Fractional Data & AI Workflow Engineering
Six offerings across data infrastructure and AI workflow architecture.
Purpose-built data pipelines that replace fragile vendor syncs with infrastructure you own and control.
When off-the-shelf connectors fail or cost more than they're worth, a custom pipeline is the answer. I design and build ETL/reverse-ETL pipelines using dlt, DuckDB, Snowflake, and direct API integrations — scoped to your exact data flow, with monitoring and alerting built in.
Teams whose vendor sync tools are too slow, too expensive, or too inflexible. Companies that need data flowing between systems on their terms, not the connector's.
Proof of work: "How We Reduced CRM Data Latency by 96%" blog post
Config-driven Snowflake access control — AI-assisted interview, deterministic Terraform deployment.
An AI-assisted CLI interview captures your org's access requirements and generates a declarative config. Terraform deploys it. The AI never touches your Snowflake account — deterministic infrastructure-as-code with an AI intake layer.
Teams that have outgrown "everyone uses ACCOUNTADMIN" and need real governance before their next audit, security review, or compliance milestone.
Proof of work: fds-snowflake-governance-blueprint repo (Apache 2.0) | Blog post
Find out what you're overpaying for and what you're missing — with a clear path to fix it.
A 10-query Snowflake workload audit profiles your environment across query patterns, warehouse utilization, refresh cadence, and DuckDB compatibility. The Evidence BI report shows where credits are going and recommends whether to optimize, route to DuckDB, or right-size the entire stack.
Teams spending $2K–$10K/month on Snowflake and wondering if it's justified. Teams with excess credits rolling over and no plan for deploying them. Teams building their first analytics stack.
Proof of work: fds-snowflake-cost-audit repo (Apache 2.0) | BI-in-a-Box (repo coming soon)
A platform-agnostic knowledge layer your team and AI agents can read from any client.
Your decisions, meeting notes, and context persist across sessions and across AI providers. No more re-explaining context every conversation. Your memories follow you from Claude to ChatGPT to whatever comes next.
Founders and small teams tired of re-explaining context every AI session. Teams building AI-powered products that need persistent, queryable memory.
Proof of work: "The Platform Is Catching Up" blog post
An automated morning email that compiles decisions, action items, and open questions across all your work streams.
A GitHub Action collects overnight activity from your data sources, Claude formats it into a decision-ready brief, and it lands in your inbox before you wake up. Reply with your decisions and they're captured and executed. Scannable in 5 minutes.
Solo founders, fractional executives, and anyone managing multiple concurrent work streams who needs a single-pane triage every morning.
Proof of work: CEO Agent architecture post
A structured audit of your AI workflow before it hits production — failure modes, human checkpoints, and total cost at scale.
Describe your AI workflow, and the assessment engine scores it across six failure types and five risk dimensions. The output is a report your engineering lead can take to stakeholders to shift the conversation from "is this safe?" to "here's exactly what could go wrong and what we're doing about it."
Engineering leads shipping AI-powered features who need to answer "is this safe to put in front of customers?" Teams whose AI prototypes stalled between demo and production.
Proof of work: fds-ai-workflow-audit repo (Apache 2.0) | "Your AI Prototype Is the Easy Part" blog post
Production-quality visual documentation of your AI workflows that non-technical stakeholders can understand.
Animated architecture diagrams and visual documentation using Remotion and programmatic video. Explain how your AI system works to investors, customers, and new hires without requiring them to read code.
Product teams, marketing teams, and founders who need to communicate "how our AI works" to people who don't read code.
Proof of work: "Your AI Prototype Is the Easy Part" blog post (video demos)
Available for fractional retainer engagements and project work. Typically 2–3 new clients per quarter.