AI Integration
We build AI into existing products and workflows. RAG systems, custom agents, vector search, and automation. Production deployments only — no pilots, no slide decks, no “AI strategy.”
What we build
Most “AI projects” stall in pilot. We build the ones that ship — and we run our own in production to know what breaks.
AI that answers from your own documents, products, and policies. Trained on your content, scoped to your domain, escalates to a human when it's out of depth.
Where we've shipped this
TideReply (our SaaS, live across Baltic + EU SMBs), melynasautobusiukas.lt (Lithuanian vehicle rental, RAG knowledge base in Lithuanian).
See it liveAI that takes action, not just answers. Reads your tickets, extracts data from documents, routes work between systems, runs on a schedule. Connects to the tools you already use.
Where we've shipped this
ParcelRay (warehouse parcel scanning — OCR plus AI for unstructured fields, dual-write to partner systems).
See it liveVector search, semantic recommendations, classification, and content generation. Embedded into the product itself, not bolted on as a separate widget.
Where we've shipped this
TenderWish (AI-powered EU public procurement matching — discover and analyze tenders by relevance, not keyword).
See it liveHow we work
Most AI implementations fail because they get stuck in scoping. Ours don't, because we scope short and ship a working version fast.
45-minute call. We look at where your team actually spends repetitive time, what data exists, and what's realistic to automate inside one quarter. We come back with a written scope: what we'd build, what it costs, what it doesn't do.
We design around your existing stack, not on top of it. Your CRM, your inbox, your ERP, your CMS — whatever you're running stays. AI plugs into the gaps. Not a parallel system you have to migrate to.
We train and build on your actual data. Your documents, your tone, your edge cases. Not a generic template with your logo on it. We deploy to staging, you test, we iterate.
We ship to production, watch what happens, and fix what breaks. AI work isn't done at launch — the model behavior shifts, the data drifts, and the system needs maintenance. We stay on it.
Stack
Production-grade models, vector databases, and orchestration. Picked for reliability, cost, and how well they hold up at scale — not what's trending on X this week.
Anthropic Claude
OpenAI
Supabase pgvector
Pinecone
LangChain
Python
Node.js
n8nFAQ
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Let's make this inevitable
We'll reply within 24 hours. Free 30-min call to figure out if we're the right studio for what you need.
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What happens next
