Resources/Example AI Workflows

Resource

Example AI workflows for a GTM agent deployment layer

A walkthrough of how solid data infrastructure, scalable compute, and agent skills can let GTM teams deploy their own production-grade agents without rebuilding authentication, data access, logging, or approvals for every new workflow.

See the deployment layer View workflow evidence 
$ pi agents list --workspace gtm
✓ pre_call_brief
✓ renewal_risk_digest
✓ expansion_signal_queue
✓ crm_writeback_reviewer

$ modal app list --tag gtm-agent-runtime
✓ transcript_extractor
✓ account_research_worker
✓ digest_scheduler
✓ patch_plan_executor

The engineering answer

The product is not one bot. It is a safe way for teams to deploy agents.

The best internal AI programs do not rely on a central team manually building every use case. They build the rails: governed data, reusable compute, deployable skills, observability, and controlled writeback. GTM teams can then ship useful agents at the edge.

01

Governed data substrate

Snowflake-backed contracts for accounts, opportunities, contacts, product usage, transcripts, support, billing, and marketing engagement.

  • Row-level permissions
  • Shared metric definitions
  • Citation-ready source records
→
02

Modal compute runtime

Elastic workers for scheduled, event-driven, and user-triggered agent jobs — transcript preprocessing, account research, scoring, and long-running analysis.

  • Isolated secrets
  • Job logs and cost tracking
  • Versioned Python/SQL execution
→
03

Pi skill packaging

Each workflow ships as a versioned skill: instructions, tools, allowed data, evals, owner, approval policy, and distribution targets.

  • Reusable templates
  • Human-in-loop controls
  • Agent catalog
→
04

HUD control plane

A team-facing surface to browse agents, trigger runs, inspect logs, review citations, approve writebacks, and measure workflow value.

  • Slack/CRM/email outputs
  • Run history
  • Audit trail

Anonymized client workflow evidence

Three patterns we have built in client work

Client identifiers are removed, but the evidence structure is preserved: source systems, agent/code path, generated artifacts, and distribution back into GTM workflows.

Calendar-triggered pre-call intelligence

When a strategic meeting appears on a seller's calendar, the workflow identifies the account and attendees, retrieves account history, prior calls, stakeholder map, and external context, then generates a briefing packet for the account team.

CalendarMeeting, attendees, owner
CRMAccount, opp, activity
CallsObjections, pain, next steps
EnrichmentCompany context

Generated output excerpt

Opening hypothesis: The prospect is likely losing time in manual review cycles and fragmented documentation.

Risk to address: Incumbent-system lock-in and unclear urgency. Probe for cost of delay and compliance exposure.

Revenue and customer-health operating brain

A warehouse-centered intelligence layer unifies structured GTM data and text-heavy customer signals, then runs scheduled jobs that produce prioritized recommendations for executives, CS, Sales, and RevOps.

7-factorhealth score
Dailydigest cadence
Modalagent runtime

Generated insight excerpt

Renewal risk: High-ARR account has declining usage, recent support escalation, and no executive touch in 21 days.

Expansion signal: Three accounts crossed product-adoption threshold but have no open expansion opportunity.

Governed CRM writeback workflow

Agents propose changes, but business systems remain controlled. The workflow produces typed patch plans with before/after values, citations, risk level, and approval state before anything is written back.

{
  "object": "opportunity",
  "field": "next_step",
  "before": "Demo completed",
  "after": "Security review scheduled; send implementation plan",
  "approval_required": true,
  "citations": ["call:timestamp", "crm:activity"]
}

Interactive synthetic demo

Click a GTM question and watch the agent path change

This uses fake companies and metrics, but mirrors the source joins, reasoning steps, and distribution surfaces from the client workflows above.

Agent library

Acme Dental — pre-call brief

Acme has renewal in 74 days, Reviews usage is up 42%, Messaging is not configured, and the VP Ops recently viewed implementation content. The expansion angle is automated patient messaging tied to review-volume growth.

#gtm-brain · generated insight

Hypothesis: Account is ready for Messaging expansion.

Evidence: CRM renewal date, product usage trend, call transcript phrase “centralizing patient communication,” and marketing engagement.

Recommended action: create expansion opp and draft AE follow-up.

30-day engineering pilot

Prove teams can deploy agents without bespoke infrastructure work

The success criterion is not “one impressive demo.” It is whether a second team can deploy the third agent using the same data contracts, Modal runtime, Pi skill template, and HUD controls.

Week 1Define governed data contracts, permissions, and run-log schema.
Week 2Deploy two Modal-backed agents as Pi skills: pre-call brief and risk digest.
Week 3Add citations, evals, cost/log tracking, Slack delivery, and CRM writeback previews.
Week 4Hand the template to an ops team and have them deploy a third agent through the same rails.