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AI Consulting Services
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AI Consulting
Commercial and technical guidance for enterprises choosing where AI should create value, how it should be governed, and what architecture should support it.
Supporting articles
Building Document Understanding Pipelines with On-Premises Small Language Models
A practical guide to constructing document understanding pipelines using small language models on-premises, covering OCR integration, layout analysis, entity extraction, and classification workflows.
GPU Memory Management and KV Cache Optimization for On-Premises LLM Serving
Practical strategies for managing GPU memory and optimizing KV cache allocation when serving large language models on-premises, from paged attention to dynamic memory pooling.
Multi-Region On-Premises AI Deployment: Synchronizing Models Across Data Centers
How to deploy and synchronize AI models across geographically distributed on-premises data centers while maintaining consistency, low latency, and compliance with regional data regulations.
Enterprise AI Transformation Playbook: From Pilot to Production (2026)
A practical playbook for enterprise AI transformation covering readiness assessment, architecture decisions, pilot design, governance, organizational change, and scaling from experimentation to production-grade AI capability.
Agent-Driven Organization Design: Framework, Patterns, and Implementation
A comprehensive framework for designing organizations where AI agents participate in execution, coordination, and decision-making as operational actors, not just assistive tools.
The Complete Guide to On-Premises AI for European Enterprises (2026)
A comprehensive guide covering architecture, security, cost management, model operations, governance, and scaling strategies for enterprises deploying AI on private infrastructure in Europe.
AI Data Security and Privacy On-Premises: A European Architecture Guide
How to design on-prem AI for GDPR, data residency, access control, and auditable privacy in European enterprise environments.
Best Practices for On-Prem AI Agents
Operational best practices for building and governing AI agents on private infrastructure with strong observability, tool control, and security.
Cloud vs. On-Prem AI Cost Management: Where the Economics Actually Change
A practical framework for comparing cloud AI spend with private AI capacity and identifying the cost crossover point.
Common Mistakes in On-Prem AI Ecosystem Management
The operational mistakes that weaken private AI environments over time, from unclear ownership to unmanaged model sprawl.
Latest Design Principles for Enterprise AI Systems
Modern design principles for enterprise AI systems that need to stay governable, composable, and useful in production.
Multi-Model Agent Architecture: How to Combine Specialist Models in Enterprise AI
A practical architecture for agent systems that combine small models, large models, tools, memory, and routing in private enterprise environments.
Achieving Real Results with Small Language Models On-Premises
Why small language models often outperform larger, costlier deployments in enterprise on-prem AI when paired with the right routing and context design.