Industrial‑first training grounded in outcomes
We optimize models toward what matters: fewer interventions, higher first‑pass yield, safer operations, and faster onboarding.
Objective‑driven
Define measurable “good” via SOPs + signals (interventions, pass/fail, cycle time).
Expert feedback
Use supervisor comments and labeled moments to guide training (RLHF where useful).
Private by design
On‑prem/VPC options and models that remain your IP—no cross‑customer training.
Model types we train
Vision (images/video)
Defect detection, assembly verification, PPE/compliance checks, surface inspection.
Temporal / sequence
Step ordering, anomaly/event prediction, near‑miss windows.
Multimodal
Fuse video + audio + machine logs + transcripts for richer context.
Instruction‑tuned
SOP‑aware assistants for setup, troubleshooting, and training snippets.
Retrieval‑augmented
Connect models to SOPs, manuals, and prior incidents for grounded answers.
Anomaly detection
Unsupervised/semi‑supervised for rare defects and process drift.
Time‑series
Predictive maintenance and quality using sensor streams.
Edge‑optimized
Quantized/pruned models for low‑latency on cameras and IPCs.
Objectives & data sources
Objective functions
Train toward disengagements avoided, defects caught, cycle‑time adherence, and safe‑state compliance.
Ground truth
Use SOP‑aligned labels, gold sets, and pass/fail outcomes to anchor training.
Signals
Video frames, transcripts, sensor logs, operator comments, and machine states.
When uncertainty is high, we escalate to humans; those decisions become new training data.
Training workflow
1) Data curation
Balance classes and edge cases; dedupe, stratify, and augment responsibly.
2) Supervised fine‑tuning
Start from strong baselines; adapt to your tasks with your labeled data.
3) RLHF (where applicable)
Use expert preferences to refine outputs toward your operational goals.
4) Evaluation harness
Offline metrics (mAP/F1/AUROC), task checks, and error buckets.
5) Red‑team & safety
Probe failure modes; set thresholds, guardrails, and human‑in‑the‑loop triggers.
6) Packaging
Containerized runtimes, quantization/compilation for edge, and SDKs.
Evaluation & benchmarks
Offline metrics
Precision/recall, F1, mAP, AUROC; per‑class and per‑scenario breakdowns.
Task checks
Step‑order compliance, false‑stop cost, and near‑miss detection quality.
Online metrics
Intervention rate, first‑pass yield, cycle‑time adherence, MTTR.
We report baselines vs. pilot models and highlight trade‑offs so you can choose thresholds that fit risk and throughput.
Deployment & runtime
On‑prem / VPC
Air‑gapped or private cloud; models and data remain within your boundary.
Edge
Low‑latency inference on cameras/IPCs; quantization and compilation for target hardware.
APIs & SDK
gRPC/REST with client SDKs; batching and streaming for video.
We document latency budgets and throughput expectations for each runtime.
MLOps & monitoring
Versioning
Datasets, models, and configs are tracked with changelogs.
Drift & alerts
Detect distribution shifts; route uncertain cases to humans.
Continuous improvement
Close the loop with new labels; scheduled retraining windows.
What you get
Model artifacts
Weights, configs, and inference containers; edge builds when needed.
Evaluation report
Baselines vs. pilot; confusion matrices, error buckets, and thresholds.
Runtime & SDK
Docs and client libs for deployment; example pipelines.
Playbooks
SOP updates, escalation rules, and human‑in‑the‑loop guidance.
Rollout plan
Pilot → adjacent workflows → multi‑site scaling.
IP terms
Your data and trained models remain your IP.
Security & IP
- On‑prem or private VPC deployments with least‑privilege access.
- Configurable retention; privacy zones and redaction for video.
- No cross‑customer training; models remain your intellectual property.
We map controls to your policies and industry standards during onboarding.
FAQ
Which models do you start from?
We begin with strong open/commercial baselines appropriate for your task and constraints, then fine‑tune to your data and objectives.
Do we need GPUs on‑site?
Not for pilots. Training can run in a secure cloud/VPC; inference can run on edge devices or on‑prem servers.
How do you handle rare edge cases?
We upsample/target them during curation and route uncertain production cases to human review to create new labeled data.
What’s the fastest path to value?
Pick one workflow, ship an initial model in 2–4 weeks, and track 2–3 operational metrics (e.g., intervention rate, FPY).