Services / AI Solutions
We separate technical risk from commercial risk — validating feasibility on your real data first, then wrapping it in a product users actually keep using.
What do you actually build?
Assistants, RAG over your own data, and agents that take action — shipped as product, not as a demo.
Could you build one for us?
That's what the sprint is for. Starter proves feasibility on your own data in about two weeks — before you commit to a UI or a roadmap.
And where does it all end up?
With you. The repository, the prompt strategies, the keys and the deployment config are handed over at the end — standard, portable, no lock-in.
AI assistant — it can be imperfect. For anything specific, book a call.
Recognise any of these?
Your team re-keys data out of documents, emails and forms all day. We hand that to an agent and keep a human on the exceptions.
The answer exists — in a wiki, a PDF, a ticket from last year. RAG over your own sources surfaces it, with the citation attached.
A model that invents details is worse than no model. We ground every answer in your data and refuse the rest server-side.
Keyword search fails the moment someone phrases it differently. Semantic and hybrid retrieval matches meaning, not spelling.
Generic widgets feel foreign and get ignored. We build the assistant into your product — your data, your tone, your permissions.
Impressive in a slide deck, unusable at real volume. We benchmark accuracy, latency and cost on your data before anyone commits.
Documents, tickets, records or a catalogue the work already runs on.
Feasibility tested on your own data before a roadmap gets signed.
Not a widget in the corner: an assistant with your data, tone and permissions.
You get the benchmarks, the repository and the keys — swap models whenever you like.
This is a real assistant we built and run in production on this site — same stack we'd ship for you. Ask it anything about doubleucode.
Try asking
Grounded in
AI assistant — it can be imperfect. For anything specific, book a call.
Live model, guardrails on: off-topic and unsafe prompts are refused server-side, and answers stay inside what we've actually published.
Practical AI connected to your data, your workflows and your product — not a generic chatbot bolted on the side.
Conversational assistants and generative interfaces that feel like part of your product, not a widget.
Retrieval-augmented generation so the model answers from your documents and knowledge — without hallucinating.
Agents that take action across your tools — reading what comes in, pulling out the data that matters, and completing multi-step tasks end to end.
The models and tools we use to ship reliable, production-grade AI.
Shipped patterns
Move Maya to the Pro workspace
workspace.moveDone — Maya is on Pro, seat count updated to 43.
An in-app assistant that answers from product data and takes actions for users — grounded, cited and safe.
Read Full Case StudyShipped patterns
Overnight queue · 214 tickets
A support assistant that resolves common tickets from the knowledge base and escalates the rest to a human.
Read Full Case StudyShipped patterns
Which contracts auto-renew in Q3?
Semantic search and answers across thousands of internal documents, with per-user access control and citations.
Read Full Case StudyShipped patterns
Something warm for a rainy commute
A conversational shopping assistant that understands intent and recommends products from the live catalogue.
Read Full Case StudyShipped patterns
invoice-4821.pdf
An agent that extracts, validates and routes data from incoming documents — cutting manual processing time.
Read Full Case Study68%
Support questions resolved without a human
4.2k hrs
Manual document handling removed per year
1.8s
Median time to a grounded answer
92%
Answers delivered with a source you can open
Our users used to open a ticket just to find a setting. Now they ask inside the app and get an answer with a link to the exact page — and we can finally see what they were asking for all along.
SaaS & Product
In-app copilot grounded in product data
The assistant takes the repetitive half of the queue and hands us the rest with the context already gathered. Nobody waits until the morning for a first reply any more.
Customer Support
Support agent with human handoff
Invoices and delivery notes used to sit in an inbox until someone typed them in. They're read, checked and routed the same day now, and we only look at what the agent flags.
Operations
Document processing agent
01
You tell us the use case; we say whether AI is the right tool for it.
02
30 minutes on your data, your constraints and the risk worth validating first.
03
You pick the stage, we agree the scope, and the build starts.
Most AI projects fail by trying to solve technical, commercial and usability risk all at once. We separate the journey into three stages — validate what matters at each step, and never pay for the same work twice.
We map your data, define what success looks like, and select the right risk profile and stage for your project.
A focused, intensive build with our engineers — from a feasibility lab to a production-ready MVP, depending on the stage.
You get the repository, the prompt strategies and the keys. The code is yours — standard, portable and documented, with no lock-in.
Best for
Can the AI actually do this? A functional lab to stress-test your logic on your real data.
What you get
Best for
Will investors and stakeholders fund this? Your validated logic wrapped in a stunning, live prototype.
What you get
Best for
Will users stick around? A production-ready MVP with the infrastructure to support your first real users.
What you get
Not sure which stage fits? A 30-minute technical assessment, no commitment — we'll help you pick who you need to impress next.
Paweł Pierzchlewicz · Head of AI · Call: +48 667 083 196
Got a question about your own use case? Send it straight to us:
Ask about your AI project100%. At the end of the sprint we hand over the full repository, prompt strategies, API keys and deployment configuration. The code is yours, with no lock-in.
Industry-leading models — OpenAI, Anthropic (Claude) and Gemini — with a streamlined stack: Next.js for the interface, Supabase for backend, and pgvector for vector storage. We pick the best model for your accuracy, latency and budget, and benchmark options against your data during the Starter stage.
Most agencies sell you a six-month roadmap before proving the model works. We separate technical risk from commercial risk: we validate feasibility first, on your real data, before you invest in UI, infrastructure or scale. You get working software and a clear decision framework — not slide decks.
Yes. The stages build on each other — the backend logic we validate in Starter becomes the brain for Plus, so you never pay for the same work twice.
You receive the complete repository, prompt strategies, keys and deployment config. You can continue with your own team or with us. We also offer ongoing maintenance and support for production systems that need monitoring, updates and improvements.
