Industry · SaaS & Tech
AI for SaaS companies that already have signal — just no one reading it.
Churn prediction on real product telemetry. Expansion signals before a renewal call. CS automation that doesn’t insult the customer. PLG analytics that point at the next ten customers, not the last hundred. Built on HubSpot, Mixpanel, Intercom, Stripe, Amplitude, Segment.
What’s broken without AI in a SaaS company
Every Series-A-and-beyond SaaS company we walk into has the same five problems, in roughly the same order:
- Churn is a post-mortem, not a forecast. Customer success finds out an account is dying when the renewal QBR is two weeks away. The activity log told a different story for the previous ninety days; nobody had time to read it.
- Expansion signals die in Mixpanel. Power-user accounts that have outgrown their tier sit in the product analytics tool with nobody reading them. AEs ask the CS team for “hot accounts”; CS guesses; everyone moves on.
- CS pods are reactive by structure. The CSM with 80 accounts cannot proactively triage anything. Inbound tickets win every time. The accounts that would benefit most from an intervention are exactly the ones that don’t complain.
- Onboarding is a guessing game. You can see which users hit the “aha moment” in the funnel. You cannot see which of the silent users are about to fall off. Generic email cadences hit everyone the same.
- Pricing and packaging move on gut. The product-led growth motion generates more usage telemetry than any team can read. Decisions about feature gating, free-tier limits, and conversion triggers are made on the strongest opinion in the room.
What we build for SaaS companies
Churn-risk model trained on your retention history
Activity decay in the product. Support-ticket sentiment drift. Stalled feature adoption. Champion-user departures detected via email-domain changes. We train a model on your last 24 months of churned versus retained accounts — not a vendor benchmark — and write the risk score back into HubSpot or Salesforce as a contact field. CS pods see the at-risk list every Monday, ranked.
Expansion-signal scoring across your product telemetry
We ingest Mixpanel / Amplitude / Segment events, layer on your CRM stage and seat counts, and identify accounts that are using the product like they’re three tiers up. Output: an expansion queue your AEs see in their CRM, not a separate dashboard nobody opens.
CS automation that handles the bottom half of the workload
Renewal-prep packets generated automatically: usage summary, support-ticket history, NPS trend, ROI estimate. Onboarding reminders triggered by silence in the product, not by a calendar. Knowledge-base answers drafted into Intercom for a CS rep to send in one click. We handle the work that doesn’t need judgement, so the CSM can focus on the half that does.
PLG conversion engine on your free-to-paid funnel
We score every free-tier account for paid-conversion likelihood, identify the next-best in-product nudge (Pendo / Appcues / native), and write the lifecycle stage back to HubSpot so marketing can sequence them. The free tier becomes a sales pipeline, not a cost centre.
Voice-of-customer synthesis from support, sales calls, and NPS
Gong / Chorus call transcripts, Zendesk / Intercom tickets, NPS comments, churn-survey responses — all clustered into themes weekly, fed to product and to revenue leadership. The signal was already there. We make it readable.
Renewal QBRs that start informed
In our recent SaaS deployments, CS teams typically walk into renewal calls with a fully drafted usage + risk + expansion packet. The hours saved per CSM per week compound across the quarter.
The stacks we plug into
On the revenue side: HubSpot, Salesforce, Pipedrive, Close. On product analytics: Mixpanel, Amplitude, Heap, PostHog, Segment. On CS / support: Intercom, Zendesk, Front, Help Scout, Pylon. On billing and lifecycle: Stripe, Chargebee, Maxio, Recurly. On in-product engagement: Pendo, Appcues, Userflow. On call intelligence: Gong, Chorus, Fathom.
Data residency and customer trust
SaaS companies sell trust as a product. We deploy AI work into your AWS / GCP / Azure account by default, so customer data never leaves your tenancy. Vendor model access is via no-retention endpoints (or your own keys). We sign SOC 2 / DPA / data-processing addenda as standard. If your enterprise prospects ask “does any third party see our data when you use AI?” the honest answer remains no.
Frequently asked
We’re pre-Series-B. Is this overkill?
It depends what you already have. If you have at least nine months of CRM activity and at least 200 churned-versus-retained accounts to train against, the churn model alone usually pays back the 30-day engagement before the quarter ends. Pre-Series-A teams are usually too early.
We have an in-house data team. Do you compete with them?
No — we usually accelerate them. Data teams are great at modelling; they’re typically not staffed for the CRM / Mixpanel / Intercom integration plumbing. We build the integration layer, document it, and hand it back. Your team owns the model from there.
Does this work for B2B2C / multi-tenant products?
Yes — with care on tenant isolation. We’ve shipped models where each customer’s end-users are scored separately and predictions never cross tenancy boundaries. It is more engineering, and we scope that up-front.
Can we white-label any of this into our product?
Two different conversations. Internal AI (CS, sales, marketing) is fastest to ship. AI features inside your own product require deeper scoping — pricing, latency, multi-tenant cost ceilings — and we do that under custom agents.