AI Readiness & Data Roadmaps
An honest assessment of where your data, systems, and governance stand, and a phased plan to get AI into production.
- Inventory of systems of record, data quality, and integration debt
- Use-case prioritization tied to mission outcomes and measurable value
- Target architecture, governance model, and phased implementation plan
API-Led Integration
MuleSoft system, process, and experience APIs that connect case management, CRM, ERP, and legacy platforms so AI works from authoritative data.
- API contracts (RAML/OpenAPI) and reusable integration assets
- Legacy modernization through APIs rather than rip-and-replace
- DevOps automation and a Center for Enablement (C4E)
AI Agents & MCP
Connect AI agents to your systems through governed APIs and the Model Context Protocol, so every agent is registered, secured, and observable.
- APIs turned into governed, least-privilege agent tools
- Human approval for high-risk actions
- Agent activity logged to your SIEM
AIOps
Event correlation, anomaly detection, and safe automation grounded in your service and operations data.
- Noise reduction and event correlation across monitoring tools
- Anomaly detection on operational and integration telemetry
- Automation with guardrails and approval steps
Data & Analytics
Pipelines, data quality controls, lineage, and Databricks analytics that give program leaders one trusted view.
- Pipeline design (ETL/ELT) with data quality controls
- Lakehouse and analytics platforms, including Databricks
- Dashboards decision-makers actually use
Secure AI & SecOps
Zero-trust design for APIs, data pipelines, and AI, with SIEM integration and hands-on SOC experience.
- Least-privilege access for users and AI agents
- Protection of sensitive data (PII, PHI) in AI workflows
- Monitoring and audit through Splunk or your SIEM
Let's talk about your mission
Tell us which systems your teams rely on and where AI could help. We will come back with a practical first step.