AI & Machine Learning
ML in production under ISO 42001 — from notebook to SLA
We take ML projects past the prototype: custom models, MLOps pipelines, ISO 42001-aligned governance, audit trail, on-prem or cloud deployment. Case: Naranja X — +30% credit approval with non-traditional data.
The applied AI era
Why Now
From experimentation to enterprise systems
AI moved from R&D labs into operational systems running in production. The companies winning are those that built the discipline — data pipelines, model lifecycle, audit trails, governance — not those still demoing notebooks. ROI lives in the operational layer, not the proof-of-concept.
Governance is the new procurement requirement
ISO 42001 (Dec 2023) gave regulators, auditors and boards a common framework for AI governance. EU AI Act enters effect in waves through 2026. Enterprise customers now ask 'how do you govern your AI?' before signing — and answering 'we don't' loses the deal.
The first-mover advantage compounds fast
AI capability is a flywheel — the more decisions you instrument, the more data you collect, the better your next model. Companies that started 12 months ago are now training on private data their competitors don't have. The gap widens monthly.
Capabilities
What We Build
Natural Language Processing
Custom NLP models and LLM integrations for document processing, chatbots, sentiment analysis, and knowledge extraction at scale. We fine-tune transformer architectures on your domain data to achieve accuracy that generic models cannot match. Our NLP pipelines handle multiple languages and integrate with your existing content management and CRM systems.
Computer Vision
Image recognition, object detection, and visual inspection systems for manufacturing, healthcare, and security applications. We build end-to-end computer vision pipelines — from data annotation and augmentation through model training to edge deployment on devices like NVIDIA Jetson and Coral TPUs. Our systems achieve production-grade accuracy with optimized inference times under 100ms.
Predictive Analytics
Machine learning models for demand forecasting, risk scoring, churn prediction, and recommendation engines tailored to your data. We combine traditional statistical methods with deep learning approaches to maximize prediction accuracy. Every model includes explainability dashboards so business stakeholders can understand and trust the predictions driving their decisions.
MLOps & Integration
End-to-end ML pipelines with monitoring, retraining, and seamless integration into your existing infrastructure and workflows. We implement automated data validation, model versioning with MLflow, drift detection, and scheduled retraining triggers. Our MLOps setups run on Kubernetes with GPU orchestration, ensuring your models stay accurate as data distributions evolve over time.
Responsible AI & Model Governance
Bias detection, explainability, monitoring, and model versioning to ensure your AI systems are fair, transparent, and auditable. We implement SHAP/LIME explainability layers, fairness metrics across protected attributes, and automated model cards that document training data, performance benchmarks, and known limitations. Every deployed model includes version control and rollback capabilities.
FAQ
Frequently Asked Questions
More Case Studies
Empresa de energía y utilities (bajo NDA)
Agentic Transformation in Energy & Utilities: How OrchestAI Unified AI Governance Across Business Areas
How an energy and utilities company — operating under NDA — deployed OrchestAI to govern multi-LLM access across administration, HR, operations, demand monitoring, and customer service, ending shadow AI sprawl and establishing an end-to-end audit chain aligned with regulatory requirements.
Multi-LLM
Models orchestrated
End-to-end
Audit chain
Xcapit Labs
Xcapit Privacy: Machine Learning Without Seeing Your Data
How Xcapit Labs built a platform that enables collaborative machine learning on fully encrypted data using Fully Homomorphic Encryption (FHE), so organizations can train AI models together without ever exposing their sensitive information.
559+
Automated tests
15+
ML algorithms
Relevant Industries
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Production AI pattern
From models to operational decisions
A reusable pattern for AI systems: data pipelines, model evaluation, MLOps, monitoring, compliance, and user-facing workflows that survive production.
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