Applied AI for Sales and Revenue
The revenue cycle is where the most systems meet and the fewest of them agree. CRM says one thing, the quote says another, the contract says a third, and the ERP books a fourth. Each gap between them is manual reconciliation, and each reconciliation delays the trip from winning work to recognizing the revenue.
In short
Mach12 stands up AI labs that apply AI across the revenue cycle: proposal and quote generation, pricing, CRM data quality, pipeline forecasting, contract lifecycle management, and revenue recognition. The lab connects CRM, CPQ, contract, and ERP data so the revenue picture reconciles instead of arguing with itself.
Where Revenue Leaks
These are the patterns we see most. In our experience they come down to the distance between what the systems already hold and what anyone has had the hours to do with it, rather than to a technology gap.
Proposals take weeks of senior time
The content exists across past proposals, the solution library, and people. Assembling it into a compliant, tailored response is a manual build each time, and it pulls senior people off other work.
Pricing is inconsistent and slow
Deal pricing depends on precedent nobody can search, approval chains nobody can predict, and cost assumptions that live in a spreadsheet the approver cannot see.
CRM data quality undermines the forecast
Stages are stale, amounts are guesses, and close dates slip silently. The forecast is then produced from that data and defended as though it were observed fact.
Contract obligations stop being tracked at signature
What was promised on deliverables, service levels, reporting, and pricing is in the contract. What gets executed is in the ERP. Nothing systematically compares the two after the deal closes.
Revenue recognition is a manual reconstruction
Performance obligations, milestones, and percentage of completion are assembled by hand each period from sources that were not designed to answer the question.
Renewals and expansion are reactive
Usage, support history, and engagement signals that predict churn or expansion are spread across systems. The account team finds out at the renewal conversation.
What the Lab Builds
Built against your systems and your data, shipped into production with the people who use them. A given lab will build a subset of this, in whatever order discovery ranks it.
Proposal and quote generation
Assembling responses from what the company already knows, so the senior time goes into strategy rather than assembly.
- Solicitation and RFP shredding into a compliance matrix
- Draft response assembly from prior proposals and the solution library
- Quote and configuration generation with pricing guardrails
- Compliance and completeness checking before submission
Pricing and deal support
Precedent and cost truth available at the moment the price is being set.
- Precedent pricing retrieval across comparable won and lost deals
- Margin modeling against live cost and rate data
- Approval routing with the rationale assembled for the approver
- Discount and concession pattern analysis across the book
Pipeline and forecast integrity
A forecast built from observed signal rather than from what the field typed in.
- CRM hygiene agents that detect and correct stale or inconsistent records
- Forecast scoring against engagement and progression signals
- Deal risk flagging with the specific reason named
- Territory and quota analysis from actuals
Contract to cash
Keeping the promise and the execution connected after signature.
- Obligation extraction from executed contracts into trackable commitments
- Billing event and milestone monitoring against contract terms
- Revenue recognition support with performance obligation tracking
- Renewal and expansion signal monitoring across usage and service data
Typical First Builds
Chosen for speed to production as much as for value. We would rather have something working in your environment early than something more ambitious on paper.
- 01Proposal first-draft assembly for the most common response type
- 02Obligation extraction across the executed contract portfolio
- 03CRM data quality remediation on the fields the forecast depends on
- 04Precedent pricing search for the deal desk
Built Against What You Run
Connectors are built during stand-up. You do not replace anything first, and your data does not leave your boundary.
Relevant Accelerators
Applications we have already built in this area. A lab can deploy one as-is, extend it, or use it as the pattern for something new.
Common Questions
- Is this a CPQ or CLM product we are buying?
- No. If you need a CPQ or CLM platform, buy one. The lab builds the intelligence layer around whatever you run and fills the gaps between those systems, which is usually where the pain sits.
- How is AI-generated proposal content kept accurate?
- It is generated from your own approved content and your own system data, then reviewed before it goes out. The value sits in the assembly and the compliance checking.
- Can this work if our CRM data is genuinely bad?
- Yes, and CRM remediation is often the first build for that reason. It is a well-defined problem, the improvement is measurable, and what sits downstream of it improves at the same time.
- Do you have something already built for contract lifecycle management?
- Yes. Proposal to Cash is an application the lab has already built covering bid through billing through closeout. A client lab can deploy and extend it rather than starting from nothing.
Common in these industries
The work carries across industries. These are where we see it most often.
Build This in Your Business
Tell us what your sales & revenue function runs on and where the work is piling up. We will scope the first build.
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