The Air Force’s DASH 1 (Decision Advantage Sprint for Human-Machine Teaming) experiment reinforced something Rackner has been building toward for a while: decision advantage isn’t about collecting more data. It’s about turning messy operational inputs into actionable options, faster while keeping humans firmly in control.
DASH 1 focused on proving that AI-enabled decision support can help battle managers move from situational awareness to actionability. The value isn’t “automation for automation’s sake.” The value is generating recommendations that are time-bound, constrained, and usable under pressure, the kind of outputs operators can actually act on in real-time battle management.
Rackner’s work aligns with the exact gap DASH 1 is targeting: the cognitive bottleneck between seeing what’s happening and determining what needs to happen next.
That’s why Rackner builds modular decision services instead of a single monolithic C2 platform. When decision functions are composable, they can be:
tested quickly in operationally relevant environments
improved iteratively as requirements evolve
integrated into existing workflows without forcing teams to rebuild everything at once
This approach supports real-world human-machine teaming, where AI accelerates decision-making, but operators remain accountable for the outcome.
At DASH 1, Rackner delivered a key building block in that flow through REAPER’s Perceive Actionable Effects (PAE) microservice.
The intent was clear: translate operational inputs into structured, executable outputs. Specifically, PAE was designed to produce BattleEffects, recommendations that map an effect onto an entity, bounded by constraints like rules of engagement (ROE) and tied to an explicit time window.
This is what makes perception operationally useful. Instead of simply showing data, the system helps answer:
What matters right now?
What action is viable before the window closes?
That shift, from inputs to actionable effects, is the foundation of decision advantage.
DASH 1 validated a direction that Rackner is already built for: fast, modular, explainable AI decision support that can operate in real environments. REAPER’s PAE capability proved that machine-speed understanding can be translated into structured effects that battle managers can trust, validate, and act on.
Because the next wave of capability won’t be judged by whether AI can produce an answer. It will be judged by whether it can produce actionable effects, fast enough to matter, and clear enough for operators to trust.