A decade of data engineering and compliance reporting inside one of the four largest U.S. banks — now applied across finance, security and compliance, AI governance, and operations. The through-line is the firm itself: illuminating data, empowering decisions. Every project below is built and operated rather than theorized.
Most AI consultants advise on slides. This work is the opposite: a running governance control plane, production financial and compliance dashboards, and live agents — each built and operated. The common thread is configuration-management discipline carried over from regulated enterprise environments and turned into systems a team can actually run and decide from.
A production platform that merges Stripe and QuickBooks into a single financial-reporting surface, with the client pipeline and financial KPIs in one view.
Outcome. Replaces manual spreadsheet reconciliation with one governed view of revenue, pipeline, and cash — the numbers a decision actually runs on.
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Compliance analytics over CIS benchmarks: coverage across products, rule search, and drift and gap tracking against a library of 11,000+ security rules.
Outcome. Turns a sprawling benchmark library into a coverage-and-gap view a team can act on, instead of a PDF no one reads.
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Prism Risk Intelligence & Security Management Advisor: an AI analyst layered on the baseline data that answers compliance questions and cites the underlying CIS rule IDs so answers can be verified.
Outcome. Makes the benchmark library conversational and auditable — every answer is traceable to a cited rule, not an unsourced guess.
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KRI/KPI analytics for AI agents: inventories every governable surface (agents, skills, hooks, integrations, scheduled tasks), diffs the live environment against an approved baseline, and renders drift reports and audit-ready attestations.
Outcome. Answers the question observability cannot — was this agent ever authorized to do what it did — with evidence an auditor accepts.
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A memory-backed strategic agent that runs across four operating modes, governed under an approved CM-AI baseline (defined model, tool inventory, and KRI thresholds) — the governance framework applied to a real production agent.
Outcome. Demonstrates the platform on its own dog food: a live agent operating inside the same baseline-and-drift controls the firm builds for clients.
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A daily orchestration routine plus a mission-control inbox and decision tracking that surfaces what needs a human call and routes the rest.
Outcome. Turns scattered operational signals into a single daily view of what is in flight and what is waiting on a decision.
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A pipeline that takes source research and produces a published article, audio, and infographic in under a day.
Outcome. Collapses research-to-publish from days to under a day, keeping a steady cadence of published work.
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An end-to-end onboarding pipeline: a new request auto-provisions a client workspace and a warm, human-first welcome; a six-dimension AI Readiness Assessment scopes the work; and any payment opens a gated client portal — with a person leading every client touchpoint.
Outcome. Turns a scattered, manual intake into one governed lifecycle — evaluate, adopt, deliver — where automation handles the scaffolding and a person is never replaced at the client's door.
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An internal ledger that records every external source pulled during research — its date, the claim it supports, where it's cited, and a verbatim quote — with a trend view of where the firm's research attention is concentrating over time.
Outcome. Makes every stat in a brief or blog replayable to its source, and turns scattered reading into a signal of where attention is moving — provenance and trend in one governed view.
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A growing body of published articles establishing configuration management for AI agents (CM-AI) in the open.
Outcome. Establishes the framework publicly — proof the thinking is in the open, not a private pitch deck.
View case study →Vendor-neutral and integration-oriented — extending the tools you already run, against the frameworks your auditors already use.
The clearest way to start is a defined, time-boxed engagement — a governance assessment, an analytics modernization sprint, or a compliance build. Tell me where you are and I will scope from there.