Turn plant data into defect detection and compliance guardrails
Atomic Build embeds a forward-deployed product and engineering team inside your plant to build AI systems that catch quality issues before they scale and keep compliance documentation current without manual overhead.
Quality and compliance are chokepoints eating margin and risk.
Defects slip through because visual inspection and manual sampling can't catch everything. Compliance documentation lags operations by weeks. Supply chain traceability requires spreadsheets and tribal knowledge. Your ERP and MES sit on the same network but don't talk to each other.
- Defects escape the line
- Visual inspection catches maybe 40–60% of defects. The rest surface in the field, triggering recalls, warranty claims, and reputation damage.
- Compliance is a paperwork chase
- Quality documents, batch records, supplier certifications and test results live scattered across systems and email. Audits require weeks of manual aggregation.
- Traceability is a reconstruction project
- When a part fails, you can't instantly map raw material → supplier → batch → assembly → shipping without manual detective work across multiple systems.
- Quality data isn't actionable
- You collect SPC data, defect counts, and downtime logs but they don't feed into decisioning. Root cause analysis happens in meetings, not in real time.
- Manual data entry compounds errors
- Operators hand-transcribe test results into batch records. Inspectors key defects into separate systems. Every handoff is a point of failure and audit liability.
- No early warning system
- You react to defects. There's no AI watching the data stream to flag drift, correlation or emerging patterns before they become a batch reject.
Quality and compliance automation isn't about replacing inspectors. It's about giving operators and quality engineers real-time visibility and guardrails so issues get caught and documented the moment they matter.
We don't build 'AI for quality control' as an isolated module. We embed a product and engineering team inside your plant, integrate your operational data feeds (sensors, MES, lab systems, ERP), and ship small AI services that run continuously on real production data. Every system we build has humans in the loop by design—escalation paths, review workflows, and audit trails that make your team more effective, not less visible.
- Score the opportunities that move compliance and margin
- We map your quality and compliance workflows, quantify the cost of escapes and manual overhead, and only build against the workflows where AI shifts your defect rate or audit burden.
- Production data, production decisions
- Every AI system we ship runs on your actual production data streams—sensors, MES logs, lab results, batch records—not curated training sets. The system learns your baselines and flags anomalies in real time.
- Integration first, not retrofit
- We pull data from your existing MES, ERP, LIMS, and sensor networks. We don't replace them. AI sits on top, orchestrating decisions and documentation across them.
- Traceability built in, not bolted on
- Every decision an AI system makes (defect classification, compliance flag, part hold) is logged with reasoning and data provenance. Your auditors see exactly how and why the system decided what it did.
- Escalate intelligently, not reflexively
- AI doesn't approve or reject. It classifies, scores confidence, flags edge cases, and routes to the right human with context. Your quality engineers make faster decisions because the AI did the legwork.
- Ship in weeks, tighten forever
- First system live within 6 weeks. Every new system builds on the data infrastructure and workflows already in place, so velocity and accuracy compound.
From plant data to production AI in six weeks
We start with an Opportunity Sprint to surface the highest-leverage quality or compliance workflow, then build a production system integrated into your MES and data infrastructure.
- Opportunity Sprint · Week 1
- We embed with your quality, operations, and IT teams to map workflows, audit your data feeds (MES, LIMS, sensors, ERP), and score the AI opportunities with the highest defect or compliance impact.
- Data integration and system design · Week 2
- We document data pipelines from your MES, sensors, and lab systems. Design the AI system (training data, inference loop, escalation paths, audit logging) and the human-in-the-loop workflow.
- Forward-deployed build · Weeks 3–6
- Atomic Build engineers ship the system directly into your plant environment, training on your historical data, integrating with your MES or quality system, and testing against live production conditions.
- Monitor, measure, and scale · Week 7+
- System runs continuously against production data. We track defect detection rate, false positive rate, and compliance document coverage against the metrics agreed in week 1. Queue the next workflow.
Relevant services
Most engagements combine three or four of these. Start with what hurts most.
Internal AI systems we've built for manufacturing quality and compliance
Each system integrates your operational data feeds and runs continuously on production. These are the patterns that move defect detection and compliance margin the most.
- Real-time defect detection from sensor data
AI watches machine sensor streams (temperature, vibration, pressure, geometry) for patterns that precede defects. Flags anomalies before parts finish or ship.
−73% field defects
- Visual inspection copilot
AI co-reviews inspection images with human inspectors, flags borderline parts, and suggests rework vs. scrap. Catches what gets missed in rapid-fire visual triage.
−58% escaped defects, +12% consistency
- Continuous batch documentation
AI pulls test results, equipment logs, material certs, and operator actions directly into batch records in real time. No manual transcription. Every batch audit-ready the moment it closes.
−86% audit findings, 100% traceability
- Supply chain traceability engine
Maps raw material → supplier → batch → assembly automatically by cross-referencing ERP and MES records. On defect, instant visibility back to source and forward to every customer shipment.
Recall scope −71%, RCA time −5 days
When to talk to us
Some patterns we hear on the first call. If two or more of these are true, the conversation is worth having.
- Visual inspection catches maybe 40–60% of defects. The rest surface in the field, triggering recalls, warranty claims, and reputation damage.
- Quality documents, batch records, supplier certifications and test results live scattered across systems and email. Audits require weeks of manual aggregation.
- When a part fails, you can't instantly map raw material → supplier → batch → assembly → shipping without manual detective work across multiple systems.
- You collect SPC data, defect counts, and downtime logs but they don't feed into decisioning. Root cause analysis happens in meetings, not in real time.
- Operators hand-transcribe test results into batch records. Inspectors key defects into separate systems. Every handoff is a point of failure and audit liability.
- You react to defects. There's no AI watching the data stream to flag drift, correlation or emerging patterns before they become a batch reject.
Let's Connect
Decide what is worth building first.
We start with an Opportunity Sprint to surface the highest-leverage quality or compliance workflow, then build a production system integrated into your MES and data infrastructure.