You will get Fractional CTO AI Architecture Audit - Gap Analysis & Roadmap
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Project details
Most AI systems stuck in pilot have the same underlying issues — wrong architecture, poor integration, or an AI agent that was never properly connected to the workflow. This audit surfaces exactly what is wrong. I review your full AI setup, identify architecture gaps, tool-boundary risks, and evaluation blind spots, then deliver a prioritized action plan you can act on this quarter. Built on a decade in production AI, 22 shipped AI agent and RAG engagements, and Expert-Vetted status (top 1% on Upwork).
Industry
Cryptocurrency & Blockchain, Financial Services, Legal, Real Estate, SoftwareWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,800
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 30 days |
Live Consultation (Minutes) | 1 | 2 | 3 |
Frequently asked questions
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GT
G T.
May 22, 2026
30 minute consultation
LB
Louis B.
Dec 15, 2025
30 minute consultation
Very happy
CT
Ciprian Mihai T.
Oct 27, 2025
60 minute consultation
ZH
Zulfiqar H.
Sep 19, 2025
ML Advisor - CTO Roadmap Review
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 in the future.
JS
Josh S.
Aug 28, 2025
Voice AI Custom Development
Cat is a talented Python and AI developer, she has a great attitude, quick learner, and overall fantastic experience!
About Cat
AI Agent Engineer | Claude, LLM & Enterprise RAG | Fractional CTO
100%
Job Success
London, United Kingdom - 9:00 pm local time
𝗘𝘅𝗽𝗲𝗿𝘁-𝗩𝗲𝘁𝘁𝗲𝗱 on Upwork, top 𝟭% of all freelancers platform-wide
$𝟭𝟬𝟬𝗞+ earned across AI agent, RAG, and LLM engagements, 𝗧𝗼𝗽 𝗥𝗮𝘁𝗲𝗱 𝗣𝗹𝘂𝘀, 𝟭𝟬𝟬% JSS
1,200+ hours logged on Upwork, most of it hands-on AI engineer work on AI agent, RAG, and LLM fine-tuning systems
Founder of Super Cat Technology, an AI engineer team across 𝗛𝗼𝗻𝗴 𝗞𝗼𝗻𝗴 and 𝗟𝗼𝗻𝗱𝗼𝗻 building AI agent systems
Feedback from an AI agent advisory and architecture engagement:
"Very highly recommend working with Cat. The quality of work, going the extra mile and idea or assistance given were truly outstanding. We will continue to work with Cat on many projects." (ML Advisor and CTO Roadmap client, 2025 Upwork engagement)
I build AI agent systems that survive real traffic, and every AI agent ships with evaluations, regression tests, and observability. I am Cat, an AI engineer, founder, and Fractional CTO at Super Cat Technology. My focus is production AI agent work: enterprise RAG over messy internal documents, LLM fine-tuning on domain data, and Claude and open-model deployments running 24/7. As an AI engineer, my job is to close the gap between an AI agent prototype and a system your operations team trusts at 2am.
💎 𝗦𝗘𝗟𝗘𝗖𝗧𝗘𝗗 𝗪𝗢𝗥𝗞
𝗕𝗶𝗴 𝟰 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝗺. Enterprise RAG chatbot for junior auditors, multimodal RAG with OCR over compliance libraries and audit guidelines.
𝗨𝗦 𝗰𝗹𝗶𝗲𝗻𝘁, 𝟱,𝟬𝟬𝟬+ 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀. A 24/7 AI agent call centre for a US enterprise, with telephony, appointment booking, and AI agent tool-calling.
𝗙𝗮𝗺𝗶𝗹𝘆 𝗼𝗳𝗳𝗶𝗰𝗲. Fine-tuned LLM with RAG over financial research and portfolio holdings, powering an AI agent investment workflow deployed on-prem and air-gapped.
𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝘂𝗶𝗹𝗱, $𝟯𝟳𝗞 𝗼𝘃𝗲𝗿 𝟮𝟰𝟳 𝗵𝗼𝘂𝗿𝘀. Voice agent for a commercial voice product, 5-star client feedback.
𝗣𝗿𝗼𝗽𝗲𝗿𝘁𝘆 𝘁𝗲𝗰𝗵 𝗦𝗮𝗮𝗦. AI agent natural-language query layer over property search.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲. N8N2MCP, an MCP server generator with 𝟭𝟯𝟭 GitHub stars.
✔️ 𝗪𝗛𝗔𝗧 𝗜 𝗗𝗢
- AI agent architecture on LangGraph, LangChain, AutoGen, CrewAI, and SmolAgents, plus MCP servers wiring each AI agent into live business systems
- Enterprise RAG engineering: multimodal RAG pipelines over compliance libraries and internal knowledge bases, with OCR and table parsing for semi-structured documents
- Claude and LLM integration: Claude, GPT, and open models behind one AI agent interface, with routing, caching, and cost control
- LLM fine-tuning on open models (Llama, Qwen 2.5, Phi, Deepseek, LLaVA) to ground AI agent behaviour in your domain language and data
- Private and on-prem AI agent deployment for regulated data, including air-gapped environments where nothing leaves your network
- Voice agent development on LiveKit and similar stacks, with telephony, AI agent tool-calling, and 24/7 reliability
- GraphRAG and LightRAG on Neo4J for AI agent use cases where vector search alone is not enough
- Multimodal AI and enterprise OCR (CLIP, LLaVA, PaddleOCR, YOLO) feeding AI agent document understanding
- Fractional CTO advisory: AI agent architecture review, evaluation strategy, and AI engineer team build
🎯 𝗪𝗛𝗢 𝗜 𝗪𝗢𝗥𝗞 𝗪𝗜𝗧𝗛
- Enterprise teams putting an AI agent into customer service, sales, or internal operations, not a one-off pilot
- Financial services, family offices, Web3, supply chain, and regulated organisations with real data and real stakes
- Funded startups moving an AI agent MVP into production
- Teams that want a senior AI engineer on AI agent architecture and evaluation, not the cheapest pair of hands
🔄 𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞
Step 1. Discovery: your use case, current stack, data reality, and what the AI agent has to get right in production.
Step 2. Architecture: AI agent flow, tool boundaries, fallback behaviour, and an evaluation plan, delivered within a week.
Step 3. Build and ship: a production AI agent with evaluations, regression tests, and LLMOps tooling behind it.
Step 4. Handover or stay: train your team, or stay on as the AI engineer and Fractional CTO while the AI agent matures in the wild.
Send me a message or an invite with your AI agent, enterprise RAG, or LLM project. You get a hands-on AI engineer on the build, not a handoff. I reply within a few hours, 7 days a week, across US, UK, and EU time zones.
Steps for completing your project
After purchasing the project, send requirements so Cat can start the project.
Delivery time starts when Cat receives requirements from you.
Cat works on your project following the steps below.
Revisions may occur after the delivery date.
Intake + repo/system access
You share your architecture doc, repo access, and current stack. I come to the kickoff call with specific observations, not generic questions.
Deep-dive review
Assess architecture, agent tool boundaries, retrieval and evaluation setup, LLMOps, cost model, and the team structure supporting it.