Four capabilities, each tied to an outcome you can measure.
We modernize the systems your people rely on, connect them so data moves without rekeying, and take AI into production with the oversight your reviewers expect. If you're already using AI in many places, we build the operating model that ties it together. Each capability rests on work we've delivered.
Mission workflow modernization
Fewer hours and fewer handoffs in everyday work.
We rebuild the systems and workflows staff rely on every day: workforce tools, case management, legacy applications, and the data behind them. We start with field research and measure success in hours saved and handoffs removed.
What we've shipped
Modernization and enhancement of the USDA Forest Service workforce management tool, including a generative AI prototype for exploring workforce data. Systems work for HHS, CMS, NHLBI, and Fannie Mae.
How we work
Field research with the people doing the work produces a work map with a person lane and an AI lane. Service blueprints show where time goes and which tasks AI can take. Incremental releases replace legacy systems piece by piece, with training and adoption support.
What you get
A system people use, measured gains in cycle time and staff hours, and a codebase your team can keep improving.
Decisions that carry legal or mission accountability stay at level 2 or below.
Systems engineering and integration
New and existing systems that share data reliably.
We engineer how systems fit together: the architecture, interfaces, APIs, and data exchange between what you run today and what you're adding. That includes AI components and commercial products that have to work inside an existing environment and pass its security review.
What we've shipped
Consolidation of eight GSA acquisition systems into a single platform, as a subcontractor, with web interfaces, microservices, APIs, and automated end-to-end tests. Integration of AI-enabled platform components with existing systems on a federal defense acquisition modernization program, as a subcontractor. A workflow platform for a commercial software company, deployed across several federal agencies.
How we work
We map the systems, interfaces, and data flows before changing anything, then integrate in increments behind automated end-to-end tests. Interface contracts and architecture records stay current, so your team and your ATO reviewers can see what connects to what.
What you get
Systems that exchange data without manual rekeying, a documented architecture, and integrations your team can maintain.
AI drafts interface specs, data mappings, and tests. An engineer reviews and approves them.
Governed AI, pilot to production
AI that can pass a governance review.
Many AI pilots stall before production because nobody built the testing, monitoring, and human review that federal policy now requires for high-impact AI. We build those in from the first sprint, so a pilot can move to production without starting over. Commercial teams in regulated industries get the same controls for their risk and compliance reviews.
What we've shipped
AI assistance and governance in a federal defense workflow platform, as development subcontractor to the prime: AI drafts and flags, and people decide. A plain-language data assistant for a federal workforce management system, built and tested in a sandbox.
How we work
We select use cases with an impact assessment and give each AI task a starting Earned Autonomy level. We build retrieval and agent workflows with evaluation suites and red-team tests, add review queues where a person approves the output, and monitor overrides after launch. A task moves up a level only when the evidence supports it.
What you get
A system with the test results, monitoring, and documentation your AI governance board asks for. You own the code and the data.
Pilots start at level 1 and move up only when evaluation results support it. Our workforce-tool data assistant runs at level 1 in a sandbox today.
WithAI operating model
One plan for the AI work already running.
Most organizations already use AI in many places, without a plan that ties it together. We start from the AI work actually running, tie it to business objectives, set how much AI can do based on evidence, and put governance where the work happens. We implement what we recommend.
What it builds on
Our own delivery teams already work this way: AI drafts code, tests, and documents, and engineers review. The model draws on our Ground Truth research. We're taking on a small number of pilot engagements before offering it more widely.
How we work
Engagements begin with an Estate Review of the AI work already running. We apply the operating model to one or two use cases from that review, then extend it across the organization with the decision rights, routines, and controls it needs.
What you get
An inventory of the AI work running, each item mapped to the objective it serves, a starting Earned Autonomy level for each use case, and a recommended pilot.
Each AI task in the estate gets a starting level from the evidence available. Accountable decisions stay at level 2 or below.
All four capabilities run on WithAI.
With-AI is our delivery method. We start with the work, test anything automated against known answers, and let automation take on more only when the evidence supports it. Six practices carry it through every engagement.
See the methodTell us what you're trying to deliver.
Tell us about the program. We'll reply with specific questions and a proposed path, not a pitch deck.