Exception triage, not S&OP scale. Which signal matters now?
Storage and data-fabric companies don't run a 5,000-SKU planning operation. They run a small SRE team, a small CSM bench, and an engineering org that ships custom-integrated systems into demanding customer environments — and every one of those teams generates exception traffic across Datadog, PagerDuty, Jira, GitHub, and Salesforce Service. OpsATC.AI is the AI-native orchestration layer above that stack. The Captain reads the live signal across all of it, ranks incidents by customer-revenue impact, surfaces the deployment-issue that's about to turn into an NRR conversation, and answers "what should I look at first this morning?" with a cited line — not a hallucinated paragraph.
"Our ops volume is too small to justify a control tower."
It's a reasonable objection if "control tower" means S&OP at distribution scale. That isn't what this is. OpsATC.AI for a storage or data-fabric OEM isn't measured in pallets routed or POs reconciled — it's measured in which signal matters now, across an engineering and customer-success stack that generates exception traffic faster than a small team can manually thread together. The smaller the team, the higher the per-engineer leverage from automating triage.
The five drains that consume an engineering-led ops team.
In the discovery conversations we've had with storage and data-fabric OEMs, a recurring pattern keeps surfacing — different products, different scale, the same five drains across SRE, CSM, and product engineering. The Captain is designed around exactly these.
Alert fatigue without customer-impact ranking
Datadog, PagerDuty, OpsGenie, and your APM stack fire all night. Half of them resolve themselves. The remaining half need to be ranked by which customer is actually affected, which revenue is at risk, and which deploy ticket they correlate to — and that ranking is currently a human in a chair at 7am with five tabs open.
Cross-tool stitching
The alert is in Datadog. The incident is in PagerDuty. The bug is in Jira. The fix is a PR in GitHub. The customer conversation is in Salesforce Service. All five are about the same thing. None of them know it. Threading them together is the swivel-chair tax your SREs and CSMs both pay daily.
CSM intervention timing
By the time the customer-success lead sees the renewal risk in the QBR slide, the relationship is two months past the moment a single proactive email would have changed the outcome. The signals that should have triggered the email — deployment friction, support volume drift, feature adoption decay — were sitting in three different tools nobody was watching together.
NPI velocity across customer deployments
You ship a new firmware revision, a new connector, a new policy engine. It rolls out across a dozen customer deployments at different rates, hits different edge cases at different sites, and the rollout-status truth lives in a Jira board, a deployment dashboard, and the heads of three field engineers. Nobody has the rollout-by-customer picture without spending a Friday building it.
Tribal knowledge in two engineers' heads
Your senior SRE remembers the last time this exact alert pattern preceded a customer-impacting outage. Your principal CSM remembers which customers tolerate maintenance windows and which escalate to the CRO. Both pieces of knowledge live in two human heads. When either is on PTO, the team makes the wrong call.
All five run through the same orchestration layer
The Captain doesn't replace your SREs, your CSMs, or your product engineers. She compresses the time from signal to decision — for all five drains, in the same agent, with the same audit trail, and across a tool stack you already pay for.
Read · reason · cite · draft. Operator approves.
The Captain reads your live systems via MCP — the ERP that holds the SKU master, the PLM that holds the firmware bundle and qual gate, the quality and test platforms that hold the build-record and characterization data, the RMA system that holds the field-return history, and the logistics platform that ties shipments back to customer-side acceptance. She reasons across them to keep every SKU traceable from firmware build to customer bundle, detects RMA failure patterns across SKUs and cohorts before they become field-quality escalations, and drafts cited recommendations for each role. She stops at the operator. Every commit happens in your existing tool — PLM, QMS, or the RMA workflow — with the source records cited and the audit log captured at the protocol boundary.
Concrete workflows. Concrete outcomes.
SRE · 7am incident roundup
A single morning brief that reads the overnight Datadog + PagerDuty + GitHub deploy stream, correlates each open incident to the customer deployment it's degrading, ranks them by customer-revenue impact, and proposes the first hour's triage order with citations.
CSM · NRR-influencing intervention timing
The Process Intelligence Engine watches deployment-friction signals (support volume rising, feature adoption flat, deploy ticket aging) per customer. When the pattern matches the signals that typically precede a renewal conversation going sideways, The Captain drafts the proactive outreach with the citations the CSM needs.
Engineering · Cross-tool stitched thread
The Datadog alert, the PagerDuty incident, the Jira ticket, the GitHub PR, and the Salesforce Service case all converge into a single citable thread on the customer it affects. Each tool's record carries the link back to the thread so the picture is consistent regardless of which tool an engineer opens first.
Product GM · NPI rollout-by-customer view
Every firmware revision, connector release, or policy-engine update gets a per-customer rollout view — which sites are on which version, which deployments hit edge cases, which field engineer owns the resolution. The Process Intelligence Engine quantifies the rollout lag and recommends the next site to push.
Per-persona outcome targets — measured against your baseline.
Design-stage targets, not promised magnitude. The first design-partner pilot is where the delta gets measured against your operator baseline. Below: where The Captain is built to move the needle, by role.
45-minute roundup → 5-minute review
Designed to compress the morning "what happened overnight" call from a 45-minute multi-tool synthesis to a 5-minute review of a stitched, customer-impact-ranked draft.
Traces to: SRE · 7am incident roundup
Renewal signal in weeks, not at the QBR
Designed to surface deployment-friction patterns per customer weeks before the QBR autopsy — so the proactive outreach happens in time to change the outcome.
Traces to: CSM · NRR-influencing intervention timing
NPI rollout-by-customer, live
Designed to convert the Friday-build NPI rollout-by-customer spreadsheet into a live dashboard reading Jira + deployment pipeline + customer-side telemetry as one.
Traces to: Product GM · NPI rollout-by-customer view
Five tools, one stitched thread
Designed to converge the Datadog alert, the PagerDuty incident, the Jira ticket, the GitHub PR, and the Salesforce Service case into one citable thread per customer — eliminating the cross-tool stitching tax.
Traces to: Engineering · Cross-tool stitched thread
Pre-built MCP connectors for the engineering-led ops stack.
OpsATC.AI sits on top of your existing investments — your observability stack, your incident-management platform, your source-control and deployment pipeline, and your customer-success and billing systems. Nothing gets retired. Read-only connectors via Model Context Protocol, with audit trails at the protocol boundary.
Reference adapter implementations are scaffolded for these platforms and validated against synthesized fixtures from public API documentation. Partner-sandbox re-records are pending; production validation happens during the first design-partner pilot. See platform integrations for the full reference-vs-scaffolded breakdown.
Observability & MonitoringMetrics, logs, traces, alerts
Incident & ITSMOn-call, paging, ticket routing
Source & DeployCode, builds, releases
Customer Success & CRMRenewal, health, support cases
Billing & OperationsSubscription, usage, finance
The IT lift is smaller than most CTOs expect.
No data lake. No tracing-pipeline rework. No alert-rules migration. The Captain reads your existing observability, incident, source-control, and CRM stack live via MCP — and adapts on operator feedback, not retraining cycles. See the Day 1 to Day 90 timeline →
What we need
- ✓Read-only API tokens per system you want orchestrated
- ✓Read-only service accounts on your observability and ITSM platforms
- ✓Allow-list approval for OpsATC.AI's egress addresses
- ✓One-time mapping of customer-deployment identifiers across tools
- ✓A scoping conversation about your KPIs, your role personas, and your operational vocabulary
What we don't need
- ✗Historical metrics extraction from your data warehouse
- ✗A new agent installed on your customer-facing appliances
- ✗Alert-rule rework or tracing-pipeline migration
- ✗An S&OP planning footprint
- ✗Customer-facing telemetry collection beyond what you already do
Your storage-OEM data is dirty when we start — drift between PLM and shop floor, missing firmware revisions on registered serials, RMA records orphaned in service systems, support-contract entitlements out of sync with shipped asset, telemetry feeds that drop fields after a firmware update no one tracked. The Captain Data Quality Detection Layer runs continuously: baseline at MCP connect, inline on every read, scheduled per record type, on-demand when an operator asks. Six issue classes, four detection modes, all surfacing through the Trusted Advisor card. No six-month cleanup project. See the full Data Governance architecture →
Bring your worst overnight. We'll walk through how it changes.
Thirty minutes, the last incident that took two engineers four hours to thread together. We'll walk through how the orchestration layer changes the morning brief, the customer-impact ranking, and the cross-tool stitching. Written diagnosis within one business day.