# Fractional CTO: ML Architecture & Roadmap Review | Super Cat > Reviewing the machine-learning plan, the stack and the sequencing before the team committed budget to it. Most of the value is in what comes off the roadmap. Source: https://www.super-cat.tech/portfolio/fractional-cto-review Last modified: 2026-10-22 [← All case studies](https://www.super-cat.tech/portfolio)Rated 5.0 · advisory engagement # Fractional CTO: ML Architecture & Roadmap Review Reviewing the machine-learning plan, the stack and the sequencing before the team committed budget to it. Most of the value is in what comes off the roadmap. ## The problem A team was about to commit budget to a machine-learning roadmap that had never been reviewed by anyone who had shipped one. The expensive mistakes in ML are made in the planning, and they do not surface until two quarters of engineering have been spent on them. ## What we built An advisory engagement rather than a build: reviewing the machine-learning plan, the stack and the sequencing before the team committed to it. Before: A roadmap never reviewed by someone who shipped ML; Planning mistakes surface two quarters later. After: Rated 5.0 · advisory engagement.Before - A roadmap never reviewed by someone who shipped ML - Planning mistakes surface two quarters later After Rated 5.0 · advisory engagement Stack Solution Architecture Machine Learning Advisory AI Strategy ## Why it holds up This is usually the cheapest work we do and the most expensive to skip. Most of the value is in what comes off the roadmap, not what goes on it. Rated 5.0, with the client writing: “Very highly recommend working with Cat. The quality of work, going the extra mile & idea/assistance given were truly outstanding. We will continue to work with Cat on many projects.” Related service: [Fractional Chief AI Officer](https://www.super-cat.tech/fractional-caio) ## More case studies Big 4 client · fully on-premise ### Enterprise RAG Chatbot for a Big 4 Accounting Firm Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached. - Juniors reading thousands of pages per question - Seniors acting as search engines Read the case → 200k calls/month · 67% booking rate vs 77% human ### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot. - Night and weekend calls unanswered - Staffing every shift hard to justify Read the case → US$1M collected in the first 3 months ### AI Payment Collection and Reminders The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months. - Chasing payment is awkward and late - Billable time lost to collections Read the case → Get started ## Talk to the engineer who will build it. You will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship. [Book a call](https://calendly.com/super-chain/catyung)Hong Kong & London time zones