SOLUTION · MANUFACTURING
AI Solutions for Manufacturing
Catch defects before they leave the line. Visual inspection deployed in weeks, edge inference that runs offline, and a process you can reproduce across every line—delivered by engineers embedded on your factory floor.
Push quality inspection from the limits of the human eye to line speed
Most quality problems on a factory floor aren't caused by 'no one checking'—they're caused by the fact that human inspection simply can't keep up at production speed. Standards drift between shifts, fatigue lets defects slip through, and customer returns trace back to a root cause no one can pinpoint. Adding more inspectors doesn't fix any of that. Meanwhile, off-the-shelf AI vision tools tend to assume you have reliable network, clean labeled data, and a line you can freely stop to tune—almost none of which is true on a real production floor.
Tenten AI's approach is to put engineers on-site. We capture images and train models on your actual lighting, your actual fixtures, and your actual reject samples, then run inference on an edge device right next to the line—no external connectivity, no images leaving the building. Even when the plant loses its network, inspection keeps running. Results drive your existing PLC I/O and MES integration to trigger rejects or log records, without touching your line control logic.
What we deliver isn't a demo rig—it's a reproducible process. Once the first line is validated, the model, labeling spec, and deployment scripts move to the second line and the third, so your quality lead walks into an audit or a customer complaint with judgments that are consistent, traceable, and explainable.
Capabilities
01
Line-speed visual defect inspection
Models trained on your real line lighting and takt time make real-time calls on scratches, burrs, wrong parts, missing parts, solder joints, and surface flaws—with adjustable thresholds so quality can tighten or loosen by customer spec.
02
Edge deployment that runs offline
Inference runs on an edge device next to the line (including NVIDIA Jetson-class hardware) with no dependence on external network. Inspection keeps working when the plant drops connectivity, and images and results can be kept entirely on-premise to meet security and trade-secret requirements.
03
Native MES / PLC integration
We trigger reject signals and write inspection records through PLC I/O, OPC UA, or your existing MES API—without changing your line control logic—so AI judgments slot seamlessly into your current process flow.
04
Controlled false-reject and escape rates
We tune against the escape rate you're willing to accept as a hard target, and hand you confusion matrices and threshold curves so you can make a defensible tradeoff between false rejects and escapes by product and customer requirement.
05
Equipment maintenance knowledge base
We turn repair knowledge scattered across SOPs, machine manuals, maintenance logs, and senior technicians' heads into a searchable knowledge base, so floor staff can ask 'how do I clear this alarm code' in plain language and get an answer with cited sources.
06
Scheduling and dispatch assistance
On top of your existing ERP/MES data, we help model and recommend options for changeovers, rush-order insertions, and labor allocation—turning decisions that used to rely on a senior scheduler's gut into reproducible, reviewable plans.
Use cases
Surface-defect inspection on metal stampings
Industrial cameras at the press outlet make real-time calls on scratches, dents, and burrs; NG parts are kicked out by a PLC-triggered air ejector, replacing sample-check escapes and downstream complaints.
PCB / SMT solder-joint and wrong-part inspection
Visual inspection added after placement and reflow catches open joints, wrong parts, reversed polarity, and missing components—covering the cosmetic defects rule-based AOI struggles with and cutting board-level rework.
Label and seal inspection on packaging lines
Detect crooked labels, blurred printing, wrong lot codes, and bad seals, catching them before shipment to avoid full-batch returns and downstream channel penalties.
Rapid troubleshooting from machine alarms
Floor technicians ask the maintenance knowledge base about an alarm code or fault symptom and get step-by-step remediation with citations to the manual and past work orders, cutting downtime.
Changeover scheduling for rush orders
When sales drops in a rush order, the scheduling assistant models several changeover options against current WIP, tooling, and labor, flags the delivery-date impact, and lets the scheduler decide fast.
Delivery cadence
WEEK 1
On-site survey and data capture
Engineers embed on your line, lock down lighting, fixtures, and camera mounting, collect real good and NG samples, and confirm MES/PLC integration points and the acceptable escape ceiling.
WEEK 2–3
Model training and single-line validation
We train and tune on real data, deploy to the edge device beside the line, wire up PLC/MES triggering and logging, and prove it out on one line with judgment standards aligned.
WEEK 4
Trial run and threshold calibration
We benchmark against human inspection on live output, calibrate false rejects and escapes, lock thresholds, and complete operating and maintenance handover docs for your quality team to run day-to-day.
WEEK 5+
Cross-line replication and scale-up
We copy the validated model, labeling spec, and deployment scripts to other lines and plants, expanding inspection coverage and standing up model retraining and monitoring.
Weeks
to first line live, not months
Offline
edge inference, runs without network
MES/PLC
native integration, no control-logic changes
FAQ
Does this still work when the network drops or the plant has no reliable connectivity?
Yes. Inference runs entirely on an edge device next to the line with no dependence on external connectivity, so inspection keeps working when the plant loses its network. Images and results can be configured to never leave the building—processed and stored locally only—to meet security and trade-secret requirements.
Will the false-reject rate be high enough to slow down the line?
We tune against the escape ceiling you're willing to accept as a hard target and calibrate thresholds on real production data to keep false rejects in an acceptable range. Thresholds are adjustable—quality can make a defensible tradeoff between false rejects and escapes per product and customer spec, rather than accepting a fixed black-box call.
How do you integrate with our existing MES and PLC?
We trigger reject signals and write inspection records through PLC I/O, OPC UA, or your existing MES API, without changing your line control logic. We confirm the integration points with your automation and IT teams on-site during the first week so AI judgments slot seamlessly into your current process.
Why weeks, when other deployments take months?
Because our engineers embed on-site, capturing data and training on your actual lighting, fixtures, and NG samples—no back-and-forth or remote guesswork. The first line is typically live for validation within weeks; the slower work of cross-line scale-up builds on a process that's already proven, so replication cost drops sharply.
What if we don't have a large set of labeled defect data?
Most factories start without clean labeled data, and that's normal. We help collect and label real NG samples on-site, use model strategies suited to limited data to get the first line working, and then keep improving accuracy as samples accumulate in production.
Can a validated solution be replicated to our other lines or plants?
Yes—that's the core of what we deliver. Once the first line is validated, the model, labeling spec, and deployment scripts are portable, so replicating to a second line, a third line, or another plant doesn't start from scratch each time. Judgment standards stay consistent, which also makes audits and complaint traceability far easier.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.