Services

From prototype to production

Most generative-AI pilots never reach production — and the bottleneck is not the model. It is architecture, security, data quality, cost, evaluation and compliance. Those are exactly the areas we work in every day. We join at any stage — from an idea, through a stalled pilot, to a live feature that does not scale safely — and take the work through to production.

Who it is for

• SME and mid-market companies with a working AI pilot or prototype
• Teams whose AI initiative has stalled before production
• Product owners adding AI features to an existing solution
• Teams that shipped AI quickly and now need it on a durable foundation

Scope

AI readiness review

A fixed-scope review (1–3 weeks) covering architecture, security, data, evaluation, cost and EU AI Act exposure. It ends in a prioritised remediation roadmap — what to fix first, at what effort and at what risk — run with the same method as our architecture reviews.

AI platform (with deep AWS expertise)

The foundation on which AI scales safely: identity, access policies and guardrails, knowledge bases (RAG), observability and evaluation, cost control (FinOps) and infrastructure as code. We deliver on Amazon Bedrock and AgentCore among others — pragmatically, without lock-in to a single vendor.

Delivery acceleration

Rolling out AI-assisted coding with the guardrails to make it safe: architecture boundaries, review gates, a test and evaluation strategy and documented decisions (ADRs). The goal is speed that does not turn into technical debt.

AI in your product

From AI feature strategy and prioritisation, through RAG and agent design and a modular architecture, to a per-unit cost model and production rollout — with a quality evaluation you can defend to a customer.

Why Coderon

The bottleneck between pilot and production is engineering, not the model — and that is exactly where we work every day. We bring architecture, security and cost control together in one team rather than spreading them across vendors.

How we work

We start from business value and constraints, not the model. We work close to your team, measure the effect, and leave them self-sufficient when we are done — with no dependency on us.

Architecture that keeps up with the product

Good architecture is not a diagram — it is a set of deliberate decisions that make the team’s daily work easier and lower the cost of change. We help design systems that are testable, scalable and safe to change, rather than worked around with every new feature.

Who it is for

• Teams whose delivery slows down as the system grows
• Companies planning a rebuild or breaking up a monolith
• Product owners facing a larger technical investment decision
• Teams that need an independent architecture assessment before scaling

Where we help

• Distributed system design and service decomposition
• Domain-Driven Design and domain modelling
• Integration patterns, data consistency and asynchronous messaging
• Architecture reviews and documented technical decisions (ADRs)
• Test strategy, observability and reliability

Architecture review

The most common entry point: a fixed-scope review (1–3 weeks) that assesses the current architecture for risk, cost of change and bottlenecks. It ends in a prioritised roadmap — what to fix and in what order, with the reasoning behind every recommendation.

Cloud & AWS

We design and deliver cloud architectures — serverless and event-driven, aligned with good practice (including the AWS Well-Architected Framework). In practice that means compute and integration design, infrastructure as code (Terraform), CI/CD pipelines and cost optimisation. Architecture comes first, though — we fit the solution and the vendor to the problem, not the other way round.

Why Coderon

You get an architecture matched to the real constraints of your team and business — documented, justified through its trade-offs and ready to implement, not another diagram for the drawer.

How we work

We work close to the team, on real code and real constraints. We record and justify every significant decision, and the team is left self-sufficient when we are done.

Compliance built into the system

The EU AI Act is coming into force — high-risk system and transparency obligations from August 2026, synthetic-content labelling from December 2026, penalties up to €35M or 7% of turnover. Compliance described in a policy alone will not survive an audit if the system does not enforce it. We help you meet these requirements in the architecture, not just in a document.

Who it is for

• EU companies deploying or shipping AI systems
• Especially high-risk use cases (Annex III)
• Teams that need audit-ready compliance
• Boards accountable for AI oversight and risk

Scope

Inventory and risk classification

An inventory of the AI systems in the organisation and their roles, followed by classifying each use case by the risk level the regulation assigns to it.

Gap assessment and technical documentation

A comparison of the current state against the requirements and a list of the missing pieces — technical and organisational — together with technical documentation to the extent the provider or deployer is responsible for.

Data governance, transparency and content labelling

Data quality and provenance rules, user-facing disclosure obligations and synthetic-content labelling — designed as part of the solution, not an afterthought.

Monitoring built into the architecture

Logging, operational oversight and response mechanisms embedded in the architecture, so compliance can be demonstrated with evidence rather than a declaration.

Scope of responsibility

We work in architecture and governance — formal legal opinions are provided by a partner law firm. We pair this with the coaching track for board-level accountability and oversight.

Why Coderon

We are EU-based and architecture-led — compliance that is built into the system, not just advised in a deck. Few SME-focused providers combine knowing the regulation with being able to implement it in code.

We teach what we ship

Our training is delivered by engineers who design and build cloud systems every day. Instead of slideware — real cases, exercises and architectural decisions discussed on concrete examples, including the hard trade-offs that never make it into documentation.

Who it is for

• Engineering teams building depth in architecture, cloud or AI
• Technical leaders responsible for team standards and decisions
• Executives who want to make AI decisions deliberately

Format

• Remote or on-site workshops, tailored to your team’s level
• A curriculum built around the technologies and problems you actually work with
• Exercises based on real scenarios, not textbook examples
• Materials and tasks the team keeps and reuses after the workshop

Tracks

The catalogue spans three tracks: architecture & cloud, AI engineering and leadership. Every workshop can be adjusted in scope and level to the team. See the training section for the individual programmes.

Why Coderon

We do not resell second-hand theory — we teach what we design and run in production ourselves. After the training the team has not only the knowledge but a way of making decisions that stays with it.