Federal agencies need to modernize critical systems, strengthen security and put AI to work – all while delivering the mission every day. We add the people, technology and AI capacity to move modernization forward without slowing the work that matters.
Measured in production, not in pilots.
users supported across 800+ installations
13,000
pages processed, with a person making the determination
10M
staff hours returned through automation, across programs
100,000+
a year saved, from one deployed federal workflow
$1M
years supporting the federal government
32
Four of our missions. One way of working.
Catch network failures before users experience them.
Government-wide enterprise IT · 13,000 users · 800+ sites
Problem
Hundreds of sites ran on infrastructure that could not be taken down, with no single modernization roadmap to run it against.
AI at work
The AI Network Engineer forecasts the failure, raises the event, diagnoses the cause and proposes the remediation.
Human role
A human engineer approves the change and owns it. Nothing is applied without that sign-off.
Result
Failures are caught days before users feel them and the staff hours go back to the agency.
3 days
advance network-failure detection, at 90% accuracy
Clinical review volume outran reviewer capacity. Every claim needed a documented determination, and the backlog grew faster than staff could be added.
AI at work
The AI Medical Records Reviewer classifies 25 document types and bookmarks the page that carries the evidence, so cases that need judgment reach reviewers first.
Human role
A clinician makes every improper payments determination. No change to who decides, increasing reviews with no reduction in review staff.
Result
Document-review time fell by more than 30% across roughly ten million pages processed.
A case-processing system took an hour to return one bulk download. Analysts planned their day around the wait, and the wait decided which questions got asked.
Technology at work
The workflow and the API behind it were refactored, and the data path rebuilt on the same data engineering core used across the portfolio.
Human role
Analysts decide which questions to ask, not the system. The same team now works from graphical analysis instead of tables.
Result
The hour became ten minutes, and we recorded the fastest data loads in a decade.
Security operations, vulnerability management and authorization upkeep competed for the same people, and every emergency directive landed on top of the assessment calendar.
Technology at work
Continuous monitoring, patch management and configuration enforcement run alongside assessment work, with logs consolidated into a SIEM to meet OMB M-21-31.
Human role
The security team keeps system security plans and POA&M items current as the work happens, and the authorizing official decides.
Result
Evidence stays current between assessments, and three emergency directives were absorbed without moving the calendar.
Every program, staffed with people and AI teammates.
This is how a program is actually staffed. People and teammates in one org chart, not a capability list with technology bolted to the end of it.
People Empower AI staff working alongside agency staffAI teammate With its autonomy level: L1 means it produces output for a person, L5 means it can start work on its own. See the five levels ↓
We believe agencies will soon have as many AI teammates as people.
Then the question isn’t what AI can do, but who manages it. Every teammate gets an identity, defined permissions and a named supervisor before it touches the mission. That model holds at eight teammates or eight hundred thousand.
Moving AI into production.
Jennifer Sample, Ph.D., Chief Technology Officer · Accelerating AI-enabled services at federal agencies 2:09
