Why This Exists
Because artificial intelligence is being used recklessly—and people are paying the price.
Executives are asking for AI outcomes. Builders are shipping half-done flows. Meanwhile, the operator in the middle is left holding the bag when it breaks.
So what makes this different?
This isn’t about inspiration. It’s about fixes. Built in your stack, with your real data, for actual accountability. Every session is a hands-on repair—not a prototype to forget in a week.
Examples of what gets solved here:
- 🧠 An internal GenAI assistant that keeps hallucinating SKUs—rebuilt with a prompt scaffolding + fallback logic layer
- 📊 A finance dashboard pulling “valid” numbers that never matched reality—rewired using a clean SQL pipeline and embedded anomaly detection
- 💬 A customer LLM chatbot trained on outdated support docs—rebuilt using real-time retrieval, schema validation, and controlled embeddings
- 📈 A PowerAutomate chain that triggered daily but silently failed on exceptions—replaced with a FastAPI Python service + Slack alert logic
These aren’t experiments. They’re operational saves.
Who This Is For
If you're the one people call when the numbers break or the AI “acts weird”—this is for you.
- ⚙️ Operators under pressure — You’re the default owner of every system no one else understands—and now they want GenAI on top of it.
- 🚀 Founders deploying faster than their team can support — You can’t afford experiments. You need fixes that move.
- 📊 RevOps leaders tired of dashboards that lie — When metrics don’t align and execs start doubting everything—you need a teardown, not a tooltip.
- 🧠 Analysts who’ve outgrown “just try a prompt” advice — You want logical chains, traceable outputs, and explainability that doesn’t evaporate when the context shifts.
- 🧮 CFOs and CTOs who’ve been burned — You’ve spent on AI. This is how you finally get returns—with clarity, control, and reuse.
Still unsure if this is built for your case?
Here are real-world examples of people who booked a session:
- 🧩 A Head of BizOps who built a GenAI workflow to autofill contract terms—but it couldn’t handle exceptions. We replaced it with rule-based validation + LLM fallback scoring.
- 📉 A product team trying to use OpenAI to generate SQL queries from plain English—but it failed with edge cases. We rewrote it to chain prompt output into a semantic filter with reranking logic.
- ⚖️ A legal analyst who needed AI to summarize clauses—but the summaries skipped required terms. We piped the output through a clause validator and embedded checklist logic.
- 📦 A customer support team with GenAI-based ticket tagging that overfit their top 5 intents. We tuned the embeddings, added metadata triggers, and made the system robust across long-tail tickets.
- 💸 A RevOps lead who booked just to figure out why their “LLM-powered forecasting model” made no sense in a board meeting. We did the teardown live. It was a math bug. They walked away with a working model, coded clean in Python.
If any of that sounds familiar—you’re in the right place.
What This Actually Delivers
This isn’t “learn about AI.” This is “fix the thing today, and understand it tomorrow.” You leave with logic that runs. Explained. Rerunnable. Auditable.
- ✅ Prompt chains that pass schema checks and don’t hallucinate
- ✅ Dashboards with SQL and Python logic that hold under audit
- ✅ Scripts that reduce ops debt, not increase it
- ✅ Flows you can hand off, scale, or show your CFO without fear
What tools or tech can be used?
I work inside your stack—not mine. Tools used across sessions include:
- 🧠 LLMs: OpenAI (GPT-4, GPT-3.5), Claude 2, Gemini, LLaMA-based models
- 🔗 Orchestration: LangChain, guardrails, routing logic, prompt templating
- 📊 Data: Redshift, Postgres, Snowflake, Google Sheets, SQL triggers
- ⚙️ Automation: PowerAutomate, Zapier, FastAPI, Python scheduling
- 🛠 Outputs: Slack bots, JSON endpoints, KPI dashboards, PDF reports
If you’ve got data, flows, logic, or mess—I’ll meet you where it lives. You won’t be asked to switch platforms just to fix something broken.
What We Refuse
- ❌ Slides with no code
- ❌ Black-box outputs with no way to trace what went wrong
- ❌ LLMs that sound great but fail under stress
- ❌ “Next-gen dashboards” that still hide the truth
- ❌ Flows that impress in a demo, then disappear in prod
This isn’t anti-AI. It’s pro-accountability.
I’ve worked with teams who believed they had something “working”—until it silently failed, and no one had logs, tests, or explainable outputs.
In one session, we rebuilt a chatbot that had gone rogue. Why? Because no one had set completion length limits, memory truncation, or guardrails. That one oversight caused a three-week trust gap with customers.
Another team believed their vector search was rock solid—until we tested edge cases and found that 30% of queries returned irrelevant results due to embedding drift. We solved it by adding keyword fallback logic and clustering validation.
This is what “pro-AI” means: truth, clarity, explainability, and velocity that survives real-world chaos.
This Isn’t About the Future of AI. It’s About the Present of Your Business.
And the present is brittle.
Most GenAI deployments are fragile at best. This is how you make them robust. You don’t need more vision decks. You need a working prompt chain. A dashboard that makes sense. A model that doesn’t break when someone renames a column.
This isn’t theory. It’s the fix. In session. In your stack. With you in the loop.
Describe What’s Broken → I’ll Help You Fix It