Latest Blogs.

  1. LLM security: prompt injection, data leakage, and what actually stops them

    LLM security: prompt injection, data leakage, and what actually stops them

    Most LLM security advice is 'write a better system prompt.' The actual defenses are architectural: what the model can see, what it can do, and what never returns to a user unchecked.

    3 min read
    • AI
    • Architecture
  2. LLM observability: what to log, and what it actually catches

    LLM observability: what to log, and what it actually catches

    Traditional APM tells you a request was slow. LLM observability has to answer a different question: was the answer right, and why did the model say that?

    3 min read
    • AI
    • Engineering
  3. How to build an AI analytics assistant

    How to build an AI analytics assistant

    'Ask your data a question' is a text-to-SQL problem wearing a chat interface. The architecture that makes it trustworthy: schema grounding, guardrails, and a query the user can check.

    3 min read
    • AI
    • Engineering
  4. LLM application architecture: the layers that don't change with the model

    LLM application architecture: the layers that don't change with the model

    Model, harness, data layer, and product surface: the four layers of a production LLM application, and which ones survive the next model upgrade.

    3 min read
    • AI
    • Architecture
  5. How we reduced LLM costs in a multi-tenant AI platform

    How we reduced LLM costs in a multi-tenant AI platform

    One customer's heavy usage was inflating everyone's bill. The per-tenant budgeting, routing, and caching changes that cut the platform's running cost by 58%.

    3 min read
    • AI
    • Engineering
  6. Enterprise RAG: what changes with real documents, permissions, and scale

    Enterprise RAG: what changes with real documents, permissions, and scale

    A RAG demo answers questions about a clean folder of PDFs. An enterprise deployment has to handle permissions per document, millions of pages, and daily churn.

    3 min read
    • AI
    • Architecture
  7. RAG vs fine-tuning: what should your business use?

    RAG vs fine-tuning: what should your business use?

    They get pitched as competitors and usually are not. How to tell which problem you actually have, and the cases where the right answer is both.

    3 min read
    • AI
    • Process
  8. RAG architecture: the pipeline from document to answer

    RAG architecture: the pipeline from document to answer

    Ingestion, chunking, embedding, retrieval, reranking, generation: the six stages of a production RAG pipeline, and where each one quietly determines answer quality.

    3 min read
    • AI
    • Architecture
  9. What is RAG, in plain terms?

    What is RAG, in plain terms?

    Retrieval-augmented generation, explained without the jargon: what problem it solves, the four steps in the pipeline, and where it stops being the right tool.

    3 min read
    • AI
    • Architecture

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