Capabilities built around geospatial modernization.

Problems we address

• Legacy ArcGIS environments with unclear dependencies, aging servers, service sprawl, or inconsistent publishing patterns.
• Upgrade pressure without a shared view of risk, sequencing, validation, or rollback.
• Development, test, and production environments that have drifted from each other.
• Old geoprocessing scripts, unsupported runtimes, manual deployments, and fragile operational knowledge.

What we help design

• Current-state assessment covering platform, data, services, integrations, operations, and governance.
• Target architecture for ArcGIS Enterprise, cloud/hybrid patterns, identity, data stores, and service tiers.
• Phased roadmap that separates urgent stabilization from long-term modernization.
• Migration, validation, cutover, rollback, and stakeholder communication patterns.

Typical deliverables

• Modernization roadmap.
• Architecture decision record set.
• Environment strategy and migration plan.
• Service inventory and risk matrix.
• Operational readiness checklist.

Questions we clarify

• Which systems are truly business-critical?
• What must not break during modernization?
• Which parts should be upgraded, replaced, retired, or left alone for now?
• How will the new environment be governed after launch?

Architecture areas

• Identity, SSO, role models, groups, service accounts, ownership, and access boundaries.
• Portal, Server, Data Store, web adaptor, federation, reverse proxy, and secure publishing considerations.
• Data exposure models for internal users, partners, contractors, public applications, and executive reporting.
• Auditability, data classification, backup/recovery, disaster scenarios, and operational resilience.

Practical questions

• Who can see what, and why?
• Which datasets should be services, extracts, views, or internal-only sources?
• Where are the trust boundaries between GIS, IT, data, vendors, and users?
• What happens when a credential, service, machine, data source, or public endpoint fails?

Typical deliverables

• Secure publishing model.
• Identity and access architecture notes.
• Role/group model recommendations.
• Data exposure and service boundary review.
• Governance and operational risk register.

Why it matters

Geospatial security is not just login control. Location data can reveal patterns about people, assets, operations, infrastructure, environmental exposure, and public services. A secure architecture keeps useful data available while reducing unnecessary exposure.

What we work with

• Enterprise geodatabases, Oracle Spatial/SDE, PostGIS-aware patterns, file geodatabases, rasters, imagery, tabular inputs, APIs, and service layers.
• FME/ETL pipelines, scheduled processing, validation gates, exception handling, and data movement.
• QA/QC for geometry, projection, topology, schema, metadata, naming, lineage, and publishing readiness.
• Data models that support operations, reporting, analytics, and long-term maintenance.

Automation approach

• Repeatable jobs include validation, logging, recoverability, and owner clarity.
• Processing patterns reduce manual effort while improving trust in the result.
• Automation is designed around the data lifecycle: intake, transform, validate, publish, monitor, and retire.
• Manual steps are not removed blindly; they are converted where consistency, speed, and risk reduction are clear.

Typical deliverables

• Spatial data pipeline architecture.
• Validation and QA/QC checklist.
• ETL design notes and runbook.
• Data lineage and ownership model.
• Operational automation roadmap.

Questions we clarify

• Which datasets are trusted sources of record?
• Where do geometry, projection, schema, or metadata issues enter the process?
• Which jobs need scheduling, validation, alerting, and recovery?
• How should outputs be published so users know what they can trust?

Reliability areas

• Service performance and ArcGIS Server/Portal health patterns.
• Log analysis, queue indicators, timeouts, slow data access, and resource contention.
• Capacity planning for compute, storage, file shares, database, network, and service instances.
• Runbooks, operational dashboards, threshold definitions, and incident patterns.

Signals we look for

• Which services are slow, when, and under what workload?
• Is pressure coming from compute, file storage, database, network, service configuration, or architecture?
• Which recurring failures should become operational controls?
• What does the team need to see before a service issue becomes a business issue?

Typical deliverables

• Monitoring and reliability review.
• Performance investigation summary.
• Service health dashboard concept.
• Capacity and bottleneck findings.
• Operational runbook and escalation notes.

Why it matters

Slow geospatial systems are rarely solved by one setting. The real bottleneck may be service instances, raster access, file shares, database calls, network paths, logs, dependencies, or deployment choices. Monitoring gives the team a way to separate opinion from evidence.

Common use cases

• Risk scoring and spatial prioritization for properties, assets, service areas, and infrastructure.
• Infrastructure, access, service-area, proximity, and constraint analysis.
• Environmental context, regulatory datasets, imagery interpretation, and reporting support.
• Dashboards and decision tools that explain why something matters, not just where it is located.

Modern capability language

• Predictive spatial patterns.
• Machine-assisted geospatial workflows.
• Advanced spatial intelligence.
• Human-in-the-loop decision support.
• Data foundations that make intelligent systems possible.

Typical deliverables

• Location intelligence opportunity map.
• Risk/priority model design.
• Dashboard and decision-workflow concept.
• Data readiness review.
• Explainability and governance notes.

Questions we clarify

• What decision is the spatial analysis supposed to improve?
• Who needs to trust the result?
• Which spatial factors should be explainable?
• Where should human review remain part of the workflow?