Groundwork
Results

The numbers, with the story behind them.

Not dashboards delivered or hours billed. Measurable change in the number, and the systems work that produced it. Every engagement below is anonymized: every client gets the same confidentiality, during the work and after it.

The bar

What the work is judged on.

Four outcomes that repeat across engagements, which is the part that matters. One good quarter is luck.

Forecast variance
under 5%

12 consecutive quarters · most recently 4% · Series A→C · single operator engagement

Forecast variance, repeated
~30%→~5%

Second company, same method · it wasn't the company, and it wasn't luck

New revenue motion
$0→$20M ARR

Series B · full ICP & product pivot · under 24 months · zero net GTM headcount added

Board reporting
weeks→same-day

Reporting cycle cut from a weeks-long scramble to same-day · repeated at every engagement

Selected engagements

Each one started with a broken system and ended with one the team could run.

The problem as it actually presented, what was rebuilt, and what the number did afterward.

under 5%Forecast variance · 12 straight quarters

A forecast the board plans against, not around

Growth-stage B2B SaaS · Series A→C · Salesforce
Challenge
The quarter before the engagement, one large deal, badly weighted in an unweighted mid-market pipeline, blew up the model for a ~70% miss. A modeling problem, not a people problem, and one most companies this size have today. Pipeline lived in three disconnected systems, so every board meeting opened by arguing which number was real.
What we built
Re-architected the CRM around enforced stage gates, replaced the competing spreadsheets with one weighted forecasting framework built to catch single-deal concentration, and wired pipeline into a single source of truth with a weekly inspection cadence the CFO owned.
Result
Forecast variance has held under 5% for twelve consecutive quarters, most recently 4%. The number stopped being a negotiation and became the plan the board and the operating team worked from.
$0→$20MNew-motion ARR

A full ICP and product pivot, operationalized

Series B B2B SaaS · MRR → usage-based · Salesforce ↔ Stripe ↔ NetSuite
Challenge
The go-to-market motion was built for a buyer the company had outgrown. Systems, comp, and reporting all pointed at a market that had moved, and the shift to usage-based pricing broke the old billing and ARR logic.
What we built
Rebuilt quote-to-cash, territory, and comp around the new ICP, migrated MRR to usage-based billing, and reconciled Salesforce, Stripe, and NetSuite so the new motion reported ARR the board could defend from day one.
Result
From $0 to $20M in new-motion ARR in under 24 months, with zero net GTM headcount added, roughly $800K in revenue per GTM full-time employee, on systems built to scale past it instead of being rebuilt at the next stage.
<1%Cross-system ARR discrepancy

Production AI data infrastructure, governed end to end

B2B SaaS · Salesforce ↔ Stripe ↔ NetSuite · in production
Challenge
Every agentic workflow failed on the same foundation: no schemas, no governance, and ARR that never reconciled across CRM, billing, and the GL. Each AI pilot inherited the discrepancy and acted on it faster than anyone could catch it.
What we built
Built the data dictionaries and semantic layers, then a governed Salesforce + Claude (MCP) integration with OAuth and Python guardrail middleware, so agents read one trusted definition of every number and every action they took was logged and reversible.
Result
Cross-system ARR reconciles to under 1% discrepancy, finance-defensible and in production, with AI agents running safely on top of it.

Two decades of revenue and finance systems work for companies like these

Delivered through operating and consulting roles

GoogleNissanToyotaAppleTeslaCoca-ColaHoneywellMcKessonVerizon

See where your own numbers would get flagged.

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