We design and deploy custom AI agent infrastructure for post-sales teams — health monitoring, renewal intelligence, leadership visibility, coaching workflows, and support triage — across the systems you already run.
Most CS teams we talk to have already built something in Claude or ChatGPT. A health score prompt. A QBR template. A renewal brief. The question they're all asking is the same: how do we make this a real thing?
A prompt running in a chat window isn't pulling live CRM data, support tickets, or product usage. It's working off what you paste into it.
Someone still has to trigger it, format the input, and do something with the output. That's not an agent, that's a better search bar.
One agent monitoring health and a separate one building QBRs create duplicated effort and inconsistent data unless they share a backbone.
Workflow design, data routing, escalation logic, and human-in-the-loop oversight have to be built before anything runs in production reliably.
We build on an MCP (Model Context Protocol) orchestration backbone — a shared infrastructure layer that connects your data sources, routes context between agents, and manages the handoffs that make multi-agent workflows actually function in production. Without it, you have isolated automations. With it, you have an operating system for your post-sales team.
The MCP layer means your health monitor and your renewal prep agent are working from the same account data, not two separate snapshots with drift between them.
Agents run when something happens — a health score drops, a renewal window opens, a ticket escalates — not when someone remembers to ask.
When the health monitor flags risk, the renewal prep agent gets that signal automatically. You don't wire these by hand every time.
The infrastructure determines where full automation is safe and where a human needs to review before anything goes to a customer.
Clear use case, clear owner, clear success criteria. The bottleneck has to be specific before anything gets built.
Where does the data live? What events matter? What should the agent do, and what should it surface to a human?
MCP server configuration, data source connections, routing logic, and the shared context layer agents run on top of.
Individual agents built on top of the shared backbone, tested against real data, with fallback logic and escalation paths.
Launched by use case or customer tier. Full control back to your team. The system runs without us in the room.
These are the workflows CS leaders are actively trying to operationalize. Each one is a discrete agent that can run standalone or as part of a connected multi-agent stack.
Pulls signals from CRM data, support activity, product usage, and interaction history to surface churn risk and expansion opportunity earlier than human review cycles catch it.
Risk & VisibilityAutomatically assembles a full account brief before a renewal call — health history, open tickets, usage trends, prior conversation context — so your CSM isn't pulling from five systems in the 30 minutes before the call.
Renewal IntelligenceBuilds account reviews from live data — usage signals, support trends, prior conversations, open initiatives — so teams spend their time driving outcomes, not building decks.
QBR AutomationGives managers an operating view across the book: renewals inside 90 days, low-engagement accounts, rising support burden, stalled implementations. Flags conditions that need intervention without digging through multiple systems.
Management VisibilityReviews call transcripts, generates coaching notes, scores playbook alignment, and identifies patterns across the team so managers can coach consistently at scale rather than just reviewing the accounts they personally touch.
Team DevelopmentCaptures recurring customer asks, categorizes workarounds, sizes their impact, and helps leadership decide what to document, standardize, escalate, or push back on as a product conversation.
Product SignalWe don't sell a platform. The work is connecting the right data sources, identifying the signals that matter, and routing them through an architecture your team can actually run.
Account records, renewal timing, ownership, lifecycle stage, and customer history — HubSpot, Salesforce, or both.
Ticket themes, escalation patterns, resolution time, recurring issues, and workaround requests from Zendesk, Intercom, or HubSpot Service Hub.
Adoption trends, feature depth, engagement patterns, and behavioral changes that indicate risk or expansion opportunity.
Meeting transcripts, customer sentiment, follow-up commitments, and coaching signals from Gong, Chorus, or your call recording system.
Playbooks, help center content, process documentation, and known workarounds that agents need to answer complex questions accurately.
Notifications, triggered tasks, leader alerts, and handoffs routed to Slack, email, or directly into your CRM task queue.
The CS AI Capability Assessment takes about 10 minutes. It scores your team across the same six use case categories covered in our work, identifies where your data and tooling gaps are, and gives you a prioritized starting point before any conversation with us.
We can identify the right starting use case, map your systems, design the architecture, and build a rollout that ends with your team running it, not depending on us to keep it working.
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