Wali Ullah K.

𝗜 𝘀𝗵𝗶𝗽 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝘀𝘂𝗿𝘃𝗶𝘃𝗲 𝗺𝗼𝗻𝘁𝗵 𝘁𝘄𝗼. Most AI projects work in the demo and break later, when token spend climbs, retrieval quality drifts, an automation fails silently overnight, and nobody can say which change caused which regression. I build the layer that stops that: evaluation harnesses, caching, cost observability, real error handling and tenant isolation, designed in from the first commit. US-based, Central Time. Purdue MSIT. ID-verified. 𝗪𝗢𝗥𝗞 𝗧𝗛𝗔𝗧 𝗥𝗘𝗔𝗖𝗛𝗘𝗗 𝗣𝗥𝗢𝗗𝗨𝗖𝗧𝗜𝗢𝗡 ▸ A multi-tenant AI growth platform running 50+ custom Claude Code skills across GA4, Shopify, Klaviyo, Google Ads, Search Console and Convert, automating CRO, SEO, email and paid workflows for ecommerce brands ▸ Autonomous research and discovery agents that scaled a marketplace 50x: 3,500 to 175,000 active listings and 150 to 7,800 organizations in six months ▸ A Postgres RLS-enforced multi-tenant data layer on Supabase with cron jobs and materialised views, replacing live API paging with pre-aggregated tenant-scoped queries and cutting both reporting latency and token spend ▸ Prompt caching plus per-job token and cost tracking on the Anthropic SDK worker: about 90% lower recurring cost on cache hits, with spend per skill visible in the operator portal ▸ An LLM-as-a-judge evaluation framework comparing prompt performance across models, with hallucination detection, topic modelling and sentiment analysis ▸ A Dockerized MCP runtime across Google Ads, Meta Ads, Notion, Slack and Gmail that cut manual agency operations about 70%, with a human gate on anything that publishes or spends ▸ RAG pipelines over unstructured clinical notes that automate patient-history summarisation, every claim cited back to the source note ▸ 25+ production Next.js and TypeScript sites, all still running without me 𝗦𝗧𝗔𝗖𝗞 ▸ Models and SDKs: Claude and the Anthropic SDK, OpenAI, Gemini, Llama, Mistral, Hugging Face ▸ Orchestration: Claude Code, MCP servers, LangChain, LlamaIndex, CrewAI, AutoGen ▸ Retrieval: pgvector, Pinecone, Qdrant, Chroma, FAISS, hybrid search, reranking, citation ▸ Evaluation: LLM-as-a-judge, golden datasets, regression suites, hallucination and drift detection ▸ Adaptation: LoRA, QLoRA, SFT, DPO, structured output, function calling, tool use ▸ Automation: n8n, Make, Zapier, GoHighLevel, voice agents on VAPI and Twilio, with retries and logging ▸ Data: Python, SQL, Postgres, Supabase, ClickHouse, Spark, Pandas, scikit-learn, NLTK ▸ Growth: GA4, Shopify, Klaviyo, Google and Meta Ads, server-side testing, attribution, cohorts, LTV ▸ Serving: FastAPI, Docker, Next.js, TypeScript, background workers, queues, AWS, Azure, Vercel 𝗛𝗢𝗪 𝗜 𝗘𝗡𝗚𝗔𝗚𝗘 1. Architecture review. I read your setup and write down what breaks at 10x volume 2. Scoped build with a written spec, a fixed price, and an environment you can open any time 3. An evaluation harness shipped with the feature, not after it, so quality is measurable from day one 4. Handover: documentation, runbook, recorded walkthrough, 30 days of included fixes 𝗕𝗘𝗦𝗧 𝗙𝗜𝗧 𝗜𝗙 ▸ Your LLM feature works in the notebook and falls over in front of real users ▸ You need RAG over private or regulated documents with citations and access control ▸ Your token bill grows faster than your usage and nobody knows which job causes it ▸ Your operations run on manual work across a dozen tools and you want that automated properly ▸ You are building a multi-tenant AI product and want the isolation right the first time 𝗡𝗢𝗧 𝗔 𝗙𝗜𝗧 if you want an undocumented one-off script, or a chatbot demo that will never face a customer. I will say so instead of taking the contract. Send me your current setup, or the problem you are stuck on. You get a real technical opinion back before any money changes hands. Wali 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: AI engineer, LLM engineer, machine learning engineer, AI agent, multi-agent system, RAG, retrieval augmented generation, vector database, pgvector, Pinecone, Qdrant, LangChain, LlamaIndex, Claude API, Anthropic, OpenAI API, MCP server, Model Context Protocol, LLM evaluation, prompt engineering, fine-tuning, LoRA, AI automation, workflow automation, n8n, Make, Zapier, GoHighLevel, Python, FastAPI, Supabase, Postgres, ClickHouse, Docker, Next.js, TypeScript, NLP, embeddings, semantic search, chatbot, GA4, Shopify, Klaviyo, MLOps, AI consulting.