajectory, plus qualitative measures like senior dependency and onboarding satisfaction. We share progress against these in regular cadences and revisit targets every quarter.
How do you engage with our existing engineering team?
The engagement is structured around the framework, not the headcount. We typically run a defined scope (a feature, a modernization, a platform initiative) using the AI-augmented delivery practice, with your engineers participating in delivery and inheriting the workflows as we go. By the end of the engagement, the PRD/ADR practice, the rules, the test rule generators, and the AI-assisted PR review are all owned by your team. The bootcamp can be run alongside, in front of, or after the delivery work, depending on what you need first.
