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Generative AI

Large language and multimodal models applied to enterprise knowledge work, with retrieval, evaluation and human oversight.

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How a question reaches a governed answer

Knowledge, retrieval, model, reasoning, action — with the user’s permissions evaluated before retrieval, not applied to the answer afterwards.

What it is

Understanding Generative AI

Generative AI refers to models that produce text, images, code or other content from a prompt.

In enterprise use the model is rarely the interesting part: value comes from grounding it in governed organizational knowledge, constraining what it can do, evaluating its output systematically, and integrating it into a workflow where a person remains accountable.

Why it matters

Substantial gains in document-heavy and knowledge-retrieval work — drafting, summarization, search over internal knowledge, code assistance. The gains are real and the failure modes are equally real, which is why evaluation and oversight are part of the capability rather than an afterthought.

Problems it addresses

  • “Our people cannot find information that exists somewhere in the organization.”
  • “Drafting and summarizing documents consumes significant professional time.”
  • “We ran an AI pilot and it never reached production.”
  • “We cannot tell whether an AI system's answers are correct.”

Capabilities

  • Use case identification

    Assessment against value, feasibility, data readiness and risk — with a defined stopping criterion.

  • Retrieval-augmented generation

    Grounding answers in governed enterprise content with citations.

  • Model gateway and abstraction

    Provider abstraction, routing, fallback, prompt versioning and cost tracking.

  • Evaluation

    Accuracy, groundedness, safety, latency and cost measured continuously, not once.

  • Guardrails and oversight

    Input and output filtering, escalation paths and human review where consequences warrant it.

  • Integration

    Intelligence delivered inside the workflow rather than in a separate chat window.

Use cases

  • Enterprise knowledge assistance

    Question answering over internal documentation with citations and permission-awareness.

  • Document processing

    Extraction, classification and summarization at volume.

  • Drafting assistance

    First drafts in a controlled template, reviewed by a person.

  • Engineering assistance

    Code generation and review support with human accountability retained.

Before you commit

Prerequisites, governance and risks

The questions that determine whether this will work in your environment, rather than in a demonstration.

What has to be true first

  • Governed, permission-aware content before retrieval is built over it
  • An evaluation dataset and process before production deployment
  • A defined human oversight model proportionate to the consequences
  • Cost monitoring — token consumption scales with usage in ways that surprise people

Governance

Three controls do most of the work: permission-aware retrieval so a user cannot obtain through AI what they could not access directly; a continuous evaluation process rather than a launch-time check; and a human accountability point for consequential output. AI governance policy should classify use cases by risk and apply oversight proportionately.

Risks

  • Confident, fluent, wrong output — the failure mode is plausibility, not obvious error
  • Data leakage through prompts or through retrieval that ignores entitlements
  • Cost growth proportional to usage
  • Over-reliance without a human accountability point
  • Model and provider change altering behavior beneath a working system

Where Generative AI leads

Every connection below is recorded in the content itself, and each one says why it is here.

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