A focused group of engineers who get close to your problem and stay there until it's solved. No account managers, no bloat, no off-the-shelf models dressed up as solutions.
Because proximity means something. We're a small, focused studio — when you work with us, you get senior engineers, from kickoff to deployment, with no account manager in between.
But there's a second meaning: we can run AI literally next door to your data. Deployed on your own servers, using open-source models, with no data ever leaving your infrastructure. The name is the promise.
Everything we build is tested, documented, and deployed on your infrastructure — not a proof of concept you'll need to rebuild. We work in Polish and English and handle end-to-end: design, build, infrastructure, handover.
We deploy open-source models on your own servers — Bielik, Llama, DeepSeek, Qwen and others. No external API calls, no data residency risk. Built-in GDPR compliance for regulated industries.
No account managers, no ticket queues. Direct Slack or email to the people actually building — both of us, on every project, every week.
We scope, build, and hand over. No subscription, no SaaS seat, no lock-in. You get code you understand and infrastructure you control.
Three stages, no surprises. We keep the process lightweight so the energy goes into building.
Before writing a line of code we map your workflow, data, and constraints. Most AI projects fail here — not in the model.
We build the smallest thing that actually solves the problem, with weekly demos so you see progress and can steer early.
We handle cloud deployment, write the handover docs, and stay available for the first weeks post-launch.
You'll work directly with the people who build your product — start to finish.
PhD candidate in CS & Economics at the University of Warsaw. 4 years building production AI systems across healthtech, finance, and startups — from LLM agent pipelines at Bayer to a full-stack AI recruitment platform as a technical co-founder. Specialises in RAG, agents, and scalable ML on AWS & Azure.
I’m a computational engineering professional with a background from UW ICM and a Mechatronics focus in Photonics. I’ve worked across corporate and startup environments in data engineering, analytics, and data science, with experience in NLP, machine learning, AI engineering, and both classical and modern computer vision methods.
Shipped products, deployed systems, and prototypes with visible outcomes.
AI admissions workflow that reads applicant documents, extracts structured signals, and lets staff query the full candidate pool in plain English.
Voice-led math tutor with multiple agents for task generation, answer verification, and interactive guidance through problem solving.
Custom annotation and research workflow tooling built around a healthtech team’s exact process, replacing a generic paid platform.
AI assistant for navigating ZUS workplace-accident benefits, eligibility checks, and filing steps for self-employed users.
Enterprise document intelligence system for querying large corpora, surfacing risk, and turning static archives into an operational knowledge layer.
Web app that reads NDAs, flags unusual clauses, and explains what a user is signing in plain language.
Desktop app that searches PubMed, extracts genes and variants from full text, and exports reviewable evidence tables for biomedical research.
Swift iOS app that streams to a FastAPI agent backend, writes structured state to Firestore, and reflects actions in real time.
3D-printed positioning rig that stabilises head placement relative to a phone camera for consistent visual-data capture.
Tell us what you're trying to automate, build, or fix. We'll reply within one business day — in Polish or English, your call.
nextdoorai@gmail.comNo forms, no CRM sequences. A direct reply from one of us.