Autonomous AI discovery built for government and public service.
Your systems already hold the case records, application data, service-delivery logs, and constituent contact history. DecisionBox runs discovery against all of it overnight — on your own infrastructure — and ships a ranked list of findings by the time you walk into the monthly service delivery review.
Built for CIOs, Heads of Digital Services, and Directors of Operations at federal agencies, state and local governments, public hospitals, and quasi-governmental bodies.
Your service-delivery dashboard shows the median. Not the long tail nobody can clear.
DecisionBox runs them while you sleep. An autonomous AI agent picks what to investigate, runs the analysis, and re-checks every number against your data from a second angle before it ships. By morning you have a ranked list of findings — each with the affected case category, ZIP code, or service window — and a numbered recommendation.
How the agent picks what to investigateWhat stays hidden when the questions don't get asked.
Three patterns every agency leader recognizes on sight. The discovery loop runs against all of them in a single pass — and surfaces where your numbers diverge from the curve.
Three findings the agent surfaced on a state agency's data.
One overnight run. The agent picked the analysis areas, ran them against the platform's data, re-checked every number, and ranked what to fix. Three shapes of finding — one cliff in a single case category, one pattern across application and approval data, one anomaly the department aggregate was hiding.
Point it at your service-delivery data. See what the discovery loop ranks before your next service review.
Same data, five sets of questions.
One discovery run produces findings ranked for the team that owns the metric. Each panel is what the agent investigates in your data — questions it picks on its own.
The case category silently dragging your average.
Your backlog dashboard shows median wait time. The agent reads wait time category-by-category against case type, routing path, and clerk action — and ranks where the long tail is concentrating. Pair with Why a metric moved.
- Which case categories carry the steepest long-tail wait time
- Where routing logic sends specialty cases to generalist queues
- Which form types accumulate the highest stalled-application share
- Where missing reference data is silently blocking case progression
- Which intake channels show consistent re-work loops
Three properties your service delivery review can defend.
DecisionBox is built so the findings on the service-review call are not the kind that fall apart under a follow-up question — or an oversight committee.
Sees what nobody had time to look for.
The agent picks the questions. It reads every case category against every closure rate, every ZIP code against every enrollment curve, every service window against the historical baseline. The investigations nobody had time to formally request happen anyway — overnight, ranked by morning.
Every number you'd defend in the service review.
An independent re-check runs every number from a second angle. If the agent can't reproduce its own claim, the finding is dropped or adjusted with the corrected number, visibly. Every step of the agent's reasoning stays attached to the finding for review.
Your constituent data never leaves your environment.
Run DecisionBox on your own infrastructure or as a managed service — either way, case records, applicant information, document uploads, and constituent contact history stay on your own infrastructure. Open source under audit, so your security team can verify exactly what the platform touches and what it does not.