Applied AI engineering for B2B SaaS revenue systems.
We work with revenue leaders at $10–100M ARR B2B SaaS companies. We clean up the data and systems underneath, then build AI that moves the numbers you report on.
The problem we keep seeing
Most B2B SaaS revenue orgs in the $10–100M ARR band have some version of the same four problems at once:
- The revenue tech stack has sprawled into dozens of overlapping tools with no clear system of record and no one on the team with time to consolidate it.
- Sales, marketing, and CS define the same things differently, so nobody trusts a cross-functional number.
- Someone has already piloted an AI tool and it didn’t stick.
- The board is asking for an AI story while the team is already stretched and the ops function is one or two people deep.
AI fails in these environments because the data and processes underneath are too broken to support it. Our work starts with fixing that foundation, and the AI only comes after it holds.
What we build
Each engagement is scoped around one functional leader and the metrics they own. The examples below are common project shapes by function, not a fixed menu, and the actual scope is defined per engagement.
Sales
- Metrics we target: win rate, forecast accuracy, deal velocity.
- Processes we focus on: qualification, pipeline hygiene, forecasting.
- What we ship: deal qualification agents, forecasting models, meeting-to-CRM sync.
RevOps
- Metrics we target: data quality, reporting trust, ops-team leverage.
- Processes we focus on: data model design, reporting, process governance.
- What we ship: data model cleanup, unified reporting layer, ops copilot agents.
Customer Success
- Metrics we target: NRR, churn rate, time-to-value.
- Processes we focus on: onboarding, health scoring, renewal and expansion.
- What we ship: churn risk signals, CSM copilots, health scoring models.
Finance
- Metrics we target: forecast accuracy, quote-to-cash cycle time, billing error rate.
- Processes we focus on: forecasting, quote-to-cash, revenue reporting.
- What we ship: forecast intelligence, Q2C automation, billing reconciliation agents.
Cross-functional GTM
- Metrics we target: pipeline coverage, funnel conversion, CAC payback.
- Processes we focus on: lead-to-revenue motion, marketing-to-sales handoff, cross-functional reporting.
- What we ship: unified revenue data layer, funnel intelligence, cross-functional AI agents.
How the work runs
Four steps in every project.
- Audit. We map the current state of the target function across data, process, tooling, ownership, and where things break, and you get a written diagnosis whether or not we continue.
- Data and systems. We clean the data model, connect the stack, and define the process the AI has to live inside.
- Context layer. We build the retrieval and information layer that makes AI useful on your business, your buyers, and your motion.
- AI and HITL agents. We ship the agents into production with the human-in-the-loop patterns that make your team trust them and use them.
Most AI pilots skip the first three steps, which is why they stall.
How engagements are structured
- One sponsor. A single functional leader owns the engagement, and there is no steering committee.
- Named targets. We agree on the metrics we’re moving before work starts, and they show up in the final report.
- Fixed scope and timeline. Every engagement has a defined end date and a working system delivered into production.
- One builder. The person on your discovery call is the person writing the code, so there are no account-manager or offshore handoffs.
Who this is for
Good fit:
- B2B SaaS between $10M and $100M ARR.
- A CRO, VP RevOps, VP Sales, CS lead, or CFO with budget authority.
- You’ve seen at least one AI tool fail to land on your existing stack.
- Your priority is a system that runs in production.
Probably not a fit:
- Under $10M ARR, where the problems aren’t concentrated yet.
- Over $100M ARR, where internal teams or large SIs already own this.
- Non-SaaS companies.
- Buyers looking for a bolt-on AI feature without interest in the revenue system underneath it.
Who leads the work
Every engagement is led by Rasmus Sikk. He spent a decade architecting revenue systems inside B2B SaaS companies, including six years running his own Salesforce consulting agency, before two years building and shipping production AI systems as a founder.
Get in touch
If any of this matches what your revenue org needs, we’d be glad to hear from you. A short intro call is usually the easiest way to start, and email works equally well if that’s more your speed.