You don't need more AI. You need to know which of it moves revenue.
Every commercial leader is under pressure to do something with AI, and the market is glad to sell forty tools for it. We start somewhere else: with the commercial operation you already run — how you sell, what you charge, how you keep customers — and find the few places AI demonstrably moves the number. Then we build those, prove them on live deals, and leave the rest on the shelf. The people doing this have built, sold, priced, and deployed AI inside commercial functions themselves — not advised on it from a deck.
Five places revenue leaks — and AI plugs
None of these is a technology problem. Each is a commercial process no one has engineered, where AI pointed at the right step pays back fast.
Leads go cold before anyone calls back
Inbound interest has a half-life measured in minutes. Most teams answer in hours or days, across timezones, with uneven quality. An AI agent that qualifies and responds in seconds — then hands a warm, briefed prospect to a human — is among the fastest returns available, and one of the easiest to prove on live traffic.
Quotes and RFPs crawl
In deal-driven industries, the proposal is the product. When it takes five days and three departments to answer an RFQ, you lose to whoever answered first. AI trained on your past proposals, pricing, and product data drafts faster and more completely, and learns from what won — turning the slowest step in the cycle into an advantage.
Pricing is left to instinct
Most companies cannot say which prices won and which left money on the table. Discounting is habit, not strategy. AI on your own deal history surfaces what the market actually pays, where you under-charge, and where a firmer number would have held — the highest-margin change in the whole commercial operation.
Deals die quietly
A deal goes silent and no one notices until the forecast slips. AI watching the signals across email, CRM, and calendar flags a stalling deal before the rep does, and tells you which ones are real — so the forecast reflects behaviour, not optimism.
Churn shows up on the renewal date
By the time a customer gives notice, the decision is months old. The signals were there earlier — usage, tone, response times. AI reading them flags an account at risk while you can still act, and surfaces the expansion cues that turn a won customer into a growing one.
How an engagement works
Revenue AI Audit
Four weeks inside your commercial operation. We interview the team, shadow the quote and onboarding flows, and map every customer touchpoint. You leave with a prioritised, vendor-neutral assessment of where AI moves revenue, where it does not, and what to buy, build, or leave alone.
Fractional AI Lead
A partner embedded in your commercial leadership for 6 to 12 months — in the deal reviews, designing and deploying AI where it pays, accountable for revenue impact rather than slideware. A leader who builds, not a consultant who reports.
Build & Deploy
The agents and systems themselves — speed-to-lead, RFP and quote acceleration, pricing intelligence, churn signals — built into the process you already run and proven on live deals before they scale. Independent of any vendor: the right tool, configured or built, on the merits.
Advisory Retainer
For teams with AI deployed and running. Monthly sessions with your commercial leadership, current intelligence on what is working in your sector, and a senior voice to pressure-test the next move before you commit the budget.
Five questions before you spend a dollar on AI
Most teams discover mid-project that they cannot answer the third. That discovery is expensive.
If any of these is genuinely hard to answer, that is where we start.
Can you name the three points in your sales cycle where deals most often slow or stall — precisely enough to act on?
Which systems hold your deal, quote, and customer data — and who owns each one?
Can you say what a good deal and a good price look like, in terms specific enough that AI could flag a bad one without a human checking every case?
Has your leadership agreed what AI handles on its own versus what triggers a human — and is that written down?
Who owns the result for the next ninety days, and do they have the authority to change a process when the system needs it?