Supervisor reviewing multiple stations on a dashboard

Built for the real world, not just software

Industrial operations are physical, dynamic, and full of context. We turn what your best people do into a private training set and measurable guidance for everyone else.

Strategy → Pilot → Scale

Align use‑cases with outcomes, then prove value in a tightly scoped 2–4 week pilot.

Feedback‑Driven Training

Operators/supervisors add quick notes at moments that matter; we pair it with the exact clip + transcript.

Your Models, Your IP

Data stays private. Trained models are your intellectual property—no cross‑customer training.

Our process

A pragmatic path from today’s workflows to AI‑assisted operations.

1) Capture

Record key moments on the line (video + audio) and ingest SOPs, checklists, and prior incident logs.

2) Annotate

Supervisors add feedback live via dashboard—or we run passive capture and label after‑the‑fact against your policies.

3) Train

Optimize objective functions around what “good” looks like: fewer interventions, higher first‑pass yield, safer ops.

4) Deploy

Surface guidance, checks, and training snippets right where work happens; close the loop with continued feedback.

Illustration of capture → annotate → train → deploy
Supervisor dashboard and passive review timeline

Engagement model

Live Supervision

  • Dashboard (iPad/desktop) to watch multiple stations
  • One‑click comments generate labeled moments
  • Great for complex tasks and onboarding

Passive Review

  • Continuous recording; our team labels post‑shift
  • Follows your training policies and SOPs
  • Low lift for teams with limited bandwidth

Pilot timeline (2–4 weeks)

  • Week 0: Free consult, define workflows & success metrics
  • Week 1: Capture setup, data intake (SOPs, logs), kickoff
  • Week 2: Feedback/labeling and first model iteration
  • Week 3–4: Deploy guidance, measure gains, next‑step plan

What you get

Use‑case brief

Documented problem framing, objective functions, data sources, and constraints.

Instrumentation plan

Capture setup, privacy zones, retention policy, and consent workflow.

Labeled dataset slice

Moments tied to clips + transcripts + feedback; exportable to your storage.

Pilot model

Task‑specific model artifact and runtime instructions (on‑prem/VPC ready).

Deployment blueprint

How to roll out to adjacent lines/sites; roles, SOP updates, training plan.

ROI snapshot

Baseline vs. pilot metrics with recommendations for scale.

Cards showing pilot outputs and reports
Sample metrics: first‑pass yield, intervention rate, MTTR

Outcomes & metrics

Onboarding speed

Time‑to‑proficiency for new hires.

Quality

First‑pass yield and defect escape rate.

Stability

Intervention rate per shift, unplanned stops.

Efficiency

Cycle time adherence and rework.

Safety

Near‑miss capture and corrective action closure.

Knowledge capture

Reusable clips tied to SOPs for training.

Security & IP

  • Data tenancy options: on‑prem or private VPC.
  • Granular access controls and audit trail.
  • Configurable retention windows and redaction zones.
  • No cross‑customer training—your models are your IP.

We align with your policies and industry standards; we’ll map controls during the consult.

Lock icon over a factory schematic
Operator asking a supervisor a question with a headset

FAQ

How is this different from generic AI platforms?

We train task‑specific models on your operations. Feedback is tied to exact clips and SOPs. Data and trained models remain private to you.

Do we need special hardware?

Usually not—commodity cameras and tablets are fine for pilots. We’ll document requirements during the consult.

Where does the data live?

On‑prem or in your private VPC. We respect your retention policies and can set up redaction for privacy zones.

What’s the fastest path to value?

Pick one high‑leverage workflow, run a 2–4 week pilot, ship a small model, and measure 2–3 metrics that matter.

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