In trading and market infrastructure, outages are not operational problems — they are P&L events. ReliOps gives SRE and platform teams a concrete way to map where the next costly incident originates, how far it spreads, and which fix delivers the highest risk reduction per engineering dollar. Enterprise engagement adds private deployment, data isolation, and a leadership-ready reliability readout.
A leadership-ready readout of concentration risk, critical-path exposure, and the remediation work with the highest risk reduction per engineering dollar.
Run ReliOps in your own environment with local model options for strict data-isolation and regulated operating requirements.
Service audit, blast-radius analysis, incident-pattern review, and response-plan generation in one workflow instead of scattered spreadsheets and tribal knowledge.
The team already firefighting cross-service fragility. ReliOps gives them a concrete way to prove where risk is systemic, not anecdotal — and a remediation backlog ranked by impact.
ReliOps ties reliability work directly to outage exposure, critical-path concentration, and prioritized risk reduction — not generic observability noise.
The strongest wedge is where dependency chains are dense, incident minutes are expensive, and resilience decisions visibly affect revenue, clients, or regulatory posture.
The core is portable, inspectable, and useful without a sales conversation. Validate the workflow on your own terms.
Teams can run the core locally, inspect the logic, and validate the product against their own architecture.
The open product proves the workflow: upload service definitions, surface critical issues, inspect blast radius, and review incident patterns.
Open source builds trust. It tells technical buyers they are adopting a transparent reliability model rather than a black box.
Speed, deployment quality, calibration, and a business-ready operating model for regulated environments.
Enterprise engagements start at $150K/year — aligned with comparable finserv SRE tooling.
Ingest your service definitions, run initial audit, deploy in your environment.
Map dependencies and blast radius. Deliver first risk report to SRE and leadership.
Prioritized remediation plan with fix-level guidance. Board-ready reliability risk assessment.
In trading and market infrastructure, outages are not just technical defects. They hit execution quality, client confidence, and operating risk immediately.
Order routing, pricing, market-data ingestion, risk checks, and notification chains often fail through coupling. ReliOps is strongest where that coupling is hard to see early.
Once the financial-services wedge is established, the same story extends to any environment where uptime, compliance, and critical-path dependencies matter.
Tools like BigPanda and Selector AI correlate alerts after something breaks. ReliOps maps dependency risk and blast radius before the first alert fires — from config files your teams already maintain.
AIOps platforms need months of connector setup into every alerting and ITSM tool. ReliOps starts from YAML, Helm charts, and Terraform you already have.
AIOps thinks in alerts and incidents. ReliOps thinks in dependency chains and concentration risk — the signals that matter before outages happen.
Neither BigPanda nor Selector AI lets you inspect the scoring logic. ReliOps's 8-rule engine is open-source — explain every finding to your risk committee.
Run ReliOps as a focused pilot on a critical dependency path — order routing, market data, or settlement. Use the results to justify broader adoption.