Two senior 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 two-person studio — when you work with us, you get both 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.
Real deliverables, real outcomes. More case studies coming soon.
Universities spend over 90 minutes reviewing each applicant manually — combing through documents, cross-referencing criteria, and updating records by hand. We built a platform that does this automatically: reads applications, extracts what matters, and lets admissions staff query the entire pool in plain English.
Private tutors are expensive and most homework tools only check final answers. In 8 hours at the AI Tinkerers × Google DeepMind Hackathon we built a voice-powered AI tutor: five specialised agents handle research, answer verification, and task creation, while a voice AI guides students through exercises on an interactive canvas — catching wrong reasoning mid-solution, not just wrong final answers.
A healthtech startup — working with researchers from Stanford, Harvard, Oxford, and Cambridge — was paying $20,000/year for a generic video labelling platform that only half-fit their needs. We replaced it with a custom tool built around their exact workflow, at zero recurring cost.
Navigating Polish social insurance (ZUS) after a workplace accident means deciphering dense legal forms and eligibility rules — alone. We built an AI that guides self-employed individuals through the process step by step: asks the right questions, checks eligibility, and explains what to file and why.
Large enterprises sit on vast archives of documents that no one has capacity to review. We built an AI system that understands, queries, and reasons over complex document corpora — turning static files into an intelligent knowledge layer that answers questions and surfaces risk on demand.
Reviewing an NDA shouldn't require a lawyer on retainer. We built a web app that reads your agreement, flags unusual clauses, and summarises what you're actually signing — in plain language, in seconds. Built and deployed to production; presented at MIM UW × IBM conference.
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.