MegaDoc
My standardized framework for moving AI from research to production reliably. Focused on Risk, Quality, and Process.
This page documents the methodology I use to take AI systems from research to production in a repeatable way. I use this framework when leading AI initiatives in different domains, adapting it for teams building production AI systems.
Zero data retention. Ephemeral processing.
Smart routing and tiered models.
X-Ray tracing, metrics built-in.
Security gates and compliance ready.
I use a 5-phase lifecycle to ship production AI systems with predictable quality and minimal risk.
Every PR must pass these automated checks before merge; these gates are non‑negotiable in teams I lead.
I do not approve deployments that bypass these gates. In my experience, skipping quality checks for speed always results in slower delivery due to rework and production incidents.
I apply a three-layer guardrail architecture in production: input gatekeeper, model strategy, and output auditor.
Pre-Inference Security. Heuristic analysis to detect "Jailbreak" patterns and Zero-shot classification to reject out-of-scope queries.
Hybrid Inference Architecture. Vendor-agnostic design with OpenRouter gateway. Fine-tuning for style, RAG for facts.
Post-Inference Validation. Self-consistency checks to prevent hallucination and JSON Schema Validation for structured output.
Three layers of defense ensure production AI systems are secure, accurate, and compliant. No single point of failure.
The SQL Sandbox demonstrates our commitment to zero-trust data handling principles across the platform.
All uploaded databases exist in-memory or temp storage. Automatic cleanup after 30-minute session TTL. No filesystem persistence beyond session scope.
SQLite connections use URI mode flag: ?mode=ro. Agent
system prompt blocks DML operations. Pre-execution SQL validation layer.
Native SQLite: Direct connection. SQL Dumps: Runtime rehydration via executescript(). Spreadsheets: Pandas normalization → SQLite translation.
LangChain OpenAI Tools agent (temperature=0). Auto-correction on SQL errors. Schema-aware query generation. Transparent SQL exposure (Glass Box AI).
Try the SQL Sandbox → to experience ephemeral data processing in action.
In Enterprise SaaS, unchecked inference costs kill margins. I architect systems with a 'Router Pattern' to optimize unit economics:
Impact: In practice, this pattern can reduce blended token cost by up to ~85% compared to a naive 'GPT‑4 for everything' approach.
I design feedback loops so models and RAG pipelines improve continuously instead of stagnating after deployment.
I use this golden dataset as input for future fine-tuning, prompt/RAG tuning, and regression evaluations.
My leadership focus is building high-performing, psychologically safe teams through structured rituals and continuous learning.
I lead by example in using AI as a copilot, not a replacement. The goal is to automate boring, repetitive work so people can focus on creative problem solving, decision-making, and deep collaboration. I deliberately design workflows where AI handles low-value tasks (summaries, boilerplate, data prep) and humans own judgment, strategy, and relationships.
All significant architectural changes start with a Request for Comments (RFC). This ensures decisions are made collaboratively and prevents technical debt.
When incidents occur, I focus on system improvements, not blame. Post-mortems produce concrete action items and runbooks. This builds psychological safety and resilience.
I encourage engineers to have deep expertise in one area while maintaining broad knowledge. This is achieved through pair programming and cross-team collaboration, reducing bus factor.
I require a "Problem Statement" and "Alternatives Considered" section in every RFC. This forces us to validate the problem before jumping to solutions and explicitly evaluate trade-offs.