Services
AI solutions
AI that does real work inside your product.
We're the AI development partner for founders and teams who need more than a demo.
Who's it for
SaaS teams adding AI features
Your users expect AI now. We build it into your product with proper permissions, error handling and UX — not as a chatbot bolted on the side.
Companies buried in documents
Invoices, contracts, applications, reports. We turn unstructured files into structured, checked data your systems can actually use.
Teams with scattered knowledge
Answers live in Notion, Drive, Slack and people's heads. We build a company brain your team can search and ask questions.
Founders with an AI prototype
It works in the demo but breaks on real inputs. We make it reliable, measurable and ready for real users.
What we offer
We design and build AI functionality that runs in production: agents that complete multi-step tasks, pipelines that read and extract from documents, search that understands meaning, and interfaces that use voice and vision. We pick the right model for each job — Claude, OpenAI, Gemini or open-source — and keep you free to switch.
Because we're a product team, the AI comes with everything around it: the data model, integrations, permissions, interface and monitoring. Need it inside your existing app or in a new one? That's our web app development work, done by the same team.
What you'll get
AI agents & LLM workflows
Agents and custom LLM workflows that take actions in your tools, with guardrails and human review where it matters. Including MCP servers that connect your systems to Claude, ChatGPT and other assistants.
Document intelligence
Classification and structured extraction from PDFs, scans, emails and forms — validated against a schema before anything touches your data.
AI company brain
A knowledge base over your internal documents, with semantic search and answers that cite their sources. It respects who is allowed to see what.
Voice & vision
Speech-to-text, voice agents and image understanding, built into the flows where your users already work.
Why founders work with us
- Production, not demos. We test against the messy inputs real users bring, and design what happens when the model is wrong.
- Model-agnostic. We choose models on quality, cost and speed for each task, and keep the architecture ready to swap them as better ones arrive.
- Measured quality. Every AI feature ships with a set of test cases, so changes to prompts or models are checked, not guessed.
- Your data stays yours. We use providers and settings that don't train on your data, and can keep processing in the EU when you need it.
How we build AI solutions
Discovery
We define the use case, what a good result looks like, and collect real sample data. You get a clear scope before anything is built.
Prototype & evaluate
We prototype on your own data and measure the results. If the approach doesn't hold up, you find out in days — not after months of development.
Build into your product
A senior team builds the feature into your product, with the interface, integrations, permissions and monitoring it needs to run reliably.
Launch & improve
We launch with you, watch how it's used, and keep tuning prompts and models as your data and the AI landscape change.
You have questions?
We have answers
Which AI models do you work with?
We work with models from Anthropic (Claude), OpenAI and Google, and with open-source models when they fit better. We choose per task based on quality, speed and cost, and build so you can switch models later without a rewrite.
Will our data be used to train AI models?
No. We use API plans and settings where your data isn't used to train the provider's models, and we only send the data a feature actually needs. When required, we can keep processing and storage in the EU.
Can you add AI to our existing app?
Yes — that's one of the most common requests. We review your current app, find where AI adds real value, and build it in as part of the product, so it works with your existing users, data and permissions.
What is an MCP server and why would we need one?
MCP (Model Context Protocol) is an open standard that lets AI assistants such as Claude and ChatGPT use your systems safely. With an MCP server, your team or your customers can ask an assistant to look up data or take actions in your product, within the permissions you set.
How do you deal with hallucinations and wrong answers?
We ground answers in your own data with citations, validate structured output, and add human review where mistakes are costly. Every feature is tested against a set of real examples, so we know how accurate it is before launch.
How long does an AI project take?
It depends on scope, but we measure in weeks, not months. Most AI builds go live within four to six weeks of the first call. Book a call and we'll give you a clear estimate for your project.
Let’s get in touch
Ready to put AI to work?
Have an AI feature in mind or a process you want to automate? Book a free call and we'll tell you what it would take to ship it.
