Builtt AI
AI Services
We're not an AI hype shop. We're a development agency that has been building intelligent features into products for years. We now offer that work as a standalone service for clients who want to move faster and work smarter.
30 min · free · no commitment
10+ AI features shipped in production
Why Builtt
The difference matters more than it sounds. Here's why.
Strong on models, lighter on engineering
Dev agency first. AI is how we build, not what we sold last quarter
Delivers a proof of concept, hands it over
We deliver production-ready code that runs in your business
Doesn't know your stack, your users, or your market
We already know e-commerce, SaaS and web deeply
Separate teams for AI and implementation creates friction
One team discovers, designs, builds and integrates
Engagement ends at delivery
We stay post-launch to optimise, monitor, and iterate
Three principles we hold ourselves to on every engagement. After the comparison, this is what actually matters day to day.
Every capability we offer is something we've already built inside a real product, not a service we added to our deck because it's trending. When we say we build recommendation engines or RAG systems, we mean we've done it, debugged it, and watched it run in production.
Most projects fail in integration, not in the build. When the AI team hands off to the dev team, things get lost. At Builtt there is no handover. The team that understands your architecture is the same team that builds your frontend, your API, and your data pipeline.
We run a discovery session before every engagement so we can tell you whether AI is the right solution, or whether something simpler, faster, and cheaper would work better. We'd rather lose a project than oversell one.
What we build
We don't offer AI as a single service. We offer five distinct capabilities, each applicable across different business contexts and backed by projects we've already delivered.
01
We integrate intelligence directly into your product so it learns, predicts, and personalises at scale, without rebuilding what's already working.
Applicable to
02
We build on top of GPT-4o, Claude, Gemini, and open-source models, using your data and context so outputs match how your business actually communicates.
Applicable to
03
We map the processes that eat your team's time and replace the repetitive parts with automation built on solid engineering, not no-code tools that break the moment something changes.
Applicable to
04
Built for online retail: capabilities shaped around how people browse, choose, and buy, not generic features dropped onto a storefront.
Applicable to
05
Not every business problem needs AI. This engagement is for teams who want a straight answer before committing to a build, plus a clear roadmap if the answer is yes.
Applicable to
Not sure which capability fits your situation?
That's what the discovery session is for. →Problem, solution, result. Real stacks, real releases.
How we work
Every engagement starts with a clear answer on whether this is the right solution. If it is, here is how we work.
Phase 01
Duration: 1–2 weeks
We start before writing any code. We need to understand your business context, your existing tech stack, your data, and the specific problem you're trying to solve. We do not assume AI is the right answer; we find out if it is.
What we do in this phase:
Phase 02
Duration: 1–2 weeks
Once we know what we're building, we define exactly how. Which models, which data sources, which integration points. We design the user experience for AI interactions and write the technical spec before a single line of code is written.
What we do in this phase:
Phase 03
Duration: 4–10 weeks depending on scope
We build it properly. Production-grade engineering from day one, not a prototype dressed up as a product. Security, fallback logic, monitoring, and error handling are built in from the start, not bolted on at the end.
What we do in this phase:
Phase 04
Duration: Ongoing
These systems behave differently from traditional software: they improve with data and need attention after launch. We monitor performance, collect feedback, and iterate. That is where value compounds over time.
What we do in this phase:
Common questions
For business & strategy
Not at all. That is exactly why we offer a discovery session first. Most clients arrive with a general sense that AI could help, but no specific brief. We spend the first 30 minutes mapping your workflows, identifying high-value opportunities, and telling you whether AI is the right tool or whether something simpler would work better. No commitment required.
Off-the-shelf tools are generic by definition. They are not trained on your data, they do not integrate with your existing systems, and they do not fit your workflows. We build systems connected to your data sources, tuned to your processes, and shipped to production standard so they get used. The difference is between a tool that demos well and one that still runs in your business twelve months later.
It depends heavily on scope. A focused automation or a single feature can be scoped and delivered in weeks. A full product build or complex LLM integration is a larger engagement. We do not publish fixed prices because every project starts from a different place, but we are transparent about scope and cost before anything is agreed. The discovery session is the right place to get a realistic picture.
A focused first phase (one feature, one automation, or one integration) typically takes 4 to 8 weeks. More complex builds with multiple components run longer. We recommend starting with a narrow, high-value scope rather than trying to do everything at once. A smaller first delivery means faster value, lower risk, and a better foundation to build on.
For technical teams
No, but understanding what data you have (and what you do not) is part of discovery. Some features require training data, some work with structured data you already collect, and some work without proprietary data at all. We tell you what you need and whether it exists before we write a single line of code.
We work across the main models (OpenAI GPT-4o, Anthropic Claude, Google Gemini) and choose based on what the task requires, not what is trending. We also work with open-source models where privacy or cost makes that the better decision. Model choice follows your requirements, data sensitivity, and budget. We walk you through the tradeoffs.
This is always part of the technical spec from day one, not an afterthought. Depending on your requirements we implement data anonymisation, on-premise or private cloud deployment, access controls, and audit logging. For regulated industries (finance, healthcare, HR) we have experience building systems that meet strict compliance requirements. If data sensitivity is a concern, raise it early and we design around it.
30 min · free · no commitment
Tell us what you are building. We will map where AI can help and what to leave alone.