Offerings
AI Agent Systems in Notion
Most "Notion AI" engagements stop at Q&A over your docs. That's not an agent system. That's autocomplete with permissions.
I build production multi-agent fleets that live inside Notion: a System Control Plane for orchestration, a Worker tools layer for execution, Dead Letter handling for the runs that fail, and a Governance layer that does the work most deployments skip — instruction version control, drift detection, and behavioral anomaly alerts.
Every agent has an owner, an instruction history, and a measurable output. ROI gets reported the same way you'd report on a hire. Alerts route to Slack, email, or Notion depending on severity. Nothing runs in a black box.
Fits teams already past the demo phase — content ops, research, data triage, lead routing, analyst workstreams — who need agents that operate reliably on real workflows and can be audited when they don't.Notion AI Strategy & Implementation
Notion AI is the most-deployed and least-measured AI investment in most companies. Teams turn it on, run a few prompts, and never quantify what it actually delivered.
I build the program that closes that gap. Use-case selection driven by impact, not enthusiasm. Prompt and skill libraries that codify how your team actually works. Evaluation harnesses so you know when a workflow is improving and when it's regressing. ROI measurement in dollars and hours, not vibes.
I also draw the lines: what AI should be doing inside your workspace, what it shouldn't touch, where humans stay in the loop, and how data exposure gets controlled across teams and tiers.
Built for Notion Business and Enterprise customers who want a defensible AI program — one you can show to a board, a security review, or a CFO.Workspace Architecture for Data-Heavy Teams
Spreadsheet-driven Notion breaks at scale. Relations that should have been foreign keys, views that should have been queries, automations that should have been jobs.
I architect Notion as the operational front-end to real data stacks — Supabase, Postgres, FastAPI, Next.js, external APIs — so the heavy data lives where it belongs and Notion does what it's actually good at: intake, review, dashboards, reporting, and orchestration for the humans.
Engagements cover database modeling, relations and rollups designed for scale, view strategy, automation design, and integration with custom backends through the Notion API, Custom Connectors, and webhooks.
Common builds: research operations, content and editorial systems, analyst workflows, data-quality pipelines, internal tools. Best fit for teams that need a workspace usable by non-technical people and trustworthy enough for production reporting at the same time.Software & Vendor Due Diligence
Most software diligence ends in a PDF that gets filed and forgotten. Mine ends in a Notion workspace your team keeps using.
The framework runs three stages: Technical Audit, Valuation Model, Report Assembler. The audit reads the codebase, the architecture, the security posture, the AI-generated and vibe-coded signals, the scalability story, and the team capability. The model scores it on a tiered scale with specific remediation paths. The assembler turns findings into live Notion databases — risks, recommendations, evidence — so they stay tracked instead of buried.
Use cases: M&A and investment diligence on SaaS targets, vendor selection for critical software, internal build-vs-buy audits, post-acquisition technical integration.
You leave with a verdict, the math behind it, and a workspace you can act on for months.Developer Enablement in Notion
Engineering teams are adopting Cursor, Claude Code, and Copilot faster than they're standardizing how to use them. The result is inconsistency — different prompts, different patterns, different output quality across the team.
I build the Notion-native dev environment that fixes that. Setup Templates, Agent Skills, and Reference Documentation databases that house coding standards, retrofit patterns for Python/TypeScript/Next.js/FastAPI/Bun, adversarial review checklists, Claude Code hooks, and prompt libraries your engineers actually use.
The workspace becomes the source of truth for how your organization writes software with AI in the loop. Onboarding gets faster. AI-assisted output gets more consistent. The knowledge stops living in individual heads and starts living in a system.
Built for engineering leaders who want AI tooling standardized across the team, not adopted ad hoc.
