Your deals already tell you what to charge. If you read them.

The invisible cost of pricing by instinct

Most founders set pricing early: a few advisor conversations, a glance at competitors, and a number that feels reasonable. That number becomes the anchor for every proposal and call, adjusted only by feel.

Gut-feel pricing has no feedback mechanism. When a prospect accepts in under an hour without a question, that is a win. It is also a signal you left money on the table. When a deal dies after two weeks of negotiation, your CRM records "lost" without distinguishing price from packaging from structure.

Delivery costs compound the problem. You quote at a healthy margin, but actual hours run 40% over estimate. Contractor costs land higher than planned. Founder time never gets tracked. The deal that looked profitable broke even. You find out months later, if at all.

The data to fix this already lives in your CRM, call recordings, and project management tool. Unconnected.

How quarterly pricing analysis works

We build a system that runs quarterly, pulling data from across your stack and producing actionable pricing intelligence.

Deal data extraction. The system pulls every deal from the quarter out of your CRM: won, lost, and in limbo. For each one it captures engagement type, scope, quoted and accepted price, deal velocity, and the full negotiation arc. Call transcripts fill in what CRM fields cannot: how the prospect reacted, what they compared you to, whether they pushed back or signed immediately.

True margin calculation. The system matches each deal against delivery data from your project management tool: actual hours, contractor invoices, and a reasonable allocation for founder time. Revenue minus all costs produces a real margin figure per engagement type. This is where surprises live. The engagement type you thought was most profitable often is not, once scope creep and untracked hours are accounted for.

Market signal analysis. The AI layer reads call transcripts for pricing-relevant signals: competitor mentions and their quotes, the price range prospects expected, and whether they anchored high or low. These signals aggregate across the quarter to show where the market sees your price relative to alternatives.

The speed heuristic. Deals that close in under 24 hours with no negotiation are almost certainly underpriced. Deals that close after a week or two of discussion are in the right range. Deals that stall beyond two weeks have hit a ceiling. Deals that die on price suggest the number is too high or the packaging does not match what the buyer wanted.

Margin data adds a second lens. Margins consistently above 60% mean you are leaving revenue on the table. Below 30% means delivery efficiency needs attention. Neither finding is visible without connecting deal data to delivery data.

What the system produces

Each quarterly cycle generates a concrete set of outputs.

Price adjustment recommendations per engagement type. Where data supports a price increase, the system specifies how much and cites evidence: instant-acceptance clusters, above-target margins, competitor pricing that positions you below market.

Packaging analysis. Sometimes the price is fine but the offer is shaped wrong. The system identifies which scope elements buyers treated as essential in winning deals and which rarely influenced the decision. Tiering recommendations come with deal evidence behind them.

Deal-level retrospectives. Specific deals where signals pointed to higher price tolerance than what you quoted, framed as learning opportunities for pattern recognition.

Updated pricing model. Ranges per engagement type. Guidance on when to price at the top of a range versus the middle. Indicators to watch during sales conversations that signal where a deal should land.

What compounds quarter over quarter

The first cycle often produces surprises. Founders discover their most-quoted engagement type has the thinnest margins, or that a segment of deals closed so fast they were underpriced by 20% or more.

Those are one-time corrections. The compounding happens across multiple quarters. The dataset grows. Seasonal patterns emerge. The relationship between deal velocity and optimal price point becomes clearer with each cycle. Your team starts recognizing pricing signals during live calls because the reports have taught them what to listen for.

By the third or fourth quarter, prices are grounded in data, packaging reflects how buyers actually buy, margins are visible at the deal level, and every analysis makes the next one sharper.