--- title: "AI-Native Delivery" url: "https://www.doddledesign.co.uk/services/ai-native-delivery" section: "Services" description: "Production-grade AI products built on modern stacks, with our proprietary delivery harness underneath. Includes fixed-price MVPs for funded startups." publisher: "Doddle" --- [What we do](https://www.doddledesign.co.uk/services) AI-Native Delivery Other services[OutSystems Delivery](https://www.doddledesign.co.uk/services/outsystems-delivery) [Migrate away from OutSystems](https://www.doddledesign.co.uk/services/outsystems-migrations) [Consultancy Services](https://www.doddledesign.co.uk/services/consultancy-services) ![Claude](https://www.doddledesign.co.uk/logos/claude-code-icon.svg) Build # AI-Native 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. [Let's talk](https://www.doddledesign.co.uk/contact) [What we build with AI](https://www.doddledesign.co.uk/services/ai-native-delivery#capabilities) ![Engineers working on an AI build](https://www.doddledesign.co.uk/images/ai-product.webp) What this is ## From prompt to production-grade. Software development shifted under everyone's feet this past year. Customers expect better products, AI features as standard, and delivery measured in days. Teams are spinning up MVPs and demos in tools like Lovable or Builder - and for proving an idea, that's brilliant. A sharp demo and a product you can put in front of real users are different things. Getting from one to the other takes real engineering - Claude in the loop, everything under version control, a proper delivery pipeline and testing - and twenty years of carrying a prompt all the way to production. 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. [Same harness underneath everything we build - see the full Engineering practice→](https://www.doddledesign.co.uk/engineering) Capabilities ## The AI patterns we build often. [See what we've built with AI →](https://www.doddledesign.co.uk/built-with-ai) ### 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](https://www.doddledesign.co.uk/contact) 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 approach](https://www.doddledesign.co.uk/insights/in-house-ai-not-alone) [Start a conversation](https://www.doddledesign.co.uk/contact) 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 - Google Gemini - Hugging Face - Next.js - React - Vercel - PostgreSQL + pgvector - Supabase - Langfuse - Sentry - 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](https://www.doddledesign.co.uk/contact) 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 Delivery](https://www.doddledesign.co.uk/services/outsystems-delivery) [Migrate away from OutSystems](https://www.doddledesign.co.uk/services/outsystems-migrations) [Consultancy](https://www.doddledesign.co.uk/services/consultancy-services) 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. How do you decide which model to use? 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. How do you stop the AI from hallucinating in production? 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. Do we own the code, the prompts, and the evals? 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. Can you work alongside our existing team? 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. We're an OutSystems shop. Do we have to leave the platform to do AI properly? 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. What does an MVP cost and how long does it take? 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 →](mailto:hello@doddledesign.co.uk) [Let's talk](https://www.doddledesign.co.uk/contact) [See an AI build](https://www.doddledesign.co.uk/work/boardly) ## More from Doddle - [Studio summary for agents](https://www.doddledesign.co.uk/llms.txt) - [Full text of every page](https://www.doddledesign.co.uk/llms-full.txt) - [How to work with Doddle as an agent](https://www.doddledesign.co.uk/agent-instructions.md) - [Every page on the site](https://www.doddledesign.co.uk/sitemap.xml) - [Talk to a human](https://www.doddledesign.co.uk/contact) or email hello@doddledesign.co.uk