iFoundry

Put AI into production where it solves a real problem.

We build model-backed workflows with evaluation, observability, human review, and release discipline from day one.

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Workflow diagram for evaluating and releasing AI features
If AI is going live, we want the evaluation and release steps on paper early.

Model capability is only useful when real teams can trust it.

  1. AI-assisted workflows

    Design AI-assisted flows around the decision, the data, the fallback, and the person who owns the outcome.

    Output: workflow slice with review states.

  2. Evaluation harnesses

    Build test sets, scoring runs, and release gates so teams know when a model is getting better or drifting.

    Output: repeatable evaluation dashboard.

  3. Production controls

    Add logging, traceability, version control, and escalation routes before the workflow reaches your customers or your team.

    Output: observable production path.

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Tell us where the AI workflow needs to hold up in production.

Tell us what exists today and where it is stuck. We will say what we'd tackle first in AI and ML, who should be in the room, and whether we're the right team for it.