From AI strategy to working systems.
Direction, governance, engineering and training, delivered alongside your own team.
AI strategy and governance
For organizations with AI in pockets and no direction. We give you a clear plan, a policy people can follow, and a short list of use cases worth doing first.
What you get
- Review of current tools, accounts and data flows
- Tool-neutral evaluation of enterprise AI assistants, frontier models and open-source options
- AI use policy and data classification
- Risk and compliance mapping (NIST AI RMF, HIPAA where it applies)
- Ranked use cases with a baseline to measure against
A good fit when
- Vendors keep pitching and you need a way to judge them
- Leadership uses different AI tools and wants a standard
- Sensitive data must be protected before anything is rolled out
- There's no AI owner or policy yet
Applied AI and ML engineering
Working solutions on your own data, built with your developers. We start with a focused pilot, read-only by default, and validate real-world performance before expanding scope.
What we build
- Operational analytics: spend, yield, throughput, loss and outlier detection
- Knowledge assistants with retrieval (RAG) that cite their sources
- Agentic workflows with permissions, audit logs and human approval
- MCP servers that give models governed access to your systems
- AI-assisted engineering workflows for your development team
How we build it
- Private inference, dedicated VPC and self-hosted open-source deployments when compliance requires data to stay within your tenant boundary
- Frontier models under approved enterprise agreements when the data allows
- Governed data access: read-only by default, with two-way actions gated by approval workflows and audit trails
- Evaluation loops so quality is tested, not assumed
- Documentation and handover to your team
Data integration and modernization
AI is only as good as the data underneath it. We build and modernize the pipelines, APIs and platforms that make your data trustworthy and reachable.
What we do
- ETL and event pipelines for HL7, FHIR, CDA, XML, CSV and JSON
- REST and GraphQL APIs, including gateways between CRM, ERP and core systems
- Legacy-to-cloud modernization on AWS and Azure
- Data quality audits and schema validation
Engineering practice
- Clean architecture and domain-driven design
- Automated testing and CI/CD, with infrastructure as code
- Observability: tracing, structured logging, monitoring
- Authentication and authorization with OAuth 2.0, Okta and Auth0
Training and enablement
Tools only pay off when people use them well. We train by role, on real tasks, and leave playbooks your team keeps current.
What you get
- Role-based training on the tools you choose
- Prompt and workflow playbooks built from your real tasks
- Practical guidance on validating model outputs and safely managing sensitive data across authorized tools
- Adoption measured against the baseline
Ongoing support
- Operations and maintenance for what we build
- Agile project delivery and project management
- Hosting and infrastructure support
- Monitoring and maintenance after launch
Organizations where the data matters.
Healthcare and life sciences
Public-health agencies, health systems and clinical laboratories, where data is regulated and decisions affect patients.
Financial services
Banks and lenders integrating AI under strict data-protection rules, with auditability expected from day one.
Public sector and nonprofits
Federal, state and local agencies and mission-driven organizations that need modern systems on realistic budgets.
Not sure where to start? Start with a conversation.
Tell us what you want AI to do for your organization and what worries you most. We'll tell you plainly whether and how we can help.