Use cases
Start with the three patterns we demo for BizOps and integration teams on one governed operations runtime. Vertical scenarios—including ESG and supplier evidence—use the same tenant.
Explore the platform overview.
Three patterns teams run in production on one tenant—the same beats we show in demos for BizOps and integration leads.
Problem: Onboarding and intake run through email, forms, and CRM with manual handoffs and no shared execution history.
Outcome: From portal or form submit to CRM and ops records in one tenant—without bespoke glue per channel.
Problem: Exceptions need judgment, but copilots cannot safely see processes or run integrations under policy.
Outcome: AI loads a tenant playbook, proposes action, a human approves, and integrations run under guardrails.
Problem: Failed syncs surface in logs or inboxes with unclear ownership and risky ad hoc retries.
Outcome: Failures visible in Monitor; processes or agents wait and retry with policy instead of silent scripts.
Each pattern below includes who it is for, the problem, outcome, and example output—the same beats we walk through on a live demo.
Reference workflow
Who: BizOps, RevOps, and integration leads
Problem: Onboarding and intake run through email, forms, and CRM with manual handoffs and no shared execution history.
Outcome: From portal or form submit to CRM and ops records in one tenant—without bespoke glue per channel.
Example output: WebApp or trigger → process → integrations → Datapool row with lineage.
Reference workflow
Who: Operations leads and tenant admins
Problem: Exceptions need judgment, but copilots cannot safely see processes or run integrations under policy.
Outcome: AI loads a tenant playbook, proposes action, a human approves, and integrations run under guardrails.
Example output: Trace AI skill → describe action → HITL → governed execute.
Reference workflow
Who: Integration engineers and BizOps
Problem: Failed syncs surface in logs or inboxes with unclear ownership and risky ad hoc retries.
Outcome: Failures visible in Monitor; processes or agents wait and retry with policy instead of silent scripts.
Example output: Monitor alert → agent_wait or process step → retry with guardrails.
Industry and program-specific outcomes on the same platform—including ESG & supplier evidence as a solution package, not a separate product.
Vertical scenario
Who: Sustainability, procurement, and finance teams
Problem: Supplier emissions and ESG evidence live in email, PDFs, and spreadsheets—with no single source of truth.
Outcome: Structured supplier ESG data, repeatable collection, and reporting mapped to what auditors and frameworks expect.
Example output: Coverage dashboards, golden supplier records, and export packages aligned to ESRS-style reporting.
Vertical scenario
Who: Ecosystem leads, partnerships, and platform owners
Problem: Partner onboarding, contracts, and shared data are fragmented across CRMs, files, and ad hoc channels.
Outcome: A governed network model of companies, relationships, and value flows you can operate—not just document.
Example output: Partner graphs, onboarding status, and shared datasets consumable by your marketplace or internal tools.
Vertical scenario
Who: Operations, sustainability, and supply chain teams
Problem: Materials, logistics, and counterparties change hands through disconnected systems and opaque handoffs.
Outcome: Traceable flows across companies, materials, and logistics partners with a shared operational picture.
Example output: Material and shipment graphs, partner roles, and KPIs for reuse, recovery, and routing.
Vertical scenario
Who: Procurement and supply chain leadership
Problem: Tier-N visibility breaks down when every supplier uses different formats and tooling.
Outcome: Normalized supplier and site data with lineage and quality signals you can feed into risk and planning tools.
Example output: Multi-tier supplier lists, risk flags, and integration-ready datasets for ERP and analytics.
Vertical scenario
Who: Revenue, strategy, and data teams
Problem: CRM and static firmographics miss how companies actually participate in ecosystems and supply chains.
Outcome: Richer context from structured network and company data for prioritization, targeting, and insights.
Example output: Enriched accounts, relationship strength, and signals for agents and workflows you already run.
Vertical scenario
Who: Product and platform teams launching network businesses
Problem: Launching a marketplace means coordinating data, participants, and rules across many independent actors.
Outcome: A governed network layer for participants, transactions, and shared data—without forcing a single app on everyone.
Example output: Participant catalogs, transaction events, and APIs that plug into your front-end and finance stack.
Scroll through sector examples—all powered by the same governed operations runtime: integrations, process automation, Datapools, and policy-bound agents in one tenant.
Build platforms that manage material reuse, track product lifecycles, connect waste streams with markets, and unify supplier ESG data.
Create a unified marketplace for complex services, including bookings, payments, and quality control.
Connect city needs with providers like e-scooter fleets, charging stations, and last-mile delivery drones.