Turn existing exercise data into evidence-backed findings for AAR and post-exercise review.
Post-mission. Read-only. Supports evaluator judgment and mission command.
In training, evaluation, and exercise environments, post-mission review often relies on manual reconstruction and subjective interpretation. Observer Controllers and evaluators may already have the data they need, but turning it into consistent, defensible AAR outputs still takes time.
Field IQ reduces that burden by turning existing exercise data into evidence-backed findings for post-exercise review.
Field IQ fits into the post-mission evaluation workflow, helping Observer Controllers and evaluators turn existing exercise data into evidence-backed findings for AAR and readiness review.
Field IQ structures movement, timing, dwell, and readiness-relevant indicators using deterministic, explainable logic.
Outputs are consistent across repeat runs, traceable to source data, and designed for defensible evaluation.
Field IQ turns existing exercise data into findings that Observer Controllers, instructors, and evaluators can actually use.
It surfaces movement patterns, timing variance, dwell, and plan-relative comparison when mission plan data is available.
Field IQ fits into existing dashboards, training tools, and evaluation workflows without forcing platform migration or workflow replacement.
Teams can adopt capability incrementally in customer-controlled environments.
Designed for teams responsible for post-mission evaluation, AAR, and readiness review in training, certification, experimentation, and test environments.
When mission plan or route geometry is available, Field IQ highlights deviations between planned and actual execution so evaluators can support AAR and post-exercise review with evidence-backed findings.
Surface pauses, dwell time, low-speed movement, and pacing changes in post-mission data so evaluators can review execution patterns without manual reconstruction.
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Field IQ produces structured, repeatable readiness-relevant indicators linked to supporting rationale so evaluators can use them in AAR and post-exercise review.
![[digital project]](https://cdn.prod.website-files.com/6930f7420e6b1845ceb5c417/6961bc716df0940706a2c8c3_image%206.png)
Detect noise, gaps, and sampling issues in source data so evaluators understand how much confidence to place in each run, reducing false certainty and audit risk.
When movement slows, pauses, or becomes inconsistent near a decision point, Field IQ surfaces the change for evaluator review using observable post-mission data.
These movement-pattern shifts are identified automatically so evaluators can see where execution changed, not just where the route moved.
When mission plan or route geometry is available, Field IQ highlights where actual movement diverged from the planned route and surfaces the change for evaluator review.
Evaluators can distinguish adaptive movement from route error using observable evidence rather than assumption.
Extended stopping is surfaced as a dwell event for evaluator review.
Field IQ identifies dwell points and shows how they affected pace, exposure, and mission flow using observable post-mission data.
Field IQ brings movement patterns, deviation events, dwell, and supporting rationale into a structured readiness review output.
Evaluators get a faster, more defensible view of performance across routes, scenarios, and exercise events without manual reconstruction.
Field IQ plugs directly into the systems you already run, without forcing platform migration or workflow disruption.
Connects directly to the systems teams already rely on, without forcing platform migration or workflow changes.
Evidence-backed findings and review outputs can be delivered into existing dashboards, mission tools, or evaluation workflows, where teams already conduct post-mission review.
Exposes a minimal, deterministic API that teams can validate quickly without standing up new infrastructure.
Review outputs can be routed into existing systems immediately, allowing teams to validate value before committing to deeper integration work.
Scales by adding capability, not complexity. Teams can expand from a narrow evaluation use case to broader post-mission review workflows without reconfiguration or retraining.
As adoption expands, the evaluation model remains consistent, preserving trust in outputs over time.
Existing exercise data becomes useful when it is turned into evidence-backed findings that evaluators can use in AAR and post-exercise review.
Field IQ surfaces post-mission findings quickly, without replacing evaluator judgment or existing workflows.
Whether you’re exploring pilot fit, workflow integration, or post-mission evaluation use cases, we can help determine where Field IQ fits and what a narrow first deployment could look like.
Conversations typically focus on evaluation workflows, AAR support, integration into existing systems, and pilot scope for training, test, or experimentation environments.
Secure transfer options are available on request.
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Clear answers on deployment, integration, and operational fit.