ipnops

Science

Methodology, cited end-to-end.

We run an explicit model bench. Every prediction the platform makes is traceable to the model, version, dataset, and confidence interval that produced it. Where research models exist, we use them. Where domain physics exists, we use that too. We do not train novel foundation models — we orchestrate the best of what the field has produced.

Model stack

Open foundations, cited and named.

We orchestrate the best open and research-grade models for industrial systems — never black boxes. Every prediction, every dispatch decision is traceable to the model, dataset, and confidence interval that produced it.

Read the methodology

TimesFM 2.5

Google Research

Equipment health + OEE forecasting

Vibration RMS, kurtosis, and crest-factor trend forecasting; OEE / yield / throughput projection at line and station granularity. Zero-shot on new asset classes.

Chronos

Amazon

Probabilistic ensemble partner

Quantile-aware forecasts for failure distributions; remaining-useful-life with calibrated tails.

Moirai

Salesforce

Mixed-frequency forecasting

Heterogeneous SCADA + MES + lab-data fusion at irregular sampling intervals.

Temporal Fusion Transformer

Open research

Interpretable multi-horizon forecasting

Per-feature attribution for production planners — helpful when an OEE drop has multiple plausible drivers.

OpenVLA

UC Berkeley

Vision-Language-Action for robotic manipulation

Open-weight (Apache 2.0) cross-embodiment robot policy trained on the Open X-Embodiment dataset (1M+ trajectories). Drives cobots, pick-and-place, assembly tasks, agnostic to robot vendor.

RT-2

Google DeepMind

Reference VLA (closed weights)

55B-parameter Google DeepMind policy. Strong generalisation; not publicly available, used as a benchmark target for OpenVLA fine-tunes.

Octo

UC Berkeley

Lightweight cross-embodiment policy

93M parameters, fast inference, runs on edge silicon for smaller cobots and AMR manipulators.

Qwen3-VL

Alibaba

Quality-inspection VLM

Defect detection on production-line imagery, OCR on serialised parts, dimension reads, packaging integrity. Adapts to new defect taxonomies via prompt rather than retraining.

Pixtral

Mistral

Edge VLM for in-line inspection

Fits a Hailo-10H / Jetson Orin Nano Super at the line edge — sub-second inspection at 1,200+ parts per minute throughput.

SAM 3.1

Meta AI

Spatial segmentation

Asset masks for cropped, model-ready imagery; product segmentation on conveyor; safety-zone monitoring around cobots.

PANNs

Open research

Acoustic-emission classification

Bearing wear, gearbox tooth fault, motor stator/rotor fault, fan unbalance — continuous, low-power, in-cabinet enrolment from a few minutes of audio per asset.

YAMNet

Google

Edge audio embeddings

Always-on monitoring at machine cabinets — emits embeddings on-device so raw audio stays inside the customer's perimeter.

ProcessSim ensemble

Ipnops · open foundations

Discrete-event + process simulator

Production-line balancing, predictive vs reactive maintenance scenario runs, AGV/AMR fleet routing, batch-recipe optimisation, autonomous-truck dispatch. OpenDESS / SimPy / Aspen-style foundations.

SAC / PPO controllers

Open research

Reinforcement-learning process control

Soft Actor-Critic and Proximal Policy Optimization for continuous-process loops (refinery catalytic reforming, blending, fermentation). Control-Informed RL hybrids preserve PID set-point tracking while gaining nonlinear modelling capacity.

Gemini 3.1 Flash Lite

Google DeepMind

Reasoning + tool orchestration

Conversational interface, tool-calling agent for forecast/simulate/dispatch, document QA over standards (ISA-95, IEC 62443, ISO 10218), shop-floor reports.

Gemini Embedding

Google DeepMind

Semantic search over operational text

SOPs, work instructions, MOC records, deviation reports, batch records. Matryoshka 1536-d for pgvector halfvec at the workspace tier.

Benchmarks

Tested against the published baselines.

OEE benchmark:Nakajima TPM’s world-class OEE benchmark is 85% (90% Availability × 95% Performance × 99% Quality). Real-world US plants typically run 55-60%; world-class discrete is 85%; world-class continuous-process is 90%+. The platform tracks every improvement against this canonical reference.

Inspection vision: Production-line inspection systems in 2026 (Cognex In-Sight 3800-class, VisionPro Deep Learning) operate at 1,200+ parts per minute with documented 99.2%+ defect detection accuracy on automotive body panels, semiconductor wafer lines, PCB assembly, and pharmaceutical tablets.

Battery cell inspection: 2026 gigafactory deployments inspect every cell in 0.3 seconds with 100% coverage and 97%+ first-pass yield, detecting defects down to 20 microns — well below human-hair scale.

VLA robotics:OpenVLA (UC Berkeley, Apache 2.0, trained on the Open X-Embodiment dataset of 1M+ trajectories) matches Google’s closed RT-2 performance on cross-embodiment manipulation. Octo (93M params) covers fast-inference edge embodiments.

Acoustic-emission monitoring: AE sensors detect rotating-machinery faults earlier than vibration-only sensors thanks to their higher frequency sensitivity. PANNs CNN14 pretrained on AudioSet provides strong zero-shot anomaly behaviour; per-asset linear classifiers fingerprint each transformer or gearbox in under 30 minutes of enrolment audio.

Standards

Native to the protocols of the floor.

Industrial systems run on a thicket of standards. A platform that doesn’t speak them natively is doomed to perpetual integration work. We speak them natively.

  • ISA-95 + Unified Namespace

    Enterprise-to-shop-floor data architecture

  • OPC UA + MQTT Sparkplug B

    Edge data movement; ISA-95 over MQTT

  • IEC 61131-3

    PLC programming languages (LD, FBD, ST)

  • IEC 62443

    Industrial automation cybersecurity (zero-trust IACS)

  • NIS2 (EU 2026)

    Mandatory cybersecurity for critical-infra entities

  • ISO 10218 + ISO 13849

    Robotic safety; speed-and-separation monitoring

  • MTConnect

    CNC + machining-center telemetry

  • ROS 2

    Robot Operating System for cobots, AMRs, manipulators

  • ISO 50001 / 50006

    Energy management (industrial)

  • ISO 55001

    Asset management (predictive maintenance grounding)

  • GMP + Pharma 4.0 (PAT)

    Pharmaceutical manufacturing compliance

  • FSMA 204

    Food traceability (24h record retrieval)

Selected references

Where the work comes from.

  • OpenVLA: An Open-Source Vision-Language-Action Model

    Kim et al. — UC Berkeley, 2025 (arXiv:2406.09246)

  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    Brohan et al. — Google DeepMind, 2023

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Open X-Embodiment Collaboration, 2023 (1M+ trajectories)

  • Decoder-only Foundation Model for Time-Series Forecasting

    Das et al. — TimesFM, 2024 (rev. 2026)

  • Recent Advances in Reinforcement Learning for Chemical Process Control

    MDPI Processes, 2025

  • Control-Informed Reinforcement Learning for Chemical Processes

    Industrial & Engineering Chemistry Research, 2024

  • Computer vision in precision livestock farming

    PMC Animal Frontiers, 2024

  • Qwen3-VL: A Multimodal Large Language Model Family

    Qwen Team, Alibaba 2026

  • PANNs: Large-Scale Pretrained Audio Neural Networks

    Kong et al., IEEE/ACM TASLP 2020

  • A Review on Acoustic Emission Technique for Machinery Condition Monitoring

    ScienceDirect, Mechanical Systems and Signal Processing 2024

  • NIS2 Directive — EU Critical Infrastructure Cybersecurity (in force 2023; transposition 2024-2026)

    European Commission

  • IEC 62443 — Industrial Automation and Control Systems Security

    IEC, ongoing series