Sales has one pipeline number. Finance has another. The dashboard shows a third. We build forecasting models and reporting systems that turn scattered CRM, marketing, and finance data into one governed number, before the board asks you to defend it.
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A breakthrough in forecast accuracy. Built for commercial use.
Sales has one. Finance has another, built in a spreadsheet nobody else can open. The dashboard shows a third. By the time it reaches the board, someone has quietly reconciled the differences by hand, and you're the one who has to explain which number is real.
There's no standing model, just a spreadsheet someone reassembles before every forecast call, using whatever export they could pull that morning. Accuracy depends on one person's discipline, not a system.
Marketing's influence on the forecast gets asserted in a QBR slide, not measured in the model. When budget gets questioned, there's nothing to point to except attribution that falls apart under scrutiny.
Commit numbers come from gut feel on a call, not from stage conversion or deal velocity. Some reps sandbag, some oversell, and the roll-up just averages out the noise, then misses by a mile.
Most RevOps and Finance teams we meet are reconciling three separate versions of the same pipeline number before every leadership review. None of them agree, and nobody owns the difference.
The Method
Before we build anything, we find out why the numbers disagree. We map every system, spreadsheet, and definition feeding your current forecast, CRM, marketing automation, finance models, and identify exactly where and why they diverge. Most of the "forecasting problem" turns out to be a data-agreement problem hiding underneath, and until that gets named, no model will hold.
We design the model to match how your team actually sells. Stage-based, velocity-based, or a blended approach, built and calibrated against your historical conversion and deal data, not a generic framework. We test the model against past quarters before it ever touches a live forecast, so you know where it would have been right and where it would have missed.
We connect the systems so the number only has to be assembled once. CRM, marketing automation, and finance systems get connected into a single reporting layer, with standardized fields and definitions enforced at the source, not patched together in a spreadsheet after the fact by whoever drew the short straw.
We build the dashboards your teams will actually use. Executive, RevOps, and team-level views, each scoped to what that audience needs to see and act on, refreshed automatically on a defined cadence tied to your existing review rhythm. The dashboard shows up when the meeting starts, not when someone remembers to refresh it.
We stress-test the model before you present it. The forecast gets run against several historical quarters to check where it would have been right and where it would have missed, then we document ownership, update rules, and escalation paths so the system stays trustworthy after we leave, not just on launch day.
We make sure accuracy doesn't depend on us. RevOps, Sales leadership, and Finance stakeholders are trained to run, maintain, and adjust the system as your business changes, so the forecast survives a team change, not just a quarter. You get a reference playbook, not a phone number to call when something breaks.
Six work streams. Every deliverable ships as a model, a dashboard, a governance rule, or a trained operator, not a recommendation.
The data-agreement layer. Every source of truth mapped, every conflict named.
The forecasting logic itself. Stage-based, velocity-based, or blended, calibrated to you.
The pipes. CRM, MAP, and finance connected into one governed data layer.
The surfaces the business actually reads. Scoped by audience, refreshed on rhythm.
The trust layer. Backtested, owned, documented, so the model doesn't decay after launch.
The survival layer. Your team can run the model without us in the room.
A 30-minute call. We'll look at your current forecasting and reporting setup and show you exactly where the numbers disagree, and what fixing it would take. No deck.
Every source feeding your current forecast, and exactly where they disagree. The shared baseline for what fixing it actually takes.
Typical engagement: model and infrastructure in 8–12 weeks. Governance and training close it out.
Patchstack, an open-source vulnerability intelligence company, had never configured forecasting or reporting in HubSpot. The instance existed, but it was barely used. Over roughly three months we cleaned up the CRM, set up lead scoring and a product library so revenue could be attributed by offering, and built custom quarterly MRR/ARR forecasts, weighted-pipeline analysis with stage-based multipliers, and deal-velocity tracking by stage. Those reports now feed real-time executive dashboards used directly for board reporting, and the team runs the whole system without us.
Growth-stage and mid-enterprise B2B companies, $30M–$500M ARR, with at least 12–18 months of deal and retention data. SaaS, technology services, professional services, fintech, healthtech, energy/sustainability. Especially those whose win rates vary wildly by segment and who can't explain why.
Most $30M–$500M ARR companies live at Stage 2 and believe they're at Stage 3. They have a dashboard. Nobody trusts the number on it.
The forecast lives in someone's personal file. It gets rebuilt by hand every cycle, and it dies with whoever built it.
Most companies start hereSales, Marketing, and Finance each have their own number. All three call it "pipeline." None of them mean the same thing.
The data lives in one place, but someone still has to pull it, clean it, and assemble the forecast by hand every week.
Definitions, cadence, and logic are standardized and run without a person in the loop, and everyone knows who owns accuracy.
Forecasting ships you here
The same forecast shows up in the CRM, the board deck, and the finance model, because it's the same underlying data, not three separate exports someone reconciled at 11pm before the meeting.
Stage conversion and deal velocity replace gut-feel commit numbers, calibrated against your own historical data, not a generic template that assumes a sales motion nothing like yours.
When the forecast misses, you can point to which stage, segment, or rep drove the gap, instead of shrugging at the board and promising to look into it before next quarter.
Dashboards refresh on a defined schedule with clear ownership, so forecast accuracy doesn't depend on one person's spreadsheet discipline or their vacation calendar.
No more three-day scramble reconciling exports before a leadership meeting. The number that goes in the deck is the number that's already in the system.
Documented definitions, governance rules, and training, so the model doesn't decay the quarter after the engagement ends. Accuracy survives a team change, not just a quarter.
Governed, not frozen: we build the model, ship the reporting layer, and set the cadence for keeping it accurate as the business changes.
We'd rebuilt the forecast three times before. The difference this time was that it stopped being a spreadsheet and started being a system. Sales, Finance, and the board finally looked at the same number, and the reconciliation meetings just stopped happening.
A 30-minute call. We'll look at your current forecasting and reporting setup and show you exactly where the numbers disagree, and what fixing it would take. No deck.
The questions RevOps, Finance, and Sales leaders ask us on the first call, answered directly.
No. In most cases we work within your existing CRM and reporting stack. We may recommend configuration changes or additional integrations, but a platform switch is rarely necessary, and we'll say so if it is.
That disagreement is usually the actual problem we're solving. Part of the engagement is aligning stakeholders around shared definitions and a single methodology before we build anything. Skipping that step is why the last forecast rebuild didn't stick.
No. We focus on revenue forecasting and reporting infrastructure. Your FP&A team keeps ownership of financial planning, working from more reliable revenue inputs than they had before.
At least one full closed cycle of CRM data is ideal for calibration. Less than that, and we'll build a directional model and refine it as more data comes in, we'll be explicit about the confidence level either way.
Yes. Many clients move into an ongoing RevOps retainer to keep the model current as the business evolves, new segments, new motions, new stages. The governance playbook is designed to make that lift small.
Structure and reconciliation typically take 4–6 weeks, with the full reporting layer and governance playbook complete by week 8–12, depending on data complexity and how many source systems are involved.
The RevOps Audit diagnoses the full GTM operating model, what's broken across systems, definitions, and processes. Forecasting & Reporting is the build. Many clients run the audit first, but neither strictly requires the other.
CRM access (ideally read/write in a sandbox), marketing automation and finance data exports, historical closed-won/lost data, and executive alignment on the definition of pipeline before we start building.
Then you found it from a calibrated model instead of a missed quarter, which is the cheapest way to find it. The point of validation is to see it before the board does.
