We partner with the GTM leaders who refuse to plateau,turning AI into revenue you can forecast, decisions you can trust, speed and efficiency your existing agency can’t match
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/ AI + Human = Intelligence2 squared
Artificial intelligence gives go-to-market leaders a blank canvas. Human intelligence decides what's worth painting.
That era is ending. The canvas is suddenly blank again, and the brush is in the hands of the people closest to the customer. The question is no longer what the software allows. It's what you want your go-to-market motion to become.
Pair the two and something compounds. Judgment scales, experiments multiply, and teams stop optimizing the old playbook and start rewriting it.
Not long ago, turning an idea into a working tool meant a business case, a budget cycle, an engineering queue and two quarters of waiting. By the time it shipped, the market had moved.
The cost of trying something has collapsed toward zero. That changes the math of every decision. You no longer have to be right before you start; you can be curious, build three versions and let the results tell you which one deserves to live.
The bottleneck is no longer the build. It's the imagination, and the discipline, to know what is worth building next.
When anything can be built, the new risk is building the wrong thing faster. A team with unlimited tools and no point of view ends up with a dozen dashboards nobody opens and automations nobody trusts.
The best go-to-market leaders treat models like brilliant, tireless junior partners: fast, wide-ranging and occasionally confidently wrong. Their value multiplies when someone experienced frames the problem and edits the output.
Intelligence squared isn't AI plus a human reviewer at the end. It's human judgment woven through every step, deciding what to paint before a single brushstroke lands.
For the first time, the ceiling on a revenue team's ambition isn't budget or headcount. It's vision. A lean team can operate with the reach of an enterprise, and an enterprise can move with the speed of a startup.
Marketing, sales and customer success no longer need to be separate relay runners handing off a baton. With shared data and intelligent workflows, they can act like one continuous conversation with the customer.
The canvas is blank. The tools are ready. What gets painted now depends on the people holding the brush.
We exist to make revenue predictable. Opsforce brings operators, expert networks, and AI together so go-to-market teams can act on truth instead of guesswork.Every engagement is measured the only way that matters: pipeline you can forecast, decisions your board trusts, and speed your competitors can’t copy.
Lauren Crist
Co-founder
John McCoy
Co-founder
Chelsea Freidenrich
Head of Expert Network
Matt Litchfield
Head of Gaux AI
Sylvia Carrizosa
Vetted Expert
Glen Shillinglaw
Vetted Expert
Michelle Bewersdorff
Fully Vetted Expert
Thomas Pepperz
Vetted Expert
Result: 18% avg. lift in pipeline coverage within two quarters
Result: 30% faster rep ramp across enterprise teams
Result: 2.4x cross-sell rate on governed advisor plays
Result: 40% shorter quote-to-close cycle
Result: 25% faster sales cycle for health-tech buyers
Result: 50% fewer quote errors on configurable products
Result: 2x forecast accuracy on 12-month cycles
Result: 20% lift in wholesale reorder rate
Engagements where the go-to-market model itself was the problem, overlapping territories, stalled integrations, and org designs that no longer matched how buyers buy. These are the programs where we sat with the C-suite, redrew the operating model, and stayed until it ran.
Redesigned enterprise sales coverage across three business units, cutting account overlap and shortening approval cycles.
Three business units were calling on the same enterprise accounts with different pricing authority and conflicting proposals. Deal desk approvals routinely took eleven days, and reps were spending more time negotiating internally than with customers.
Stood up a global RevOps function to replace eight regional operations teams working from different definitions.
Regional operations teams each owned their own forecast cadence, CRM configuration and reporting logic. Global roll-ups required a week of manual reconciliation and still landed with a material variance to actuals.
Integrated two overlapping sales organizations post-acquisition without losing a quarter of pipeline.
An acquisition doubled the seller count and created duplicate accounts across two CRMs. Sellers were unclear on quota credit, and attrition risk was concentrated in exactly the reps holding the largest renewals.
Rebuilt the provider-network operations model, removing manual handoffs across four back-office teams.
Contracting, credentialing, data management and support each operated as separate queues. A single network change touched all four with no shared record, and cycle times drifted well past service commitments.
Shifted a hardware-led commercial org toward a recurring-revenue motion with new roles, metrics and incentives.
A subscription line was growing but was measured, compensated and forecast like hardware. Renewals had no owner, and churn only became visible after it had happened.
Simplified a member-facing service operation, reducing escalations while cutting operating cost.
Member requests arrived through five channels into three teams with different scripts and no shared history. Escalations were rising and the cost to serve was climbing faster than membership.
Programs where the stack was fighting the team. We consolidated tooling, designed the architecture, and shipped AI that sits inside real workflows, with humans reviewing anything that touches a customer or a number.
Consolidated a sprawling internal RevTech estate and shipped an agent-assisted account research workflow.
Sellers were moving between nine tools to prepare for a single call, and three of those tools duplicated the same account intelligence. An earlier AI pilot had produced confident summaries that sellers stopped trusting after two bad calls.
Designed and delivered an AI-assisted quoting workflow that cut proposal turnaround from days to hours.
Complex multi-product quotes required product, legal and finance input, and each pass added a day. Reps worked around the process with offline spreadsheets that finance could not reconcile.
Replatformed partner-facing systems and introduced automated deal registration with fraud checks.
Deal registration was a manual review with a five-day SLA that partners routinely missed. Duplicate and speculative registrations were consuming the review team's entire capacity.
Built an internal knowledge agent over engineering and commercial documentation, with strict source grounding.
Field engineers were answering the same integration questions repeatedly, and answers varied by who was asked. Documentation existed but was spread across four systems with no shared search.
Rebuilt lead routing and enrichment after a platform migration left conversion rates in decline.
A migration had silently broken enrichment on roughly a third of inbound leads. Routing rules had been rewritten by three different owners, and no one could explain why some segments never received follow-up.
Delivered an AI summarization layer for customer health reviews used weekly by the CS organization.
Customer success managers spent the first day of every week assembling account health narratives from usage data, tickets and notes. The output was inconsistent and rarely made it into leadership reviews intact.
Modernized the demo and trial environment, cutting provisioning time from days to minutes.
Every technical evaluation required a manually built environment. Provisioning took three to five days, and inconsistent setups made evaluation results hard to compare or reproduce.
Work that made the numbers trustworthy again. Definitions, lineage, models and reporting rebuilt so leadership stops arguing about whose figure is right and starts deciding from one.
Established a governed commercial data model that ended competing revenue figures across three reporting teams.
Finance, sales operations and the business units each produced a revenue view, and all three were defensible under their own definitions. Monthly reviews opened with reconciliation instead of decisions.
Built a client revenue analytics platform with auditable lineage for every reported figure.
Reported client revenue could not be traced back to source without manual investigation, which made both internal review and external scrutiny slow and uncomfortable.
Rebuilt attribution and pipeline reporting so marketing and sales worked from one funnel.
Marketing reported sourced pipeline that sales did not recognize. Two attribution models ran in parallel, and the gap between them was large enough to change budget decisions.
Delivered a governed client-360 view spanning custody, treasury and markets relationships.
Relationship data lived in product-aligned systems with no reliable entity resolution. Coverage teams could not see the full relationship, and cross-sell conversations were based on partial information.
Consolidated more than 600 legacy reports into a certified set of 40 used across the commercial org.
Report sprawl meant most decisions were made on uncertified content, and no one could tell which dashboard was current. Refresh cost was significant and largely spent on reports nobody opened.
Redesigned the demand and capacity model that matches expert supply to client requests.
Fulfilment planning ran on rules of thumb, and demand spikes in specific expertise areas produced long response times in exactly the segments that mattered most.
Engagements where the answer wasn't known at the start. We prototyped fast, tested with real users, and stayed embedded through delivery, people, process and technology moving together.
Took a partner-facing commerce concept from prototype to production in two quarters.
A promising internal concept had stalled between teams for a year. There was no agreed scope, no owner and no evidence it would work for the partners it was meant to serve.
Led the product and change program for a new partner onboarding experience across four regions.
Onboarding a new implementation partner took nine weeks and varied significantly by region, which limited how quickly capacity could be added in growing markets.
Built the commercial operating system behind a fast-scaling foodservice channel.
Growth had outrun the systems supporting it. Distributor performance sat in spreadsheets, and the team could not tell which accounts were genuinely growing versus reordering unevenly.
Designed and shipped a self-serve trial-to-paid journey alongside the existing sales-led motion.
A self-serve motion had been discussed for two years but always lost to sales-led priorities. Nobody had tested whether the target segment would actually convert without a seller.
Delivered a rebuilt quoting and order journey for complex network services.
Quoting a multi-site network deal required specialist involvement and typically took two weeks, with errors surfacing only at order entry.
Rebuilt the digital acquisition journey and the operations behind it in a single program.
The buying journey and the fulfilment process had been improved separately for years, so a smoother sign-up simply pushed more work into a provisioning process that could not absorb it.
Modernized field sales and service workflows for a distributed commercial fuel operation.
Field teams worked from paper and phone calls, and pricing decisions were made without visibility of current account terms. Back-office rekeying introduced errors and delays into billing.
Revenue leaders don't lose quarters to bad strategy. They lose them to work they can't see, talent they can't get fast enough, and systems that take two quarters to change. We close all three.