What it looks like
when it actually ships.
A selection. We've redacted client names where we had to. Numbers are real, methods are documented.
CupidBot — AI that gets you dates
Built from scratch with the CupidBot team: the ranking + chat models behind an autonomous dating-app agent that swipes, chats, and books real dates. Trained on ~200k real conversations; tone-conditioned chat (100+ styles), automated follow-ups, calendar-aware scheduling, and a safety layer that mimics human cadence to avoid platform bans. Featured in Vice, NY Post, Fox, Futurism, Yahoo, BFMTV.
Thumbprint — automatic furniture placement from floor-plan images
Built the core ML system for Thumbprint (US-based unicorn, commercial furniture design): given a furniture plan as an image, the model parses the layout and auto-places real catalog SKUs in 3D — sized, oriented, and snapped to walls. Powers the platform that has now visualized 181M sq ft and regenerated 428K products across 392K spaces.
Paysera — GDPR-compliant customer chatbot
Developed a customer-facing LLM assistant for Paysera (EU payments). Designed for GDPR from day one: PII redaction in the retrieval pipeline, EU-region inference, full audit log per message, right-to-erasure flows wired into the vector store. Citation-grounded answers over Paysera's own help-center, with human-handoff on regulated topics.
Cordia Group — AI roadmap memo for a large building manufacturer
A three-week diagnostic across Cordia Group's plants and divisions: 14 stakeholder interviews, a data-estate audit (ERP, MES, CAD/BIM, maintenance logs, and the inevitable Excel layer), and a 15-page board memo. Not a vision deck — a sequenced list of AI bets, each with an impact estimate, a data-readiness score, and an owner. Excerpts below.
Quote & tender automation first: RFQ parsing + BOM matching on ERP data that was already clean. Defect detection second — line cameras were already installed, labels cheap to collect. Both shippable inside one quarter.
Quoting cycle ~10 days → under 2, so 30–40% more tenders answered by the same team. Scrap on camera-monitored lines down an estimated 8–12%. Payback inside the first year. Estimates, labeled as estimates — not promises.
No central data lake first (the classic 6-month trap). No OCR-ing 20 years of paper archives upfront — cost exceeds value. No training on pre-2022 sensor data: uncalibrated clocks, silent drift. Start with the two systems that already share keys: ERP + MES.
Wait: demand forecasting, until 12 months of unified cross-division sales data exists. Kill: the internal chatbot over undocumented processes — automate the documentation first, then revisit.
B2B staffing firm — the revenue grind, automated
For a European B2B staffing firm (name under NDA): four automations shipped in one quarter. An outreach agent that researches every prospect before writing a word. A Telegram helper the partners actually use. An automatic review loop. And a SEO & GEO agent that keeps the firm visible in Google — and cited when ChatGPT gets asked who to hire.
ICP-filtered company list, enriched and researched by an agent before first touch — recent funding, hiring pages, tech stack. Reply rate 0.7% → 4.9% in eight weeks; ~30 qualified calls booked per month, on autopilot.
Lives where the partners already live. Drafts replies, preps call briefs from CRM history, logs every touch back automatically. Zero new dashboards to learn — adoption was day one.
Review requests fire on placement success, routed to Google: 4.1★ → 4.8★ on 5× the volume. The GEO agent keeps service pages structured for AI answers — the firm now appears when ChatGPT and Perplexity are asked for staffing partners in its region.
Want the unredacted version?
We'll share named references on a call once we know your project's a fit.