Multi-source mission picture
FoundationUnifies approved training, planning, maintenance, and calendar data into a shared operating view without replacing the source systems.
Nightly sync, scenario planner, and squadron calendar in operation.Federal AI/Optimize · Mission intelligence
Ask what will break before the schedule does.
Training platforms manage learning. Optimize manages the mission outcome.
Optimize connects fragmented training and readiness data, identifies the binding constraint, forecasts objective risk, compares feasible courses of action, and keeps the human decision-maker in command. Authoritative systems retain the record, Optimize adds the missing decision layer.
Delivered by Cleared FDEs
Read-only by default
Source + confidence shown
Human approval required
Current U.S. Air Force delivery
01Sense
Preflight and reconcile: schema, freshness, lineage, duplicates, and definition drift.
02Understand
Build the mission graph across objectives, students, gates, sorties, sims, IPs, aircraft, and weather.
03Forecast
Estimate objective risk: attainment confidence, uncertainty, slip risk, and emerging bottlenecks.
04Simulate
Compare feasible COAs: tradeoffs, schedule churn, protected events, and downstream effects.
05Decide + learn
Require human approval, record the decision, and compare forecast with the actual outcome.
DeploymentCustomer-authorized GovCloud, local, clustered, or air-gapped
Data postureRead-only by default · source, freshness, and lineage on every answer
SecurityNIST SP 800-171-aligned posture · SBOMs · signed releases · CUI-aware
DeliveryIn operation, current U.S. Air Force delivery · same-day hot-fix posture
Optimize mission copilot · synthetic
AI that can explain the constraint, not just describe the dashboard.
Ask a mission question and get an answer with sources, freshness, confidence, assumptions, and unresolved conflicts, then compare courses of action and stage one for human review.
Optimize mission copilot · read only
UNCLASSIFIED · SYNTHETIC · 06:42Z
UNCLASSIFIED // SYNTHETIC DEMONSTRATION // NO USAF ENDORSEMENT IMPLIED. Demonstration names, values, forecasts, and courses of action are synthetic. The demo performs no network calls and writes no data.
The strategic wedge
A system of intelligence above the systems of record.
Traditional training suites are built around courseware, simulation, student records, scheduling, and dashboards. Optimize preserves those investments and adds the missing decision layer, without another rip-and-replace platform.
SourcesAuthoritative feeds
SEATS, GTIMS training records, FHP/RAP objectives, maintenance, calendars, weather, and airspace remain the systems of record.
TrustData preflight
Source freshness, schema drift, duplicates, precedence rules, and conflicting totals are checked before they become bad readiness decisions.
MeaningMission ontology
One governed graph connects students, gates, sorties, sims, IPs, aircraft, maintenance, weather, grade sheets, and FHP.
ForesightForecast + COA
Objective attainment confidence, slip-risk drivers, and compared courses of action, with uncertainty exposed, not hidden.
CommandHuman decision
Feasible interventions are staged for approval. The decision ledger records forecast versus actual so the organization learns.
Current Optimize foundation
Documented engagement outcomes.
From the current operating engagement. External performance claims remain subject to customer validation and release authority.
12–15
Analyst hours per week displaced by the current operating workflow.
1
Shared source of truth across participating squadrons and mission stakeholders.
Same day
Demonstrated hot-fix cadence for mission-driven software changes.
$0
Per-seat licensing overhead in the documented pricing posture.
Capabilities · claim-disciplined
Foundation first. Modules when funded. Research labeled as research.
The capability map separates what operates today from configurable next-generation modules and research options, so ambition never reads as an unfielded promise.
Data preflight + reconciliation
FoundationFreshness, schema drift, duplicates, precedence rules, file-family discovery, and cross-out detection with bounded replay.
Exception trace shows why totals disagree before anyone briefs them.Governed training ontology
Configurable moduleShared definitions and a dependency graph across the full pilot-production mission, with permission-aware semantics.
Objective → constraint → intervention → outcome.Mission query + forecasts
Configurable moduleNatural-language questions answered with sources and confidence; FHP delivery confidence and class slip-risk forecasting with drivers.
Uncertainty and unresolved conflicts are part of the answer.COA simulation + briefing engine
Configurable moduleCompares feasible interventions, protected events, churn, tradeoffs, and turns mission state into commander decision cards and briefs.
Human review before any schedule change; decision ledger records the outcome.Training observability
Research optionOptional low-burden simulator participation and repetition-consistency signals using approved wearables and removable sensor pucks.
Privacy and opt-out guardrails; positioned as research, not a fielded default.
Bounded 90-day proof
One objective. One owner. Evidence that can kill the pilot.
Optimize is easiest to buy and trust as a read-only, bounded pilot tied to a real mission objective, measured on data trust, forecast calibration, decision time, workload, and adoption before scale.
00–15 daysName the mission objective
Select one threatened outcome, assign the sponsor and metric owners, agree the baseline, and define explicit stop or scale criteria.
16–35 daysConnect approved read-only data
Map source ownership, reconcile definitions, expose freshness and lineage, and establish the trusted mission ontology.
36–65 daysReplay and run live forecasts
Generate bottleneck traces, objective forecasts, counterfactuals, and COA comparisons, all actions human-approved.
66–90 daysMeasure and decide
Compare prediction with actuals, measure burden and decision speed, then scale, revise, or stop based on evidence.
SecurityBounded, inspectable AI
Approvals, audit logs, agent scorecards, signed releases, SBOMs, CUI-aware engineering, and a NIST SP 800-171-aligned posture.
Claims boundary
Optimize is positioned as a mission intelligence layer for pilot-training sustainment. Authoritative systems remain authoritative. Demonstration names, values, forecasts, and courses of action on this page are synthetic. Engagement outcomes describe the current documented engagement and imply no service-wide endorsement. Deployment patterns, GovCloud, local, clustered, or air-gapped, are used only where customer-authorized and scoped.
Cleared forward-deployed engineering
Bring us one readiness problem that should be visible earlier.
We will map the objective, authoritative sources, binding constraints, decision owner, and the 90-day proof. The first deliverable is not another dashboard, it is a decision brief and a working, bounded mission workflow.