T7 Solution ships AI on top of IoT platforms as production modules — predictive maintenance, anomaly detection, vision-based quality inspection, edge AI and LLM copilots for ops.
IoT captures the data; AI turns it into decisions. But most AI-in-IoT projects stop at a Jupyter notebook that never reaches the plant floor.
We ship AI as production modules — predictive maintenance with cited failure signals, statistical + ML anomaly detection, vision QC on the line, and edge inference where cloud round-trips are too slow.
An LLM ops copilot lets supervisors ask 'why did Line 3 stop at 14:20?' and get a cited answer from machine data, alerts and shift notes.
Remaining-useful-life models per asset class with cited failure signals and confidence intervals.
Statistical baselines + ML models for silent-failure and drift detection across tags and assets.
Line-side computer vision for defect detection, counting and OCR — with retraining loops.
On-device inference on NVIDIA Jetson, Coral or industrial edge PCs — cloud round-trips avoided.
LLM-based supervisor assistant answering 'why', 'when' and 'what next' from telemetry and logs.
Model registry, drift monitoring, retraining pipelines and A/B evaluation baked in.
Every RUL and anomaly comes with signals — not a black-box score ops has to guess about.
We choose edge vs cloud per model based on latency, cost and connectivity — not dogma.
Notebooks become production services with monitoring, retraining and rollback — not slide decks.
Plugs into existing SCADA/MES or into the IoT platforms we build — no rip-and-replace.
Production AI modules we drop into your ai in iot build.
Production ML for forecasting, churn, risk and pricing — trained on your data.
Custom vision models for quality control, retail analytics, medical imaging and more.
Multi-agent architectures that plan, use tools and complete complex tasks.
Production-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
Meaningful models usually need 6–12 months of tagged failure data. Where that's missing, we start with anomaly detection and physics-based baselines.
Both — heavy retraining in cloud, low-latency inference at the edge where connectivity or latency demands it.
We deploy on industrial cameras (Basler, IDS) with GPU or Jetson edge boxes, with retraining loops for drift.
Yes — it answers cited questions from telemetry, alerts and shift notes. It won't hallucinate about production numbers.
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Compare the other IoT modules or explore where IoT meets AI.
High-volume ingestion, historian, downsampling and analytics for sensor telemetry
PLC/SCADA integration, MES modules, machine data and control workflows
Real-time device, fleet and site dashboards with drill-down and alerts
Sub-second defect detection on the line — with a governance trail.
SKU-level forecasts that release working capital, not just improve MAPE.
S&OP-grade forecasting, replenishment and scenario planning.