Enterprise Platforms · ATLAS
ServiceNow ITSM, ITOM, HRSD, and custom workflow apps — implemented in scoped applications, deployed through pipelines, and engineered for continuous upgrades. Audit-grade governance, named release-train discipline, and Now Assist where the workload warrants it.
The problem
The shape: an ITSM implementation from the Eureka era, customized in the global scope with thousands of script-includes and business rules; upgrades treated as ten-month projects; an HRSD module bolted on without rationalizing the workflow surface; a CMDB nobody trusts; and a discovery process that produces audit findings rather than configuration items. ServiceNow's platform is exceptional; most implementations don't keep pace with it.
We implement and remediate ServiceNow with continuous-upgrade discipline: customizations live in scoped applications with explicit dependency boundaries; deployments run through pipelines (not update sets manually exported); upgrade impact is documented per artifact; and the operating cadence stays current with the platform's three releases per year. Most clients we work with run on the current LTS within three months of release rather than three years behind.
Where it ships
Specific applications we’ve built and operated. Not speculative — every example below is grounded in a real shipped engagement.
−52%
ticket cycle time
Incident, problem, change, knowledge, request — implemented or refactored on the current LTS. Workflow rationalization, SLA design, and Performance Analytics for the metrics that drive operating discipline.
Discovery, Service Mapping, and CMDB design — with explicit data quality rules, federation patterns where third-party data sources matter, and the governance frame that keeps the CMDB trustworthy.
Employee Center, Case Management, and Knowledge — integrated with HRIS (Workday, SAP SuccessFactors) through documented interface contracts. Lifecycle events, journey workflows, and HR-grade audit logging.
Industry-specific or company-specific workflow apps built on App Engine — with proper data model design, App Engine Studio governance, and the same discipline that scoped-application customizations get.
AI-assisted summarization, agent assist, and citizen-developer surfaces. Scoped to use cases where the value is real — with the data governance, refusal patterns, and audit trail clinical-grade AI requires.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans — operating rules.
Audit existing instance: customization footprint, scoped-vs-global usage, upgrade lag, performance hotspots, integration topology. Output is honest assessment with prioritized remediation roadmap or a 'this is a reimplementation' recommendation.
Customizations live in scoped applications with explicit dependency declarations, not in the global scope. Naming and namespacing follow platform conventions. Update sets are an artifact of legacy delivery — we deploy through pipelines, with PR review.
Every custom artifact documented with upgrade impact. ATF (Automated Test Framework) suites that run on every release. Deprecated platform features identified before they bite. Most artifacts survive upgrades without rework.
Quarterly upgrade cycles aligned to ServiceNow's release cadence. Performance Analytics dashboards for the metrics that drive operating discipline. Quarterly backlog grooming on technical debt. The platform compounds value over upgrades — not despite them.
Capabilities
Stack
Selected work
Common questions
Yes — we hold partner status with ServiceNow. Partner status gives us roadmap intelligence and direct support escalations; it does not bias our recommendations. We've recommended different platforms when ServiceNow wasn't the fit, and we've recommended migrating off ServiceNow when the customer's workflow surface had outgrown the platform's economics.
Yes — Eureka-, Geneva-, Helsinki-, and Madrid-era upgrades are common engagements. We assess customization debt first, recommend a remediation path (often via reimplementation in scoped apps for the worst customizations), and execute the upgrade through ATF-validated waves. Most multi-version upgrades land in 4–8 months.
Yes — and we'll tell you when 'fix the CMDB' is the wrong starting point. CMDB programs that don't have clear consumer use cases tend to become data-cleanup exercises that never finish. We start with the use cases (impact analysis, change risk, service availability), then design the CMDB scope and federation patterns to serve them.
IntegrationHub and Spokes for vendor-supported integrations. REST / SOAP through ScriptedRESTAPI for custom interfaces. MID Server for on-prem connectivity. We document interface contracts, version them, and treat integrations as primary scope rather than 'phase 2'.
Scoped to use cases where the value is real — agent assist, summarization, knowledge search, and citizen-developer surfaces. We deploy with refusal patterns, audit trail, and the data governance that audited industries require. We will tell you when an AI use case is hype-driven rather than value-driven.
Instance health audit: 2–4 weeks, $30K–$80K. ITSM implementation: 6–10 months, $400K–$1.2M. ITOM / CMDB program: 6–12 months, $400K–$1.5M. HRSD deployment: 4–8 months, $300K–$900K. Multi-module enterprise programs: $1.5M–$5M+. Managed Services: $30K–$150K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.
Within Enterprise Platforms
Talk to us
A senior engineer plus the ATLAS department lead joins the first call. No discovery gauntlet, no junior reps.
