See every at-risk account
before renewal.
Summarizing calls with AI is easy now. Knowing which accounts will churn, and acting before the renewal, is not. Scale Harmony turns scattered signal into a scored, owned risk call your team acts on while it still changes the outcome.
AI reads the signal. Your people make the call.
Summarizing calls is table stakes.
Summarizing calls with AI is table stakes now. The danger is mistaking it for retention. You have more notes than ever and you are still surprised at renewal, because no one has turned the signal into a ranked, owned risk call the team acts on in time.
Coverage scales with people. Judgment does not.
Why now
NRR is falling across B2B SaaS even as teams add headcount. Boards want an AI answer. Most AI pilots return nothing.
The platforms gave you AI. Making it pay off is on you, and that takes a methodology, not another tool: one that turns AI signal into decisions and filters out the noise. Your CSMs know your customers. Scale Harmony gives them the system to act on what AI surfaces.
The platforms gave you AI.
Making it pay off is on you.
Scale Harmony gives your CSMs the system to act on what AI surfaces.
The Retention Engine Sprint
Three weeks. You finish with a working system on your own data and a plan your board will recognize.
You get:
A signal audit, mapped to your customer journey.
Where churn and expansion signal leaks, stage by stage.
A working prototype on your own data.
A core AI skills library plus a first CS plug-in that runs on your stack. Yours to keep.
A recommended operating model.
How AI and your people split the work, and who owns each signal.
A 90-day plan to production.
A board-ready readout tied to GRR and NRR.
Internal-only, built on your stack
Internal-only, built on the stack you already run. Your team owns every customer decision.
The cost of waiting
Your team pours a year of effort into every renewal: the onboarding, the QBRs, the success plans. But renewals are not won at renewal. They are decided months earlier, in adoption, in signals your QBRs never surface.
By the time an account looks at risk, the effort is spent and the answer is already set. You cannot recover in the renewal call what slipped in adoption and no one flagged.
Human and AI division of labor
AI reads the signal and drafts the work. Your people decide, negotiate, and own the relationship.
Auditable calls
Every call is auditable, with its reasons named, so you can defend it in the room.
Judgment stays with your best people
The result scales with your book instead of your headcount, and the judgment stays where it belongs, with your best people.
The methodology
A few principles behind the work:
Retention is decided in adoption, not at renewal.
The signals that predict a renewal show up months earlier, in how the product is used and how the relationship behaves. The renewal call is a lagging indicator.
Sense, Decide, Act, Relate.
Every CS task is one of four jobs. AI should do almost all of the sensing and draft most of the acting. People own the decision and the relationship.
Summaries are not decisions.
Summarizing calls gives you information. Retention needs a ranked, owned risk call, applied the same way across the whole book.
Score risk on rules you can audit.
A useful risk read is multi-dimensional and rule-based, and it names the signal behind every score, so a human can trust it and challenge it.
Build the thin layer, buy the commodity.
Buy generic AI. Build only the judgment layer that encodes how your business defines risk. Most failed AI projects build the part they should have bought.
Best for
- Post-PMF B2B SaaS, roughly $5M ARR and up
- Leaders accountable for GRR, NRR, and renewals
- Complex, technical, or regulated products
Not for
- Pre-PMF startups
- Teams wanting generic CS playbooks
- "Customer Success as customer happiness" orgs
Scale Harmony is led by Arkady Zapesotsky, a five-time VP of CS who has renewed and expanded over $500M across global B2B SaaS, cybersecurity, and AI-first platforms, including Fortune 100.
The work comes from systems built and run in production. Arkady brings an operator's retention playbook to a small number of SaaS teams.
See where your retention signal is leaking.
A 30-minute working session, peer to peer.
What do you deliver in three weeks?
A working prototype on your own data (a core AI skills library plus a first CS plug-in), a signal audit across your customer journey, a recommended operating model, and a 90-day plan to production. Your team owns every customer-facing decision.
Is this consulting or software?
Expert services that leave you with a working system and a plan you own. If you later want it productized and run continuously, that is a separate engagement.
Can't we just use Gainsight or do it ourselves with AI?
Use the tools. AI summaries and platform scores are commodities now, and they are useful. They will not decide which accounts are at risk across your whole book, apply the same judgment every time, or assign who acts before the renewal. That judgment layer, fit to your business and your data, is the work, and it is why most in-house AI projects stall: they build the part they should have bought.