The Access solution
Access AI orchestration layer for industry
Our approach is neither a new MES nor a new ERP. It is an intelligence layer that connects to the existing — robot code, PLCs, sensors, MES, ERP, quality, maintenance — and orchestrates nine concrete workflows. Each workflow is measurable, each deployment is progressive, each AI decision remains explainable and arbitrable by humans.
Workflow 01
Workflow 01 — ATLAS Robotics: decoding multi-brand robot code
A multi-brand industrial cell accumulates KRL (KUKA), RAPID (ABB), URScript (Universal Robots) code written over fifteen years by different operators. Nobody knows precisely what each program does. ATLAS Robotics applies the methodology proven on ten legacy migrations to the robotics world: retrieve .src and .dat files, automatic parsing, complete cartography (logic, I/O dependencies, machine states, safety conditions, dead code identified), validation in manufacturer simulator (URSim, RobotStudio, KUKA.Sim) before any deployment. The client retains control over actual production deployment, piloted by their certified integrator.
Technology
ATLAS methodology transposed to KRL, RAPID, URScript, Karel, Ladder. LLM with specialized parsers. Manufacturer simulators for validation.
Customer impact
The senior operator sees his coded knowledge documented before retirement. The new operator understands in two weeks what previously took six months. The production manager regains mastery of the tool, independent of turnover.
Business impact
Reduced operational risk on poorly documented cell shutdowns. Calm preparation for OEE and predictive maintenance initiatives that rely on the truth of the code. Increased independence from manufacturer integrators.
Operations impact
Complete cartography of a multi-brand fleet in weeks instead of months of manual audit. ISO 9001 documentation regenerable from code at each evolution. Skill transfer systematized.
Workflow 02
Workflow 02 — Data twin for predictive analytics
The site wants to prepare a predictive maintenance and energy optimization initiative. Instead of investing directly in a heavy physics twin (Siemens NX MCD, Ansys Twin Builder), we start with a lightweight data twin: extraction from robot and PLC code of kinematics, I/O mapping, declared sensors, expected states. This metadata layer feeds a time-series database connected to real sensor streams. Predictive analytics relies on this structured data model, not on raw unqualified streams. The data twin can later be connected to an existing physics twin or exploited alone.
Technology
Metadata extraction from robot/PLC code. MQTT/Kafka for sensor ingestion. TimescaleDB or InfluxDB. Classic ML (XGBoost, LSTM) for anomaly detection.
Customer impact
The client retains complete ownership of the data model. They are not locked in by a physics twin vendor. They can evolve the analytical layer at their own pace.
Business impact
Progressive investment (light data twin first, physics twin later if relevant). Measurable ROI in months on predictive maintenance without committing hundreds of thousands of euros in upstream licensing.
Operations impact
Operational analytical layer in six to nine months instead of eighteen to twenty-four for a classic physics twin project. Evolutive, documented model, maintainable internally after transfer.
Workflow 03
Workflow 03 — Multi-sensor predictive maintenance
A production line suffers unplanned shutdowns two to three times per month. Sensors (vibration, temperature, motor current, pressure) stream data to a historian, but nobody has time to analyze it. The orchestration layer continuously compares observed profiles to nominal profiles, detects weak-signal drifts heralding failure (typically two to six weeks ahead), generates prioritized alerts with intervention recommendations. The maintenance manager receives a clear dashboard each morning: which machines to monitor in priority, which parts to order, which intervention windows to favor.
Technology
OPC UA, MQTT, historian connectors. ML models (autoencoders, LSTM) trained on nominal signals per machine. CMMS integration for closing the loop.
Customer impact
The operator no longer suffers brutal shutdowns in the middle of a night shift. Maintenance is planned, communicated, anticipated. Workshop social climate improves as uncertainty decreases.
Business impact
Measurable reduction in unplanned shutdowns. Savings on spare parts (ordered just in time, not heavy preventive stock). OEE increase.
Operations impact
Maintenance planning based on real wear, not theoretical frequencies. Cognitive load reduction for the maintenance manager who shifts from firefighter to pilot mode.
Workflow 04
Workflow 04 — End-of-line vision quality
Quality defects (scratches, deformations, weld defects, incorrect labeling) are detected today at end of line or in lab control, too late to correct the batch. An AI vision system (industrial cameras + VLM visual model) inspects each piece or product end of line, detects defects in real time, triggers either automatic rejection or operator alert, updates the quality dashboard instantly. Models are trained on client-specific defects (not a generic model) and learn continuously from operator corrections.
Technology
Industrial cameras (Basler, Cognex). VLM models trained per defect. Edge inference (NVIDIA Jetson, Intel NUC). MES integration for traceability.
Customer impact
The end customer receives a compliant product, with no latent defect to discover during use. Reduction of returns, disputes, recalls.
Business impact
End-of-line scrap rate reduction. Fast decision on rework versus rejection on material in progress. Quality data tracked for client audits and certifications.
Operations impact
The quality operator shifts from exhaustive manual control (tiring, fallible) to control of cases flagged by AI (concentrated on value-add). Cognitive relief and improved perceived quality of the role.
Workflow 05
Workflow 05 — Real-time OEE and drift alerts
OEE (Overall Equipment Effectiveness) is calculated monthly by management control, with two weeks delay. The production manager always reacts after the fact. The orchestration layer calculates OEE continuously, machine by machine, line by line, by cross-referencing sensor states, MES orders, quality results. Deviations versus target trigger prioritized alerts, with automatic decomposition of causes (availability, performance, quality). The plant manager sees the site in real time on a single screen. Line managers receive their targeted alerts.
Technology
OPC UA, MES, quality connectors. Real-time OEE calculation. Adaptive dashboards per role (executive, production, line, operator).
Customer impact
The internal client (production team) receives useful information at the right time, not a monthly unactionable report. Pilotage becomes possible, the sense of control increases.
Business impact
Quick reaction to drifts, measurable OEE gain in months. Investment decisions enlightened by data, not by intuition.
Operations impact
End of time-consuming manual monthly reporting. Management control concentrates on strategic analysis, not figure collection.
Workflow 06
Workflow 06 — Production energy optimization
Energy represents a growing share of production cost. Furnaces, compressors, refrigeration units consume massively, sometimes peaking at the worst tariff moment. The orchestration layer continuously analyzes consumption curves, identifies optimization margins (peak shaving, furnace planning at off-peak rates, compressor shutdown at low load, heat recovery). It proposes planning reconfigurations testable in simulation before deployment. The operator retains final control over production-versus-energy arbitrations.
Technology
Smart energy meters. Predictive consumption models. Constrained optimization (production planning).
Customer impact
The internal client (energy/CSR team) has a serious tool to pilot the energy transition instead of managing reactively on bills.
Business impact
Direct savings on energy bill. Preparation of CSRD reporting and product carbon footprint. Commercial argument toward customer principals demanding on Scope 3.
Operations impact
Relief for the energy manager who shifts from manual Excel analysis to assisted pilotage. Investment plans (heat recovery, insulation) prioritized by measured return.
Workflow 07
Workflow 07 — Maintenance assisted by procedure chatbot
An operator on shift detects a machine failure at night. Today: he searches paper or PDF manual, loses twenty minutes, ends up calling the on-call maintenance manager. With a procedure RAG chatbot: he describes the failure in natural language or photographs the error code, the chatbot presents the adapted diagnostic procedure, step by step, with reminders of critical safety points. If the situation exceeds standard scope, automatic escalation to the expert with already summarized context. Variant with AR glasses for visual guidance on the machine.
Technology
RAG on maintenance documentation, manufacturer manuals, internal feedback. LLM with access control. CMMS integration. AR option (HoloLens, RealWear).
Customer impact
The internal client (shift operator) feels supported, not abandoned facing a failure. Reduced stress in unforeseen situations. Confidence in his role increases.
Business impact
Reduced mean time to repair (MTTR). Increased machine availability. Fewer night calls disturbing the maintenance manager.
Operations impact
Continuous capitalization of feedback in the RAG. Accelerated onboarding of new operators. Living maintenance documentation instead of frozen.
Workflow 08
Workflow 08 — Orchestrated ISO documentation and traceability
ISO 9001, 14001, 45001, IFS, FDA certifications require up-to-date documentation, documented audits, complete traceability. Today: manual procedure maintenance, drift between official procedure and shop floor reality, external audits experienced as ordeal. The orchestration layer couples a RAG on documentation with a continuous observation system: when a procedure drifts (operator does differently from what is written, and successfully), alert to the quality manager for formal update. External audit becomes a formality because everything is continuously traced.
Technology
RAG on quality documentation. Coupling with MES/SCADA for continuous observation. Pre-formatted audit report generation.
Customer impact
The client principal (notably automotive, aerospace, pharma) receives impeccable traceability. Reduced quality disputes.
Business impact
Faster, less costly external audits. Minimized risk of certification loss. Commercial argument toward demanding large accounts.
Operations impact
The quality manager shifts from firefighter to strategic pilot. Documentation coherent with reality, not a holy book disconnected.
Workflow 09
Workflow 09 — Operator training HITL and skill transfer
A senior operator retires in eighteen months. His knowledge is documented nowhere. With a HITL conversational assistant: guided interview sessions that capture his reasoning facing typical situations (what would you do if... ?), progressive enrichment of a corpus dedicated to his role, generation of training simulators for future operators (case studies, decision trees, auto-generated tutorial videos). Tacit knowledge becomes transmissible, validated by the operator himself.
Technology
Guided conversational interfaces. Multimodal capture (audio, video, photo). Training module generation. HITL framework for operator validation.
Customer impact
The internal client (future operator) is trained on real and validated content, not generic theory. The learning curve accelerates.
Business impact
Preservation of industrial heritage facing the baby-boom retirement. Reduced risk of critical know-how loss. Accelerated initial training for recruits.
Operations impact
The departing operator values his end of career in transmission, which strengthens his engagement. Training becomes a maintained asset, not a chore.