The operating layer behind your forecast, your finance, and the AI agents that run them.
Most B2B SaaS teams scale finance ops by stacking headcount. Spreadsheets reconcile what the systems should reconcile, the forecast misses by a margin nobody trusts, and the AI agent layer that should compound the work never gets shipped. We build the systems instead, then sit inside your team to run them.
Six things every embedded engagement ships
Pipeline-to-forecast model
Weighted model tied to stage exit criteria, qualification methodology, and decision-authority signals. Defensible to a CFO and a board, not just a sales kickoff slide.
Stale pipeline diagnostic
The hygiene pass that strips deals inflating your number. Recurring, automated, runs before every forecast call. See how we run it →
Invoice-to-cash automation
Quote → contract → billing → collections, wired between CRM and finance system (Stripe, Chargebee, NetSuite, or your stack). Clean revenue recognition as a byproduct, not a quarterly fire drill.
AI agents for repetitive ops work
Deployed agents handling invoice chasing, contract data extraction, deal-close handoffs to finance, monthly close prep. You own the agent; we ship, monitor, and expand it.
CFO-ready reporting layer
Forecast accuracy percentage, deferred revenue waterfall, NRR and GRR, board metrics. Finance and revenue reconciled in one source of truth, not two systems with two numbers.
Forecast cadence + governance
The operating ritual: weekly forecast call, monthly close, quarterly board roll-up. Cadence designed so the model gets used, not parked next to the last consultant's deck.
Discovery → Design → Build → Launch → Optimize
Forecast accuracy audit (last four quarters), finance and revenue data map, AI agent opportunity scan, stakeholder interviews across CEO, CFO, RevOps, and sales leadership.
Forecast model architecture, finance automation flow, agent specs and scope, reporting layer schema, cadence design. Approval gate before any build.
Ship the model in your CRM, wire finance automation between systems, deploy agent v1 against the highest-pain workflow, dashboards live, runbook drafted.
Train the team, run the first forecast call together with you in the room, hand over the operator runbook, validate the agent against real workload.
Weekly check-ins for the first 90 days, model recalibration against actuals, agent expansion to the next two or three workflows, KPI tracking against baseline.
How this lands in your motion
Forecasting, finance, and AI ops mean different things depending on how revenue actually enters your business. Here's what we ship in each.
PLG
Your product generates the revenue but the forecast can't see the product. Finance reconstructs usage-based revenue in spreadsheets. Expansion ARR shows up after the fact, not before.
What we ship
- Usage-signal forecast model, leading indicators from product instrumentation rolled into a 30/60/90-day revenue forecast
- Subscription + metering layer: clean revenue recognition across self-serve, usage-based, and tiered pricing, no manual reconciliation
- Activation + expansion agents: monitor in-product behavior, route the right accounts to sales-assist, catch churn risk before it lands
What's different
We embed alongside product analytics and finance, not just RevOps. The forecast is built off product-side data, not CRM stages.
Sales-Assist
You're running PLG and a sales motion in parallel. The two forecasts don't reconcile. PQLs leak. Sales spends time on accounts that would have converted self-serve anyway and misses expansion plays in the existing book.
What we ship
- PQL scoring and routing model: which self-serve signups get human touch, which don't, and the math behind the threshold
- Two-track forecast: self-serve ARR, sales-assisted ARR, and expansion broken out cleanly, reconciled to one number
- Sales-assist agents: account research and brief generation for PLG-to-sales handoffs, expansion-trigger alerts, in-tier upgrade automation
What's different
The agent layer handles the volume so your sales team only sees accounts where humans add value. You stop running two GTMs in two systems.
Sales-First
Long cycles, multi-stakeholder deals, big invoices, custom contracts. Forecast accuracy is a coin flip. Sales lives in CRM, finance lives in NetSuite, nobody's reconciling. Reps spend hours on call prep and contract extraction that an agent should handle.
What we ship
- Weighted forecast tied to MEDDPICC or MEDIC: exit criteria that match how your sellers actually work, not how the methodology slide says they should
- Contract-to-cash automation: CPQ, billing, AR, revenue recognition wired deal-by-deal between CRM and finance
- Rep and AE agents: deal call prep packets, meeting summarization with risk flags, contract data extraction into the deal record
What's different
We stay in the forecast call with you for the first quarter: not just shipping the model, but operating it alongside you until it's defensible to the board.
Three engagements, three motions, three outcomes
Series B B2B SaaS, fintech vertical
Forecast model rebuild + finance and revenue reconciliation under board scrutiny.
Forecast variance cut from ±28% to ±8% over two quarters.
European B2B SaaS scaling $5M → $20M ARR
Stale-pipeline diagnostic + qualification methodology + weighted forecast.
60% of pipeline killed before first board forecast call; ±10% accuracy held three quarters.
Open-source PLG dev-tools company
PQL scoring + self-serve-to-sales routing + activation agents.
92% of self-serve signups routed correctly in week one; sales headcount flat through 3× ARR growth.
Where this lands hardest
Series A/B SaaS, $5–30M ARR
The point where headcount stacking can't keep up but systems debt is already real.
PLG companies adding a sales motion
Self-serve works. Adding human touch shouldn't break the forecast or the unit economics.
Sales-first companies under CFO scrutiny
Long cycles, big deals, finance and sales running on different numbers, and the board wants one number.
VC platform teams supporting portfolio finance ops
Repeatable forecast and finance ops infrastructure across multiple Series A/B companies.
Four ways to work with us on this
Fractional / Embedded
Operator-on-rotation. Mon–Thu inside your team for 90 days, monthly cadence after.
Project-based
90-day forecast + finance + agent v1 build, fixed scope, fixed outcome.
Retainer
Monthly optimization, agent expansion, cadence governance.
Workshop / Training
Forecast call rebuild, finance and revenue reconciliation training, agent ops handoff.
Your forecast is a system. Build it like one.
The 90-day operator engagement: forecast model, finance automation, agent v1, embedded delivery.
Book a forecast & finance ops review