# Portfolio — AI Case Studies | Super Cat Technology > Thirteen AI case studies: enterprise RAG for a Big 4 firm, a 24/7 voice agent, natural-language EDI, NL2SQL, query rewriting and a Slack knowledge bot. Source: https://www.super-cat.tech/portfolio Last modified: 2026-10-22 Case Studies # Production AI that shipped and stayed up Thirteen engagements, each on its own page: the pain it started from, how the solution works step by step, and why it holds up. Client names stay confidential. Expert-Vetted top 1% · 100% Job Success Score (source: Upwork profile, October 2026) · Hong Kong & London Written by [Cat Yung](https://www.super-cat.tech/cat-yung), Founder, Super Cat Technology. Last updated 22 October 2026. 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 → Ask the EDI in plain English ### Natural-Language EDI for a US EDI Solutions Vendor Business users ask questions of raw X12 — “what are the net terms on invoice 4471?” — and get answers in plain English, from a Llama 3 model fine-tuned on EDI and grounded in the parsed transactions. - Business and EDI teams speak different languages - Rejected transactions decoded by hand Read the case → Same question, same SQL, same answer ### Deterministic Natural-Language Search for a Private-Markets Data Platform Deal teams ask “industrials in the southwest received this year with LTV under 70%” and get an exact, explainable table — the model plans the query, but never writes free-form SQL. - 100+ columns only an analyst can query - Vernacular terms are not column names Read the case → Ask in Slack, answered from Confluence ### Slack Knowledge Assistant over Confluence for an EdTech Company An assistant inside Slack that answers staff questions from the company’s Confluence — routed by an agent that decides, per question, whether to search the wiki, the web, or just answer. - Knowledge buried in Confluence - The same questions answered every week Read the case → Prototype · three rewrite strategies benchmarked ### Query Rewriting for a Global Asset Manager’s Research RAG (Prototype) An asset manager’s internal research assistant retrieved well when users asked well. We benchmarked three ways to rewrite the questions they actually ask, and matched each to the kind of question it fixes. - Vague questions without keywords - Three questions asked at once Read the case → Nothing leaves the room ### Super Chain: Private On-Prem AI for a Family Office An LLM with retrieval over financial research, macro data and portfolio holdings, with the model chosen per office, running on the office’s own infrastructure. Every answer is cited. - Documents that cannot leave the office - Every AI product needed a third-party API Read the case → 247 hours over three months · 5.0 rating ### Voice AI Product Build A commercial voice product run as a long hourly engagement rather than a fixed-scope prototype — 247 hours across three months, rated 5.0. - Demo calls are not real calls - Failures only appear at volume Read the case → 246 hours · sustained engagement ### Multimodal RAG Application Retrieval over source material that is part text, part table and part image — where naive chunking quietly returns answers that are confident and wrong. - Tables split mid-row - Numbers hidden in images Read the case → Rated 5.0 · shipped to a live product ### Natural-Language Query Layer for Property Search Users describe the property in a sentence; the system resolves it into the structured query underneath — against a schema that has to return exact results. - Users translate wants into filters - Search must return exact matches Read the case → Nearly 130 GitHub stars · MIT licensed ### N8N2MCP — Open Source Turns an n8n workflow into a Model Context Protocol server, so automation a team already built becomes a tool Claude or Cursor can call directly. - Teams rebuild tools they already have - Integrations duplicated for MCP Read the case → 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. - A roadmap never reviewed by someone who shipped ML - Planning mistakes surface two quarters later Read the case → ## Your problem is probably on this page. Retrieval that has to cite its sources, an agent that has to stay up, or AI that cannot send your documents anywhere. Tell us which one and we'll tell you what it takes. Book a First-Sprint Data Scientist First-Sprint Data Scientist ## A vetted engineer embedded in your team, a shipped deliverable by end of sprint 1. Pick a time below and 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. Cat Yung Hong Kong & London time zones [Open the scheduler](https://calendly.com/super-chain/catyung?utm_source=super-cat.tech&utm_campaign=first-sprint-data-scientist&utm_content=portfolio)