Industry · B2B SaaS
Scale revenuewithout scaling headcount.
L1 deflection, omnichannel CX
Native + standalone deployments
Revenue + CS automation
Lakehouse golden record
The Problem
You're managing toolsinstead of compounding revenue.
Your stack is a Frankenstein.
Gong, Drift, Intercom, Pendo, Snowflake: eight tools that don't talk to each other in real time. Your revenue team manages tools instead of working in one.
Your CS team scales linearly with revenue.
Every $5M in ARR costs you 2-3 more humans. Margins compress. CAC payback stretches past 18 months. Your CFO has noticed.
Your competitors are deploying agents.
The cohort that figured out Agentforce or standalone Anthropic deployments is growing revenue 40% on 5% headcount. You're hiring more SDRs.
Dual Deployment Paths
Salesforce or not.We deploy Claude either way.
Not every B2B SaaS lives on Salesforce. Maple is the rare partner equipped for both.
Native: inside Salesforce Agentforce
Claude inside Agentforce via the new Salesforce-Anthropic integration. Right answer when Salesforce is the system of record and the agentic surface lives there.
Standalone: on AWS Bedrock or direct API
For B2B SaaS companies on HubSpot, custom platforms, or no CRM. Same agentic outcomes, different platform under the hood.
Use Cases
Agentic patternsfor high-growth SaaS.
PQL-to-SQL Routing Agent
Product usage signals from Pendo, Mixpanel, or Amplitude hit the data lake; an agent scores, enriches, and routes to the right AE in real time. SDR headcount stops being the gating factor.
Stack · SF + Databricks/Snowflake + Anthropic
Churn Prediction Agent
Behavioral signals — declining logins, fewer API calls, increasing support volume: trigger proactive CS outreach before the customer even knows they're unhappy.
Stack · SF + Databricks/Snowflake + Anthropic
Renewal & Expansion Agent
Monitors usage milestones and triggers expansion plays automatically: upsell sequences, CS check-ins, QBR scheduling.
Stack · SF + Databricks/Snowflake + Anthropic
Frankenstein-Stack Consolidation
Replace Intercom, Drift, and portions of Gong with Agentforce or Maven-powered agents. Fewer subscriptions, fewer integrations, one source of truth.
Stack · SF + Maven AGI or Anthropic standalone
The Engagement Model
Build, Operate, Transfer.Designed for SaaS economics.
Build
Agents and the data foundation in 6–10 weeks, not 9 months.
Operate
Monthly Agent Performance Audits with measurable Agentic ROI: cases deflected, hours saved, revenue influenced.
Transfer
Full ownership handoff when your team is ready. No vendor lock-in. No retainer trap.
What to Expect
No fake stats.Honest commitments.
Maple is in the first wave of agentic AI engagements. Our first B2B SaaS engagements are running now: PQL-to-SQL routing, churn prediction, expansion automation, and Frankenstein-stack consolidation.Published case studies are coming — meanwhile, here's exactly what every engagement commits to from day one.
Commitment 01
Time to first production agent
We architect and deploy your AI workforce in weeks, not months — with weekly milestone reviews you can hold us to.
Commitment 02
Agent Performance Audits
We optimize prompts, reasoning engines, and data triggers every month — and report exactly what work each agent did.
Commitment 03
Vendor lock-in
Every engagement ends with full ownership transferred to your team. We earn the next engagement, never the retainer trap.
Case studies in progress · Be one of our first
We Also Serve
PE/VC portfolio operators
One architecture pattern, deployed across 10–20 portcos. Centralized governance with portfolio-level cost optimization.
Vertical SaaS
Industry-specialized software companies whose buyers care about compliance and data sovereignty more than horizontal SaaS.