AI practice

Most AIprojects don’tfail on thetechnology.

They fail because the problem was never defined precisely, the process wasn’t cleaned up first, the wrong tool was bought, and nobody took ownership of the deployment. We do.

The company you call to figure it out and get it done.

We deploy AI into the commercial and operational functions we know deeply: procurement, marketing, revenue operations, customer operations, and management reporting. We identify where automation produces a measurable outcome, choose the right tool for it, and build it. Every engagement ends with a running workflow and a number, not a presentation.

We are independent of every model and platform vendor. We have built tools, agents, and workflows on the major providers and on the smaller specialist ones, and we benchmark them against each other for the job in front of us. That independence matters more each quarter: the market moves fast, the tool you bought last year is rarely the best one today, and most companies are paying for overlapping licences, redundant partners, and pilots that never reached production.

Executives do not need another AI strategy. They need someone they trust to do the right thing, cut through the noise, and get it done. That is the practice.

60 to 90Days to a first production workflow, adoption included
30 to 40%Of revenue operations time typically spent aggregating data by hand before automation
0Vendor relationships that influence which tools we recommend
01

AI Opportunity & Readiness Assessment

We map the highest-volume, highest-cost manual workflows across the functions in scope, assess data quality and system readiness, and produce a prioritized business case with an ROI estimate per use case. We also tell you what not to do yet. The output is a sequenced plan with owners and measures, not a list of possibilities.

02

Tool, Vendor & Stack Selection

Most companies now carry several AI subscriptions, a few pilots, and at least one partner whose value is unclear. We inventory what you have, benchmark the options for each workflow on cost, capability, security, and fit, and recommend what to keep, consolidate, replace, or cancel. Where a build makes more sense than a licence, we say so. We negotiate the vendor terms as part of the work.

03

Workflow & Agent Build

Contract and vendor agreement review at scale, agency scope compliance, campaign reporting, pipeline and forecast intelligence, customer operations triage, finance close support. We build the workflow, connect it to your systems, test it against real work, and hand it over running. Where agents touch multiple systems, we sequence them after the first measurable wins so the foundations hold.

04

Governance, Cost & Risk

AI spend is now a line item that grows quietly, like cloud did. We put usage and cost controls in place, define the data and security policy with your IT and legal teams, set vendor terms that protect your data and your position, and build the reporting a board expects: what is deployed, what it costs, what it returns, and what the risks are.

05

Automated Management Reporting

We build workflows that pull from existing sources and produce board-ready outputs on schedule. Finance and operations get the same numbers every month without the manual assembly, leadership gets them earlier, and the errors that come from re-keying disappear.

06

Adoption & Change Management

Training, workflow redesign, and the management practices that make new tools part of how the business runs. We measure adoption and outcome, not deployment, and we stay until the numbers hold without us.

Most AI projects never reach meaningful production. The ones that do share three things.

Operator ownership, a precisely defined use case, and a measurable outcome tied to a business function. That is how we run every deployment, and it is why we start with the process and the metric rather than the tool.

Common questions

Straight answers.

Which AI providers do you work with?

Whichever fit the job. We are independent of every model and platform vendor, and we regularly find that the tool a company already pays for is not the best one for the workflow in front of it. Selection is part of the engagement, and we benchmark the options on cost, capability, security, and fit before anything is built.

Which functions benefit most from AI first?

Procurement and contract review, marketing operations, revenue operations reporting, customer operations, and management reporting. These have high volumes of repetitive, judgment-heavy work and clear before-and-after measures.

How long does an AI deployment take?

An opportunity assessment takes two to three weeks. A first production workflow typically runs within 60 to 90 days, including adoption and training. Agentic workflows that touch multiple systems take longer and are sequenced after the first wins.

Is our data safe?

Client data stays within the client’s own environment and accounts. We do not train models on client data, we prefer enterprise-grade providers with contractual data protections, and every deployment is scoped with your IT and legal teams before anything is connected.

We already have AI tools. Why are we not seeing results?

Usually because the tools were bought before the workflow was defined, nobody owns the outcome, and the process underneath was never cleaned up. We start with the process and the metric, then fit the tool to it. Often that also means cancelling licences that are not paying for themselves.

Ready to deploy, not just discuss?

Tell us which function you want to start with. We’ll tell you what’s achievable in 90 days.