Industry

AI that earns its place on the shop floor.

Manufacturing punishes fragile software: dust, latency, legacy PLCs, and operators with no patience for false alarms. We build vision systems, predictive models, and document pipelines that hold up where uptime is the religion.

Discuss your use case →What we build

quantpi · industry/telemetry

$ industry.constraints()
✓ deployment: edge + plant network · false-alarm budget: explicit
✓ deployment: cloud · hybrid · air-gapped
✓ proof standard: measured on your data
# domain constraints are design inputs, not blockers

IND/01What's at stake

A model with 5% false alarms gets unplugged by Friday.

On the floor, trust is the deployment criterion. An inspection system that flags good parts, or a maintenance model that cries wolf, gets bypassed within a week — and the second attempt starts from negative credibility. We engineer for operator trust: explicit false-alarm budgets, explainable flags, and edge deployment that doesn't blink when the network does.

IND/02What we build here

Use cases we ship

Visual quality inspection

Camera-based defect detection trained on your parts and lighting, deployed at the edge, tuned to an agreed false-positive budget.

budget: agreed pre-deploy

Predictive maintenance

Failure prediction from sensor and historian data with lead times maintenance can act on — and precision that protects credibility.

metric: actionable lead time

Process parameter optimization

Yield and energy optimization from historian data — recommendations operators can interrogate, trialed within control limits.

trials: within control limits

Shop-floor document intelligence

Work instructions, drawings, maintenance manuals — searchable and answerable at the line, with citations to the controlled revision.

grounding: controlled docs

Production scheduling support

Constraint-aware schedule optimization that respects the realities planners know and ERPs don't encode.

constraints: planner-validated

Supplier quality analytics

Incoming-quality patterns across suppliers, lots, and seasons — caught at goods-in instead of at assembly.

signal: lot-level

IND/03Domain constraints we design for

Built for your constraints

Edge deployment for line-speed latency and network independence
OT/IT segmentation respected — we work with your network model
Integration with PLCs, SCADA, historians (OPC UA, MQTT)
Explainable flags operators can act on
Graceful degradation: the line runs even when AI doesn't
Change management with operations, not around them

IND/04Questions, answered straight

FAQ

Our data is messy historian data. Is it usable?

Almost always — messiness is the normal starting condition. The audit phase quantifies what your historians can actually support; sensor gaps and labeling debt get a remediation plan with costs before any model commitments. We'd rather tell you ‘instrument first’ than ship a model on sand.

How do you deploy on a network that can't reach the cloud?

Edge-first: models run on industrial PCs or edge GPUs inside the plant network, with store-and-forward telemetry when a connection exists. Training happens off-line; inference never depends on the WAN.

What accuracy do vision systems achieve?

On well-lit, fixtured inspection tasks: 99%+ detection with sub-1% false positives is routinely achievable after tuning on your parts. Unfixtured or high-variance tasks run lower — we measure on your line during the proof phase and commit to numbers only after that.

Will this work with our legacy MES/ERP?

Yes — we integrate through what exists: OPC UA, file drops, database views, or vendor APIs where available. Replacing your MES is not a prerequisite, and we won't pretend it is.