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AI Assisted Delivery

Building an AI prototype is easy these days. Building one that holds up under real use is a different job. We design, build and run AI products end-to-end, with the testing, monitoring and safety checks that turn a clever demo into software you can rely on.

Engineers working on an AI build
What this is

AI products built to hold up under real use.

Building an AI prototype is easy. Building an AI product that survives real users, real edge cases, real model upgrades, and real regulatory scrutiny is a different job. The gap between the two is where most AI launches stall.

We design, build and run AI products end-to-end on modern stacks. That covers everything from full AI-native products (search, in-app assistants, automated workflows, predictive features) through to smart AI improvements inside the everyday business systems you already use.

How we deploy AI safely

How we keep AI honest in production.

Most AI projects stall in the gap between a clever demo and a system the business can rely on. The four pillars below are the ones that matter most for AI work — the rest of the harness sits underneath.

Quality testing for AI

Every AI feature is tested continuously. Bad answers caught before users see them; upgrading models becomes a measured decision, not a leap of faith.

Visibility in production

Cost, performance and quality on live dashboards. Every interaction can be replayed for debugging. Nothing hidden.

Governance & guardrails

Policies enforced, sensitive data handled properly, AI outputs checked. Designed for audit from day one.

Continuous delivery

Every change flows through the same automated pipeline. Deployable in minutes; nothing skips the checks.

Capabilities

The AI patterns we build often.

Knowledge assistants & document AI

AI that answers questions using your own content, accurately, with citations users can verify, at scale.

AI assistants & copilots

In-app helpers that understand what the user is trying to do, find the right information, and stay within the rules you set.

Natural-language analytics

Ask a question in plain English, get a chart. Safe to run on real data, with accuracy you can measure.

Document processing

Turn messy inputs into structured, audit-ready data, extracting, classifying, summarising, enriching.

Agentic workflows

AI that does work end-to-end, multi-step automation that knows when to stop and when to ask for help.

Tuning & quality testing

We tune the model where it pays off, and test every AI feature continuously, so quality doesn't drift over time.

How a build runs

Four phases. Most builds inside three months.

Discovery & architecture

1–2 weeks

What to build, which models, which guardrails, and what success looks like. Comes out as a buildable plan, not a slide deck.

Build

3–8 weeks typically

The product itself, with testing, monitoring and deployment automation built in from day one, not bolted on later.

Hardening

1–2 weeks

Cost control, security review, regression testing, accessibility checks, the polish that turns a working build into something you'd actually launch.

Launch & iterate

ongoing

Live, instrumented, and ready to learn from real users. We stay close for as long as it's useful, and step back when it isn't.

For funded startups

Startup MVPs in 3–6 weeks, fixed price.

For funded startups, a tighter version of the above as a fixed-price MVP. You get a real product you can demo, hand to users, and continue building on, with the same harness underneath, so it can scale into a production platform without a rewrite.

Scope an MVP
For enterprise teams

Stand up AI delivery in your own team.

For teams whose strategy is to own AI capability in-house. We build the harness alongside your engineers — evals, observability, governance, code review — and ship the first product together. Then your team owns delivery, with us on speed-dial for the harder moments.

Read the approachStart a conversation
Tools we work with

The right tool for the job, not the trendiest one. A few of the names you'll see in our recent builds.

  • Claude
  • OpenAI
  • OutSystems Agent Workbench
  • Google Gemini
  • Hugging Face
  • Next.js
  • React
  • Vercel
  • PostgreSQL + pgvector
  • and many more
Is this the right track?

Honest about when to pick this one.

Good fit

Right for you if…

  • Funded startups building toward product-market fit.
  • Product teams building on modern stacks.
  • Enterprise teams building AI products outside the core platform.
  • Anyone who values pace and adaptability over enterprise standardisation.
Start an AI build
Not the move

Look elsewhere if…

  • Heavily governed estates that mandate a specific low-code platform.
  • Teams whose underlying platform is the actual problem.
  • When the question is 'should we even build this?' rather than 'how?'
Probably a better fit
OutSystems DeliveryMigrate away from OutSystemsConsultancy
Common questions

The things teams ask before they kick off an AI build.

Quick answers to the questions we hear every week. Yours not here? Tell us and we'll add it.

In discovery, against the actual workload. We benchmark a shortlist on your data, measure quality and cost, and pick the smallest model that does the job. We don't marry one provider.
Retrieval grounding, output validation, and continuous evals running against a labelled set. The harness fails a deploy if scores drop below the bar we set with you. It's not magic, it's the same engineering discipline we apply to everything else.
Yes. Everything we build lives in your repo, including prompts and eval suites. We're not building a black box you can only run through us.
Often. We slot in beside in-house engineers, especially on the AI-specific pieces. We're also happy to lead end-to-end where that's the cleaner shape.
No, and we'd usually advise against it. OutSystems' Agentic Systems Engineering, Agent Workbench, Mentor and the Enterprise Context Graph, makes the platform a real AI environment, and the open ecosystem means Claude Code, OpenAI Codex and Cursor all run inside the same governance model. We build there first. For workloads that genuinely need a specific stack — real-time optimisation, niche ML, custom compute — we put a service alongside and the Context Graph still governs across both.
For funded startups, fixed-price MVPs run 3–6 weeks. Exact price depends on scope, but it's usually similar to a single experienced engineer for that period. Longer programmes are time-and-materials, against a discovery plan.

Got an AI build in mind?

Bring us the goal. We'll come back with what we'd build, roughly how long, and roughly what it'd cost.

hello@doddledesign.co.uk →
Let's talkSee an AI build