Data engineering & integration · Data quality & reconciliation
02 AI & Machine LearningApplied machine learning · Generative AI
03 Software & Product EngineeringCustom application development · Legacy modernization
04 Cloud & CybersecurityCloud migration & modernization · Cloud operations & support
05 Acquisition & Financial ManagementAnalysis of Alternatives · Market research & COTS/NDI evaluation
06 Program & Project ManagementProgram management & PMO support · Process improvement
Reconcile the record · instrument the definitions · ship the smallest thing that runs · close the loop.
Readiness, sustainment, acquisition and program financial systems.
IntelligenceSecure analytics, transcription and technical talent for missions.
Federal CivilianEnterprise cloud, case workflows and public financial accountability.
State, Local & TribalFederal-grade accountability on a state or municipal budget.
HealthReliability, forecasting and workforce analytics for clinical operations.
Commercial & Small BusinessFor organizations that must defend an AI-assisted decision.
Where a customer can't be named, the component and scale are characterized instead.
ALL WORKGAO, CBO and IG findings, read closely and written up plainly.
ALL INSIGHTSEngineering, data science, and Ghost Works.
OPEN ROLES INTERNSHIPSHands-on projects and practical challenges, including our Drone Development internship.
EXPLORE INTERNSHIPS →Alternatives analysis survives scrutiny when every figure traces to a source and every assumption is named in the open.
A business case is not a document produced at the end of an analysis. It is a structure imposed at the beginning: what decision is being made, by whom, by when, and what threshold would change the answer.
Without that structure, studies drift toward thoroughness. They answer adjacent questions exhaustively and the actual question obliquely, and they arrive after the decision has already been made informally.
The discipline that makes an analysis hold up is unglamorous. Every figure traces to a source. Every assumption is named where a reviewer can find it rather than buried in a model. Sensitivity says plainly which assumptions move the recommendation and by how much.
And when two alternatives are indistinguishable within the uncertainty, say so. A false precision that collapses under questioning costs a program more credibility than an honest non-answer.
The test we use internally: could a reviewer who dislikes the conclusion reproduce it? If not, it is not finished.
If this describes a problem your program is carrying, the conversation starts with the question two systems answer differently.
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