Services

Artificial Intelligence and Industry 4.0

At QUALIRION, we design, develop, and implement customized Artificial Intelligence solutions, from the development and deployment of intelligent agents to the integration of chatbots and virtual assistants across multiple platforms. Our AI projects encompass both the automation of cognitive processes (natural language recognition, computer vision, predictive analytics, data classification, data analysis and exploitation) and the improvement of user experience through interactive and adaptive tools. We focus on building scalable, secure, and business-oriented solutions that generate tangible value.

In addition, we drive competitiveness by combining applied AI, advanced analytics, and industrial software. We design and implement solutions with measurable return and production-ready architecture (MLOps/GenAI Ops), integrated with your systems (ERP / CRM / MES / SCADA / PLC, BI, DMS) and aligned with compliance (GDPR/LOPDGDD) and corporate security.

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Industrial processes

We improve production stability and product quality with Artificial Intelligence applied to the plant: vision for automatic inspection, time series models for predictive maintenance, and recommenders to adjust parameters and recipes. We integrate the solution with PLC, SCADA, and MES to operate in real time and measure impact on OEE, scrap, and unplanned downtime.

1. Some benefits that it can bring you

  • Increase OEE and process stability.
  • Reduce scrap, rework, and waste.
  • Decrease unplanned downtime and format changeover time.
  • Detect defects earlier and with fewer false positives.
  • Standardize quality criteria between shifts and lines.

2. Some typical cases

  • In-line defect detection with vision (classification, detection, segmentation).
  • Predictive maintenance: Remaining Useful Life (RUL) estimation and early warnings.
  • Optimization of set-points and recipes to minimize variability.
  • Real-time traceability and monitoring at the lot/order level.
  • Verification of assembly, presence/absence, and automatic counting.

3. Some examples of what we can deliver

  • Trained models (vision and time series) with their API/service ready for the plant.
  • Operational and quality dashboards (includes OEE and root cause analysis).
  • Operation guide, alert response playbook, and continuous improvement plan.
  • Integration with PLC/SCADA/MES and, if applicable, specific HMI.
  • Labeled dataset and technical documentation for auditing.

4. Some examples of our procedures

  • Discovery of measurement points and definition of process KPIs.
  • Data capture (sensors, historians) and sample labeling.
  • Training and validation in a test environment; shadow testing online.
  • Adjustment of thresholds and tolerances, design of manual fallback if applicable.
  • Controlled deployment, continuous monitoring, and periodic retraining.

5. Some indicators that we measure

  • OEE (availability, performance, quality).
  • Defect rate and cost of non-quality.
  • Unplanned downtime and mean time between failures.
  • Format changeover time and performance per shift/line.
  • Detection accuracy and false positive/negative rates.

6. Some platforms and systems with which we can integrate

  • PLC, SCADA, and MES of the plant.
  • Historian and fieldbus/sensors.
  • Rejection/actuation systems and HMI.
  • Corporate BI and data lake/warehouse.
  • On-prem/edge/cloud deployment with monitoring and CI/CD.

Business and business processes

Fewer repetitive tasks, faster decisions, and better-served customers.

1. Some benefits that it can bring you

  • Reduces cycle times in administrative and commercial tasks.
  • Increases team productivity with assistants/co-pilots.
  • Improves decision quality with actionable analytics and alerts.

2. Some typical cases

  • Extraction and validation of data in invoices, delivery notes, and contracts (with human review when applicable).
  • Assistants/co-pilots for sales, support, and legal with secure access to internal knowledge (RAG).
  • Scoring and forecasting (risk, churn, sales) with automatic notifications to those responsible.

3. Some examples of what we can deliver

  • Automated flows from start to finish (documents, approvals, records).
  • Connectors ready for ERP/CRM/DMS and Office/Google/Other suites.
  • Dashboards (executive and operational) and catalog of metrics/KPIs.
  • User guides and adoption and security policies.

4. Some examples of our procedures

  1. Map & prioritize: we choose 3–5 high-impact tasks and available data.
  2. Automate & validate: functional prototype with users (short iterations).
  3. Deployment and monitoring: of KPIs and continuous adjustments.

5. Some indicators that we measure

  • Cycle time per process/document.
  • Productivity per role (tasks/hour).
  • Service SLAs and internal/customer satisfaction.

6. Some platforms and systems with which we can integrate

  • ERPs
  • CRMs
  • DMS
  • Emails
  • Office 365 suites and earlier versions
  • Google Suites (current Google Workplace)
  • BI (Power BI or others)
  • RAGs on internal repositories.

Supply chain management

Just the right and necessary stock, fewer breakages, and on-time deliveries.

1. Some benefits that it can bring you

  • Reduces average stock without compromising service.
  • Prevents breakages and improves OTIF/ETA with proactive alerts.
  • Optimizes logistics costs (routes, loads, transport).

2. Some typical cases

  • Multivariable demand forecasting by SKU/center.
  • Dynamic replenishment and adaptive safety levels.
  • Optimization of routes and load assignment (VRP) and picking sequencing.
  • End-to-end visibility with OTIF/ETA tracking and alerts.

3. Some examples of what we can deliver

  • Forecasting engine with scenario and seasonality simulation.
  • Replenishment recommendations by API integrated into your ERP / WMS / TMS.
  • Operational panel (inventory, orders, transport) and compliance reports.
  • Playbook of inventory policies and best practices.

4. Some examples of our procedures

  1. Model and simulate: demand, deadlines, capacity, and costs.
  2. Operate and alert: replenishment rules, routes, and deviations (OTIF/ETA).
  3. Measure and adjust: review of KPIs and continuous improvement.

5. Some indicators that we measure

  • Average stock and breakages by family/SKU.
  • OTIF and ETA accuracy.
  • Transport cost per order/ton/km.
  • Lead time and service level.

6. Some platforms and systems with which we can integrate

  • ERPs
  • WMS
  • TMS
  • BIs
  • Recommendation APIs
  • Scenario simulators.

New business ideas

From idea to MVP with data, security, and measurable return.

1. Some benefits that it can bring you

  • Accelerates the transition from idea to product with controlled risks.
  • Validates with real users before investing heavily.
  • Measures adoption and ROI from day one.

2. Some typical cases

  • Prototypes of assistants or intelligent products (B2B/B2C).
  • Data services and embedded analytics in your application.
  • New lines of business supported by AI for specific segments.

3. Some examples of what we can deliver

  • Business case and prioritized backlog.
  • Functional MVP with telemetry and access control (SSO).
  • Runbooks, release plan, and adoption guide.

4. Some examples of our procedures

  1. Discover and prioritize: workshops with business, definition of KPIs and success criteria.
  2. Prototype and validate: MVP with users, short iterations, and usage metrics.
  3. Scale and operate: harden architecture, observability, and product roadmap.

5. Some indicators that we measure

  • Time to PoC/MVP.
  • Adoption (MAU, retention, satisfaction).
  • Cost per use case and estimated return.

6. Some platforms and systems with which we can integrate

  • SSO, observability, data pipelines, and BI.
  • Integration with ERPs / CRMs / DMS
  • Cloud or on-premise deployment according to compliance.

Integration of model APIs (OpenAI, Gemini, Anthropic, Mistral, Cohere, Meta, etc.)

We can adapt to any model or API. Just the right and necessary stock, fewer breakages, and on-time deliveries.

1. Some benefits that it can bring you

  • Connect your systems to the market’s leading models with security and cost control.
  • Accelerate cases such as internal assistants, document analysis, and recommendations without redoing your stack.
  • Avoid rigid dependencies with a multi-vendor architecture and contingency plans.

2. Some typical cases

  • Internal assistants with tools (function calling) and secure access to corporate knowledge.
  • Summary, classification, and data extraction in documents, emails, and chats.
  • Recommendations and personalization in portals or own apps.
  • Automated flows that combine LLMs with existing systems (ERP, CRM, DMS, etc.).

3. Some examples of what we can deliver

  • Services/SDK ready to integrate (REST/gRPC) with permission and quota control.
  • Library of templated and versioned prompts, with usage guides per team.
  • Cost and quality dashboards (tokens, latency, success rate).
  • Security and privacy policies (retention, anonymization, traceability).
  • Test suite and continuous evaluation (GenAI Ops) to avoid regressions.

4. Some examples of our procedures

  • Analysis of use cases and available data; choice of provider(s) and models.
  • Design of multi-vendor architecture with fallback and limits per application/user.
  • Implementation of orchestration, caching, cost control, and rate-limits.
  • Integration in test and production environments with observability (logs, metrics, alerts).
  • Security and compliance review (GDPR/LOPDGDD), drafting of policies and auditing.
  • Control and governance of data

5. Some indicators that we measure

  • Perceived utility and accuracy of the response.
  • Latency p50/p95 and service stability.
  • Cost per interaction and per use case.
  • Rate of substantiated responses (with references) and human rework.

6. Some platforms and systems with which we can integrate

  • Model providers with API: OpenAI/Azure OpenAI, Google Gemini, Anthropic, Mistral, Cohere, Meta (and other compatible ones).
  • Internal systems: ERP, CRM, DMS, intranet, and databases.
  • Productivity tools: Microsoft 365, Google Workspace, Slack, Teams.
  • Data and analytics: BI (Power BI, Looker, others), data lakes/warehouses, vector databases.
  • Operation: CI/CD pipelines, monitoring, and activity logging.

Customized RAG (Retrieval-Augmented Generation) solutions

With RAG (Retrieval-Augmented Generation), we convert your corporate knowledge into accurate and verifiable answers. Instead of relying solely on the model’s “memory,” the system retrieves relevant fragments from your repositories (DMS, intranet, wikis, ERP/CRM, databases) and uses them to elaborate the answer with citations to the source, minimizing hallucinations. All this while respecting permissions and security policies, with secure indexing, continuous updating, and observability of quality and costs. The solution can operate in your cloud or on-premise and is compatible with the main LLMs with API (OpenAI/Azure, Gemini, Anthropic, Mistral, Cohere, Meta) and with open-source models when greater data control is required.

1. Some benefits that it can bring you

  • Accurate answers based on your own internal documents and data.
  • Security and permissions respected: each person sees only what they should see.
  • Reduction of search time and less rework in repetitive queries.
  • Traceability with source citations for auditing and trust.
  • Less model “hallucination” by being anchored to verified information.

2. Some typical cases

  • Internal FAQs about procedures, contracts, or regulations with cited answers.
  • Support for sales, legal, or customer service teams with access to updated documentation.
  • Unified semantic search over DMS, intranet, wikis, and databases.
  • Assistants for operations: SOPs, maintenance, quality, and safety in the plant.

3. Some examples of what we can deliver

  • Pipelines for ingestion and normalization of documents (PDF, Office, emails) and structured data.
  • Vector and hybrid indexes ready for production with version control.
  • Q&A service with citations, access filters, and query auditing (API/SDK).
  • Connectors to your repositories (DMS, ERP, CRM, intranet) and scheduled refresh tasks.
  • Observability panel with usage, quality, and cost metrics.

4. Some examples of our procedures

  • Discovery of sources and permissions, definition of taxonomies and relevance.
  • Cleaning, segmentation, and enrichment of documents (partitioning, metadata).
  • Choice of recovery strategy (dense, BM25, hybrid) and reranking.
  • Orchestration of the RAG flow: retrieve, filter, cite, and respond with automatic evaluation.
  • Hardening for production: caches, rate-limits, cost control, and alerts.
  • Validation with users, red teaming, and periodic regression tests.

5. Some indicators that we measure

  • Percentage of responses with valid citation and accuracy perceived by the user.
  • Average response time and average search time avoided.
  • Rate of questions resolved without human intervention.
  • Source coverage (by repository) and index freshness.
  • Cost per interaction and per use case.

6. Some platforms and systems with which we can integrate

  • Repositories and productivity: SharePoint, OneDrive, Google Drive, Confluence, Notion, wikis, others.
  • Corporate systems: DMS, ERP, CRM, intranet, SQL/NoSQL databases.
  • Vector databases and search: Elasticsearch/OpenSearch, Pinecone, Milvus, FAISS, Weaviate.
  • Models and orchestration: OpenAI, Azure OpenAI, Google Gemini, Anthropic, Mistral, Cohere, Meta Llama, and compatible open-source models.
  • BI and observability: Power BI, Looker, logging, and metrics in your current stacks.
  • Deployment: cloud, on-premise, or hybrid according to security and compliance requirements.

Custom AI agents

Just the right and necessary stock, fewer breakages, and on-time deliveries.

1. Some benefits that it can bring you

  • Offload repetitive work from teams and accelerate response times.
  • Increase first-call resolution and reduce escalations.
  • Decrease operational errors with guided and auditable flows.
  • Scale operations 24/7 while maintaining predictable costs.

2. Some typical cases

  • Customer or IT support: classify and respond to tickets, gather context, escalate with discretion.
  • Operations: create/update orders in ERP, reconcile incidents, check inventory.
  • Sales: prepare proposals with updated data, record activities in CRM.
  • Finance and legal: pre-checks of documentation, basic reconciliations, and reminders.
  • Back-office: user onboarding/offboarding, credential resets, case opening.

3. Some examples of what we can deliver to you

  • Agents defined with role, allowed tools, and memory (if applicable).
  • Playbook and policies of operation (what the agent can and cannot do).
  • Observability panel (events, quality, costs, agent decisions).
  • Test suite and regression scenarios for future changes.
  • Documentation of integration (API/SDK) and guides for users and admins.

4. Some examples of our procedures

  • Discovery of candidate tasks and definition of objectives/KPIs.
  • Design of the toolset (query DB, read email, create ticket, update ERP).
  • Guardrails and security: minimum permissions, limits per action, human review of where it comes from.
  • Controlled tests (sandbox), pilots with users, and gradual deployment.
  • Continuous monitoring, failure analysis, and iterative improvement of the agent.

5. Some indicators that we measure

  • Percentage of tasks resolved without human intervention.
  • Average time of resolution by type of task.
  • Escalation rate to people and main causes.
  • Avoidable errors, user satisfaction, and cost per task.

6. Some platforms and systems with which we can integrate

  • Business systems: ERP, CRM, DMS, intranet, and databases (SQL/NoSQL).
  • Support/ITSM and project management tools.
  • Interaction channels: email, corporate chat (Teams/Slack), web, or internal apps.
  • Model providers: OpenAI/Azure OpenAI, Google Gemini, Anthropic, Mistral, Cohere, Meta, and compatible open-source models.
  • Observability, CI/CD, and access control of your organization.

Custom language models

When data control, cost, and latency matter, we adjust open-source language models (for example, Llama or Mistral families) with your data and terminology. We apply light (LoRA/Adapters) or full fine-tuning, validate quality with evaluation sets aligned to real use, and deploy in private cloud or on-premise with predictable cost and no “lock-in.”

1. Some benefits that it can bring you

  • Total control over data, models, and inference costs.
  • Greater accuracy in your domain (legal, industrial, technical, healthcare, etc.).
  • Stable latency and operation in restricted environments (on-prem/offline if required).
  • Compliance and security aligned with your policies (audit, traceability).

2. Some typical cases

  • Specialized internal assistants (operations, legal, support, engineering).
  • Extraction/normalization of information in technical documents and forms.
  • Classification, guided writing, and verification of sensitive content.
  • Integration with RAG for cited responses using your internal knowledge.

3. Some examples of what we can deliver to you

  • Adjusted model (e.g., LoRA on Llama/Mistral) with its model card and metrics.
  • Datasets curated for training/validation and reproducible scripts.
  • Inference service (REST/gRPC API) with authentication and usage limits.
  • Prompt templates and best practice guides by team/function.
  • Observability panel (latency, cost, quality, usage) ready for exploitation.

4. Some examples of our procedures

  • Selection of base model and licenses compatible with your case.
  • Data curation: cleaning, anonymization, balance, and synthetic data when it contributes.
  • Training: SFT, LoRA/QLoRA, distillation and, if appropriate, DPO/RLHF.
  • Evaluation: general benchmarks + specific tests (accuracy, security, biases).
  • Inference optimization: quantization (int8/int4), batching, and caching.
  • Secure deployment: CI/CD, rollback, logging, and minimum access controls.

5. Some indicators that we measure

  • Accuracy in target tasks and perceived utility by the user.
  • Latency p50/p95 and throughput.
  • Cost per 1,000 tokens and cost per use case.
  • Rate of responses rejected by policies (security/content).
  • Degradation over time (drift) and % of regressions avoided by test.

6. Some platforms and systems with which we can integrate

  • Models/serving: vLLM, TGI, TensorRT-LLM, GGUF (CPU/GPU) and equivalents.
  • Orchestration and RAG: LangChain/LlamaIndex, vector bases, and hybrid search engines.
  • Infrastructure: Kubernetes, VM, on-prem, or private cloud.
  • Integrations: ERP, CRM, DMS, intranet, BI, and authentication systems (SSO).
  • Observability: your logs/metrics/alerts tools and CI/CD pipelines.

Computer vision

Just the right and necessary stock, fewer breakages, and on-time deliveries.

1. Some benefits that it can bring you

  • Reduce defects and rework with automatic online inspection.
  • Decrease false positives/negatives and stabilize product quality.
  • Increase traceability: reading labels, codes, and serial numbers.
  • Improve area security and regulatory compliance with video analytics.
  • Standardize quality criteria and accelerate line starts and format changes.

2. Some typical cases

  • Detection and segmentation of defects, superficial or dimensional.
  • Classification of parts or products by variant/status/quality.
  • OCR and verification of labeling (batches, expirations, 1D/2D codes).
  • Counting, tracking, and verification of presence/absence of components.
  • Control of packaging and palletizing; assembly verification.
  • Security analytics: unauthorized access, PPEs, restricted areas.

3. Some examples of what we can deliver to you

  • Trained and validated vision models (detection/segmentation/classification/OCR).
  • Real-time inference pipeline with API/SDK (REST/gRPC) and specific HMI.
  • Integration with PLC/SCADA/MES and rejection or signaling systems.
  • Recommended acquisition kit (camera, optics, lighting) and calibration guide.
  • Control panel with quality metrics, alarms, and inspection records.
  • Operational documentation: maintenance and recalibration playbook.

4. Some examples of our procedures

  • Feasibility study: samples, variability, target defect rate, and cycle times.
  • Design of optics/lighting and capture conditions; field tests.
  • Labeling and augmentations; training and validation with clear metrics.
  • Hardening for production: latency, throughput, caching, and tolerances.
  • Tests with real line, adjustment of thresholds, and fall-back to manual inspection when applicable.
  • Model maintenance: periodic re-trainings and drift control.

5. Some indicators that we measure

  • Accuracy (mAP/F1) and rates of false positives/negatives by type of defect.
  • Inspection time per unit and p95/p99 behavior.
  • Correct rejection rate and waste avoided.
  • Availability of the system and average time between failures/adjustments.
  • Impact on OEE and cost of non-quality.

6. Some platforms and systems with which we can integrate

  • Line and control: PLC, SCADA, MES, sensors, and rejection actuators.
  • Cameras and IO: industrial (GigE/USB3/PoE), IP cameras, and edge devices with GPU.
  • Libraries/serving: vision and OCR frameworks, real-time inference servers.
  • Back-office: ERP/BI/DMS for traceability, reports, and historical analysis.
  • Deployment: on-prem/edge/cloud with CI/CD, monitoring, and alerts.
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