AI & Automation
Put AI to work.
Build AI-powered features, intelligent workflows, agents and automation that improve products and business operations.
AI is easy to demo and hard to ship.
A prototype that works once is not a feature. Getting value out of a model means deciding where it belongs, what happens when it is wrong, and how you know whether it is working.
Sound familiar?
- A prototype impressed everyone, then met real data and has not moved since.
- Your team spends hours a week reading, sorting or copying information between systems.
- You are being asked what your AI strategy is and want a real answer.
- There is no way to tell whether output quality is getting better or worse.
- Nobody has defined what happens when the model is confidently wrong.
- You have ideas for automation but no one to judge which are realistic.
What we do
How the work runs
Find the right use case
Identify where a model genuinely beats the current approach — and where conventional software is simply the better answer.
Build the feature
Engineer it into the product properly: prompts and context as versioned code, not strings pasted into a file.
Automate the workflow
Remove the manual steps between systems, with a human in the loop where the decision warrants one.
Evaluate and guard
Measure output quality, define fallbacks, and set limits on what the system is allowed to do on its own.
What you get
What you end up with.
Deliverables, not promises. Every one of these is something you own and can point at when the work is done.
A feature in production, not a demo
Integrated into the product properly, with prompts and context versioned in the codebase like any other code.
Hours returned to the team
The manual steps between systems removed, with a person kept in the loop wherever the decision warrants one.
A way to measure quality
An evaluation set and a harness, so changes to prompts or models are judged on evidence rather than impressions.
Defined behaviour when it is wrong
Fallbacks, limits and guardrails, so a confident wrong answer is contained instead of acted on.
Typical use cases
- Search, extraction and classification over your own documents and data
- Drafting and summarisation inside an existing product
- Automating repetitive operational steps between systems
- Scoped agents that carry out defined tasks with guardrails
Engineering capabilities
- LLM integration and model selection
- Retrieval over your own data
- Prompt and context management in version control
- Evaluation harnesses for output quality
- Guardrails, fallbacks and human-in-the-loop review
- Workflow and queue-based automation
- Cost, latency and rate-limit management
- Monitoring of model behaviour in production
Why Recode
Why bring this to us.
- We are engineers first. Model choice is a design decision, not the product.
- We will tell you when conventional software solves it faster, cheaper and more reliably.
- We manage cost, latency and rate limits as first-class constraints, because they decide whether a feature is viable at scale.
How we work
From problem to production.
- 01
Discover
Understand the business, users, requirements and problem.
- 02
Design
Define the product experience and technical direction.
- 03
Build
Engineer, test and iterate.
- 04
Launch
Deploy, integrate and get the product into production.
- 05
Evolve
Monitor, maintain and continuously improve.
Ways to work together
How to start without committing to everything.
Most clients begin with a discovery sprint: a fixed fee, a few weeks, and a plan you own whether or not we build it.
Discovery sprint
Find out what it takes before committing to build it.
A short, paid engagement that turns an idea or a problem into something you can make a decision on. You keep everything we produce, whether or not you build with us.
- Scoped requirements and a defined first release
- Technical approach and architecture
- Delivery plan with phases and a cost range
- The risks worth knowing about before you spend
Best for
New products, or a build big enough that guessing is expensive.
Product build
A defined outcome, delivered end to end.
We design, engineer, test and launch the product. Work is phased so you see something real early and keep seeing it, instead of waiting months for one reveal.
- Product design and engineering
- Working software in your hands every phase
- Deployment, monitoring and handover
- Documentation your team can actually use
Best for
Getting a product, platform or internal system into production.
Embedded team
Senior engineering capacity that stays.
We work as part of your team — your board, your standups, your priorities — with the scope set by the roadmap rather than a fixed statement of work.
- A named team, not rotating contractors
- Your tooling, your process, your repository
- Capacity that flexes as priorities change
- Knowledge that stays documented, not siloed
Best for
Live products with more roadmap than delivery capacity.
Rescue and modernisation
Take on software that has stalled.
We start with an honest assessment of what exists — what is salvageable, what is not, and what it would cost either way. Then we stabilise it and make it changeable again.
- Codebase and infrastructure assessment
- Stabilisation of the most urgent failures
- An incremental path off what cannot be kept
- A system your team can safely change again
Best for
Inherited, stalled or legacy software still carrying the business.
Questions
AI & Automation: common questions.
- Will our data be used to train someone else's model?
- Not on our watch. We select providers and configure them so your data is not retained or used for training, and we will document exactly where data goes before anything is sent.
- How do we know it is accurate enough?
- We build an evaluation set from your real cases and measure against it. If the quality bar cannot be met, that is a finding worth having early rather than after launch.
- Can you start small?
- That is the recommended way. One workflow, measured properly, tells you more about the value of AI in your business than a strategy document.
- How do you price work?
- Fixed fee for discovery, phased fixed scope for builds, and a monthly rate for embedded teams. You get a cost range before any build starts, and we would rather tell you a number you do not like than discover it together halfway through.
- Who owns the code?
- You do. All source code, infrastructure definitions and documentation are yours, in your repositories and your cloud accounts, from the first commit.
Other services
Need ai & automation?
Tell us the problem and the constraints. We'll come back with how we'd approach it, what it would take, and whether we're the right people for it.