AI Integration
Improving Retrieval Quality in RAG Systems
Useful retrieval-augmented generation depends on content structure, permissions and evaluation - not only embeddings.
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Insights & Blog
Practical thinking for software that has to work in the real world
Original perspectives from the Juan Infotech team on integrating AI, engineering dependable products and supporting the systems around them.
Showing 14-25 of 85 insights
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Focused guidance for business, product and technology teams planning what to build next.
AI Integration
AI Integration
Checkpoints for AI Workflow Automation
AI automation is safer when validation, approval and recovery checkpoints are designed into the journey.
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AI Integration
Data Privacy Boundaries for AI Integration
AI features need explicit rules for what information may enter a model, remain in logs or appear in responses.
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AI Integration
Moving an AI Pilot Into Production
Production AI needs evaluation, monitoring, ownership and fallback behaviour that a demonstration rarely includes.
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AI Integration
Build an AI Evaluation Dataset From Real Business Work
A representative evaluation set makes AI quality measurable across routine requests, ambiguity and important exceptions.
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AI Integration
Defending RAG Assistants Against Prompt Injection
Grounded assistants need layered controls because retrieved documents and user messages can both contain unsafe instructions.
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AI Integration
Choose an AI Model for the Task, Not the Headline
Model selection should balance task quality, response time, privacy, operating cost and the consequences of failure.
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AI Integration
Controlling Generative AI Costs in Production
AI cost control starts with workload design, context discipline, model routing and visibility by feature and customer.
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AI Integration
Use Structured AI Outputs for Dependable Software Integration
Schemas, validation and recovery rules help applications consume AI results without treating free-form text as dependable data.
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AI Integration
Bounded AI Agents for Business Operations
Useful AI agents operate inside clear goals, approved tools, spending limits and checkpoints for consequential actions.
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AI Integration
AI Document Processing With Human Validation
Document extraction becomes operationally useful when confidence, field validation and exception review are designed together.
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AI Integration
Designing the Human Handoff in Conversational AI
A good AI conversation recognises when to stop, transfers useful context and sets clear expectations for human follow-up.
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