We are a family-run business led by me, Cyril, and my lifelong partner, Kateryna.
Before working independently on applied AI, we spent over 16 years co-building and operating PropertySpaces, a real estate photography business based in Toronto. Since 2008, we grew it from a one-person operation into a team managing more than 1,500 appointments a year with seven photographers.
Running a busy service business meant dealing with real operational issues every day — scheduling, coordination, invoicing, follow-ups, and constant trade-offs between efficiency and reliability. When existing tools didn't fit our needs, we adapted our workflows and put supporting systems in place to keep operations reliable and manageable.
That experience still guides how we work today. We focus on understanding real processes and constraints first, then carefully exploring whether modern AI can practically reduce friction through small, low-risk experiments — with an emphasis on clarity about what works, what doesn't, and why.
Kateryna is a founding partner in all of our ventures and plays a key role behind the scenes, overseeing administrative operations and ensuring consistency, follow-through, and clear communication. Her attention to detail and commitment to quality have been essential to how our businesses operate.
Today, through {CK} Web Solutions, we work independently and deliberately — combining long-term operational experience with applied AI experimentation to help organizations explore new possibilities without unnecessary risk.
We test AI models on real-world tasks to reveal strengths, limits, and the best fit for your use case.
Models capable of interpreting images, identifying objects, and extracting visual meaning.
Models we work with:
Models for analyzing, transcribing, separating, and generating audio — from speech recognition to real-time voice conversations.
Models we work with:
Models optimized for semantic embeddings and similarity search across large document collections.
Models we work with:
Models designed to understand, summarize, classify, and reason over written language.
Models we work with:
Systems for extracting text and layout from scanned documents, PDFs, and photos.
Models we work with:
Models used to tag, route, score, or flag data at scale.
Models we work with:
Models used to verify, filter, and sanity-check outputs from other systems.
Models we work with:
Many real-world problems require combining several of these capabilities into a single workflow. We help you choose the right models for each step — balancing accuracy, speed, and cost — and test whether the approach holds up before it’s ever deployed.
Here are just some of the case studies we conducted to confirm or rule out AI feasibility for real-world business problems.
We combined Matterport 3D scans with OCR to create an indoor navigation system for grocery stores. The AI reads product names directly from shelf images, maps each item to exact 3D coordinates, and provides turn-by-turn directions — like Google Maps, but inside the store. Customers find any product in seconds instead of wandering aisles.
We built a chatbot for bookstores that understands natural language queries like "something like Harry Potter but for adults" and recommends only books currently in stock. The system combines ISBN-based inventory data, Google Books metadata, semantic search, and LLM validation in a multi-stage pipeline that delivers genuinely intelligent suggestions — not just keyword matches.
We built a video analytics pipeline that counts store visitors and identifies demographics — gender, age, and children — from entrance camera footage. Traditional face-based detection failed at distance, so we used OpenCLIP to classify from body appearance instead. The system achieved 90% accuracy, giving the client proof-of-concept data for upper management.
We built a Chrome browser plugin that uses OpenAI Whisper running entirely on your computer — no cloud, no subscription, no per-minute fees. Dictate detailed prompts in seconds instead of typing them. One of the simplest and most effective AI tools we've created, proving that sometimes the best solution is a small tool that removes everyday friction.
We tested whether generative AI models could automate professional real estate photo retouching — window pulls, sky replacement, and object removal. The retouching quality was impressive, but models still introduce subtle "hallucinations" that alter reality. For MLS listings requiring pixel-perfect accuracy, this technology isn't ready yet — but progress suggests it will be soon.
Practical AI Expertise Beyond Generic Models
"Imagine testing whether an AI idea actually works — before committing time, money, or integrating it into your systems."
Learn more →"Imagine an AI solution where each task is handled by the model best suited for it — not one model trying to do everything."
Learn more →"Imagine customers asking intelligent questions about your inventory or operations — without exposing your real-time data publicly."
Learn more →"Imagine a problem that was difficult or impossible to solve a few months ago suddenly becoming straightforward."
Learn more →"Imagine getting accurate answers fast — without paying for more intelligence than the task actually needs."
Learn more →"Imagine knowing exactly what an AI system will cost — before it's ever used at scale."
Learn more →We start with a short conversation to understand the problem, constraints, and why existing approaches may not be working.
We design and run a small, contained experiment using real data to test feasibility, accuracy, cost, and limitations.
You receive a clear assessment of what works, what doesn't, and whether the idea is worth pursuing further.