Groundwork
What we sell

Three offers. Zero advisory bloat.

Forecast accuracy, NRR/GRR as defined, and reconciliation between GTM reporting and financial reporting, implemented directly in your stack, judged on whether the numbers hold up to someone outside the building.

A forecast built the way investors scrutinize one: a real weighted model on your pipeline data, stage exit criteria that are enforced rather than advisory, and variance tracked quarterly against a target band, reading the lifecycle of every deal, not a snapshot of the CRM.

What you get

  • A weighted, multi-method forecast model built directly on your pipeline data.
  • Enforced stage exit criteria with validation and historical snapshotting.
  • Forecast-variance tracking against a baseline measured from your own history, with a named owner and a target band.
What "done" looks like

Forecast variance holds under ~5%, leakage is tracked to the rep, and the numbers survive a board read without a spreadsheet rescue.

Finance-grade retention accounting: NRR and GRR computed from contract-level revenue schedules and reconciled to billing, not scored from CRM opportunities. One definition, one owner, and cohorts you can reproduce next quarter.

What you get

  • A written NRR/GRR definition: one definition, one owner, versioned and change-logged.
  • Contract-level churn and expansion cohorts reconciled to billing.
  • A board-ready metrics pack: bookings, ARR, NRR/GRR, churn and expansion.
What "done" looks like

One NRR. Finance, Customer Success, and the board deck produce the same number, and last quarter’s is reproducible from source data.

The bridge between GTM reporting and financial reporting: one ARR definition across Salesforce, billing, and the ledger, a standing reconciliation with a logged discrepancy register, and quote-to-cash guardrails so revenue enters the systems correctly in the first place.

What you get

  • An ARR bridge, CRM to billing to GL, with every dollar of the gap assigned to a named cause.
  • An exception register: every record that cannot be reconciled without a human answer, with the specific question attached.
  • A KPI glossary: what counts, where each number is sourced, and who owns its definition.
  • Quote-to-cash guardrails, including contract linkage enforced at closed-won.
What "done" looks like

Close happens in days, not weeks. Cross-system ARR variance is inside 3% at hand-off and on a governed path to under 1%, with every dollar traceable from GTM action to bank statement.

Not a fourth offer; the layer underneath the other three. Every engagement leaves your data governed for what comes next: the schemas, policies, and human-in-the-loop checkpoints that make revenue data safe to automate on.

What you get

  • Structured GTM data dictionaries defining every parameter.
  • Data ownership boundary maps and strict LLM compliance policies.
  • Agent audit trails and least-privilege access, so every automated action is attributable.
What "done" looks like

A unified semantic layer sits under your CRM. Agents act safely, and every action they take is auditable.

Before & After

Your data stack, diagnosed.

Toggle to see what Groundwork actually fixes. Same tools, same vendors, different architecture.

HubSpot CRM
⚠ customer_name
⚠ acct_id (text)
✗ deal_amount (USD)
⚠ close_date (MM/DD)
Schema mismatch
Stripe Billing
⚠ cust_name
⚠ account_id (int)
✗ charge_amt (cents)
⚠ created_at (epoch)
Type conflict
GL / Ledger
⚠ ClientName
⚠ AcctNo (varchar)
✗ revenue (decimal)
⚠ period (YYYY-Q#)
Data validation failing: 47 field conflicts detected
The layer underneath

AI is great at cleaning the CRM.

It's bad at deciding what belongs in the CRM in the first place. Groundwork covers the second thing: the policies, data dictionaries, and human-in-the-loop SOPs that decide what agents touch and when.

01

Agents inherit your data hygiene. An AI deal-flagging agent on a dirty CRM produces dirty deal flags.

02

Governance before adoption. Data ownership, consent, IP, and review SOPs documented up front. Cheaper than rolling them back after a compliance incident.

03

Vendor selection without the demo theater. Pre-qualify against your stack, score on integration cost, run the proof-of-concept in your sandbox.

04

Shipped, not pitched. Every recommendation here is a system Groundwork has deployed. No tools we haven't run.

05

One identifier across product, CRM, ERP, and billing. Agents that can't reconcile your accounts can't act on them.

Who builds this
Dan Buljan, founder of Groundwork

Every engagement is run by the person who codes it.

Most firms sell this work with a partner and staff it with analysts. That split survives a dashboard project. It is fatal for reconciliation, where the person asking why billing disagrees with the ledger has to be the same person who can read the field history and change the configuration. The handoff is where the answer gets lost.

Dan Buljan has spent 15+ years inside Lead-to-Cash, Quote-to-Cash, and finance systems for Series A–D SaaS: forecasting held under ~5% variance for 12 straight quarters, an enterprise Salesforce CPQ, CLM, and billing deployment for Google (alongside a Big Four systems integrator and the CPQ platform's product team), and nine years at Accruent from employee #8 as it scaled ~$5M→$100M+ ARR through three M&A integrations.

You work with the person who writes the code and the data dictionaries. There is no account manager between you and the build.

How engagements work

Priced up front, credited forward.

Everything above is fixed-fee, scoped from a scored assessment rather than a proposal from a stranger, and the assessment credits in full toward the sprint it recommends.

See where your numbers would get flagged before you scale.

Twelve questions across forecast accuracy, retention integrity (NRR/GRR), and cross-system reconciliation. No email required.