Strategy

We usually start with a mixed team of platform engineers, product leadership, and delivery management embedded with client stakeholders. That lets us map dependencies, validate migration paths, and decide where parallel builds make sense versus where in-place enhancement is the safer move.

• Parallel replacement when the operational risk of direct migration is too high
• Incremental modernization when live systems must keep serving without interruption
• Staffing that blends senior modernization leads with engineers who can ship inside legacy constraints

Success Stories

• We are leading the effort to make the FAFSA loan program more resilient across multiple vendor systems by improving end-to-end testing and system observability.
• Our team is building a modern passport adjudication system to replace the on-premise legacy system while preserving integration with down-stream systems.
• We are incrementally modernizing the Grant.gov system while it continues to serve users. We started with the search functionality an are now working on applications.

Strategy

Our approach combines service understanding, rules analysis, and delivery planning so modernization work reflects how adjudication actually happens. We typically staff these engagements with policy-aware delivery leads, and engineers who can work through case workflows, decision logic, and integration dependencies together.

• Service and workflow mapping across intake, review, and determination steps
• Delivery teams that can translate policy and operational rules into usable systems

Success Stories

• We're working closely with adjudicators across different sites to ensure the new Passport Adjudication system serves their needs.
• In Massachusets, our team led the effort to implement the system that coordinates and integrates the application, to adjudication, to payments systems.

Strategy

We rely on structured evaluation frameworks, to measure and enforce consistency of LLM outputs before and after changes, so teams can ship with confidence rather than guesswork. We bias toward narrow, high-value implementations first, then expand once accuracy, trust, and operational fit are proven.

• Rapid discovery to identify practical AI use cases tied to mission outcomes
• Eval-driven development to ensure LLM output consistency across iterations and deployments
• Prototype-first delivery with human review built into the workflow
• Cross-functional staffing that covers model behavior, integration, and change management

See our playbooks to using AI assistance for more effective discovery, design, and implementation.

Success Stories

• At the U.S. Department of State, we're exploring how LLMs can deliver the right policy and guidance at the moment it’s needed, giving adjudicators instant access to a complex body of policy resources to make an informed decision.
• We use LLMs to conduct deep research across legislation, rules, and public records before a project begins, so we arrive at kickoff already grounded in your context, ready to ask better questions and get to work faster.

Strategy

We embed security work as part of delivery, not as an external gate at the end. That usually means platform and application engineers working alongside security specialists, with controls designed into architecture, release workflows, and operational practice from day one.

• Zero-trust oriented architecture and access design
• Embedded security review inside sprint delivery rather than separate audit cycles
• Teams staffed to balance compliance requirements with practical engineering throughput

Success Stories

• At the U.S. Department of State, we created the consular division's first cloud infrastructure platform as part of our work on Online Passport Renewal, transferred it to the security team, and played a key role in achieving both application and platform ATO in collaboration with the security team.
• At FAFSA, we're embedding security considerations into our end-to-end testing and observability work to catch issues before production across multi-vendor systems.