AI Agents for B2B SaaS.
Your product is already collecting every signal you need — activation events, usage drops, support sentiment, seat growth. The agents we build act on those signals automatically, so your team focuses on the decisions that actually require humans.
Four places where SaaS teams leave money and time on the table.
Each one is automatable. None of them require replacing your team.
Activation gap agent
The problemTrial users who don't hit a key activation event in the first 72 hours almost never convert. Your team can't manually touch every trial at scale.
The agentAn agent monitors product events in real time. When a user stalls — no dashboard created, no team invite sent, no integration connected — it fires a targeted nudge via email or in-app chat with the exact next step for their use case. No batch campaigns, no generic drip.
Churn signal agent
The problemYou find out a customer is churning when they cancel — not 30 days before when you could still do something about it.
The agentThe agent tracks a composite of leading indicators: login frequency, feature adoption breadth, support ticket sentiment, and seat utilization. When the score dips, it alerts the CSM with a pre-drafted outreach and books a call automatically. Human still closes the loop — agent surfaces the signal before it's too late.
Tier-0 support deflection agent
The problemYour support team spends 40% of their time on questions that are literally answered in your docs. That's 40% they can't spend on complex, relationship-building tickets.
The agentAn MCP-powered agent with live access to your docs, changelog, and knowledge base handles inbound support tickets. It resolves, routes, or escalates — and when it escalates, it pre-populates the ticket with context so the human rep doesn't start from zero.
Expansion revenue agent
The problemUpsell and cross-sell opportunities exist in your data, but your AEs only look at accounts when they're already in a renewal cycle.
The agentThe agent scans usage data weekly — accounts hitting plan limits, teams that have grown headcount, features being requested via support — and surfaces expansion plays directly in your CRM with a recommended offer and talking points.
What actually moves when you automate this layer.
Trial-to-paid conversion improves when activation agents catch stalls within the first 72 hours instead of 2 weeks later.
Support ticket volume drops significantly when a deflection agent handles Tier-0 queries that have clear, documented answers.
Churn signals surface 3–6 weeks earlier when an agent tracks behavioral data continuously instead of waiting for renewal conversations.
CS team capacity expands without headcount — agents handle the monitoring and routing, humans handle the relationships.