Practices
Technical governance that turns delivery from a per-team improvisation into an enterprise standard — plus the roadmaps and business cases that get it funded.
The problem
Twelve teams, twelve pipelines, twelve readings of what “secure” means — and a security office auditing all of it by hand.
What you get
- A governance framework teams adopt because it is faster than rolling their own
- Technical maturity dashboards with measurable KPIs, not status decks
- Modernization roadmaps costed and sequenced for a budget cycle
- Executive-level advisory that survives contact with a CIO review
Migration, multi-cloud architecture, FinOps and managed operations across AWS, Azure and GCP.
The problem
A migration that stalls halfway, or a cloud bill nobody can explain to the CFO.
What you get
- Workload assessment and a migration sequence tied to mission risk
- Architecture that survives an accreditation review, not just a demo
- Infrastructure as code, so environments are reproducible rather than remembered
- FinOps practice that makes spend attributable to a programme
Zero-trust architecture, continuous ATO enablement, and compliance-as-code with automated evidence collection.
The problem
ATO slips, and the delivery schedule goes with it. Evidence gets assembled by hand for every review.
What you get
- Continuous ATO with RMF-aligned pipeline outputs
- Compliance evidence produced continuously rather than reconstructed
- SAST, SCA, DAST and container scanning enforced in the pipeline
- Policy as code, so a standard is a gate rather than a memo
MLOps platforms already delivered in a federal mission environment — pipeline orchestration, model training and monitoring, plus the governance federal AI accountability now demands.
The problem
Models that work in a notebook and nowhere else, with no answer to how a decision was reached.
What you get
- An MLOps platform models actually run on, not a proof of concept
- Training, serving and monitoring wired into the same DevSecOps discipline
- Drift and performance monitoring, so degradation is caught before a user reports it
- AI governance and model accountability suited to federal scrutiny
Data engineering, governance and analytics that turn mission data into decisions leaders can defend.
The problem
The data exists, but nobody can get to it, and no two reports agree.
What you get
- Pipelines that are automated, monitored and reproducible
- Governance that makes lineage and quality auditable
- APIs that expose mission data safely to the systems that need it
- Analytics a leader can put in front of an oversight body
Containerization, microservices and Strangler-pattern replacement of legacy systems without downtime — architected for reuse, so each modernization compounds instead of starting over.
The problem
A legacy system nobody wants to touch, and no safe path off it.
What you get
- Incremental replacement via the Strangler pattern, not a big-bang rewrite
- Blue/Green deployment, so cutover is not an outage
- Chaos engineering and SRE practice to prove resilience before it is tested for real
- Reference architectures the next programme can reuse
