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    Forward capability / Model generation

    Arc-Forge

    Turn reviewed field evidence into an evaluated, target-aware edge model near the point of need.

    Trace the release chain View deployment architecture
    The operational gap

    The threat changes.
    The model must move.

    Arc-Forge brings a controlled model-development loop near the point of need so teams can turn reviewed evidence into an evaluated, target-aware candidate.

    It combines field-deployable compute, local evidence storage, protected power, and program-selected networking with a repeatable train, evaluate, diagnose, improve, promote, and export workflow. Disconnected operation depends on the selected models, local dependencies, storage, and target toolchain.

    Model operations / controlled adaptation

    Mission evidence in.
    Approved capability out.

    Arc-Forge closes the model-improvement loop near the point of need. Teams curate reviewed evidence, build a target-aware candidate, test it against mission thresholds, and export only an identified, approved artifact with validation evidence and a rollback record.

    Arc-Forge / operational flowOperational flow model
    1. 01

      Observe

      Capture mission-relevant edge cases

    2. 02

      Curate

      Review, label, and version evidence

    3. 03

      Train

      Adapt and optimize for the target

    4. 04

      Evaluate

      Compare against thresholds and baseline

    5. 05

      Promote

      Bind identity, evidence, and approval

    6. 06

      Export

      Package with a known-good rollback path

    Interfaces / modes
    Target-aware buildsEvaluation gateIdentified artifactKnown-good rollback
    Operational result
    An inspectable release / not an untracked weight file

    Configuration note / Training time, model size, evaluation coverage, signing policy, and delivery depend on the program, hardware, dataset, power, and network.

    Model lifecycle / Evidence chain

    Six stages.
    One accountable artifact.

    Each handoff creates an inspectable output. The result is more than a weight file: it carries an identified model, dataset lineage, evaluation evidence, target profile, validation manifest, and known-good rollback reference.

    1. 01 / 06

      Observe

      Capture mission-relevant examples and edge-case evidence from deployed systems.

      Controlled output
      Field evidence
    2. 02 / 06

      Curate

      Review, label, version, and isolate the data that belongs in the next build.

      Controlled output
      Versioned dataset
    3. 03 / 06

      Train

      Retrain or adapt the model locally, then quantize it for the target edge hardware.

      Controlled output
      Candidate model
    4. 04 / 06

      Evaluate

      Run program-defined tests against held-out data, mission thresholds, and resource limits.

      Controlled output
      Evidence package
    5. 05 / 06

      Promote

      Promote a candidate only when it clears the configured threshold; otherwise keep the known-good champion.

      Controlled output
      Promoted artifact
    6. 06 / 06

      Export

      Package the promoted model for a supported target with its manifest, validation results, and rollback reference.

      Controlled output
      Edge package

    Model identity

    A versioned identity ties each artifact to its dataset, target hardware, and configuration.

    Release evidence

    Evaluation results travel with the candidate so approval is based on visible mission criteria.

    Rollback path

    Known-good releases remain addressable when a fielded build needs to be withdrawn.

    Deployable reference system

    A complete forge.
    Forward.

    A representative Arc-Forge configuration packages compute, evidence storage, power protection, and a program gateway for a controlled model pipeline. Installed capacity, toolchains, connectivity, and target support vary by configuration.

    Compute

    Dual 128 GB-class

    Two unified-memory AI systems support local adaptation, evaluation, and quantization workloads.

    Evidence store

    Mission NAS

    Network-attached storage keeps datasets, checkpoints, evaluations, and releases together.

    Resilience

    Conditioned power

    UPS and power conditioning protect the active workflow through unstable field power.

    Integration

    Program gateway

    Validated artifacts move through the program-selected transfer path, including disconnected workflows when configured.

    Deployment architecture

    Evidence in.
    Capability out.

    Forward model operations / Logical view
    Approved artifacts only cross release gate
    Input / Mission systems

    Field evidence

    • Mission-relevant examples
    • Operator-reviewed labels
    • Platform and sensor context
    Arc-Forge

    Local build + release cell

    Curate + version
    Train + optimize
    Evaluate + compare
    Promote + export
    Output / Program delivery path

    Validated model package

    • Target-specific artifact
    • Manifest + validation results
    • Known-good rollback reference
    Shorter adaptation loop

    Respond near the point of need

    Move from reviewed field evidence to an edge-ready candidate without waiting on continuous reachback.

    Hardware-aware output

    Build for the deployed target

    Quantize and evaluate candidates against the resource profile and mission criteria that matter in the field.

    Controlled promotion

    Advance with a recovery path

    Promote identified artifacts with lineage and evaluation evidence while keeping a known-good model available for rollback.

    From mission evidence to a validated edge artifact

    Build locally. Promote deliberately.

    Program office / contact

    Put Arc
    on the mission.

    Defense inquiries

    [email protected]

    Partnerships & commercial

    [email protected]

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