AI & Automation

Put language models inside a controlled workflow

Our LLM integration consulting helps you define a clear task, provide relevant context, connect the right tools, and set review points before putting a language model into live work.

Conceptual language-model workflow with bounded inputs and reviewed output
  • Scoped context
  • Output evaluation
  • Human review

Our Approach

Integrate, evaluate, and keep control

  1. Define the task

    Specify allowed inputs, expected outputs, users, and situations where the model must not decide alone.

  2. Build the boundary

    Connect approved context and application actions with access limits, logging choices, and fallback behavior.

  3. Evaluate in use

    Test representative cases and failure modes, then monitor changes against agreed quality and safety criteria.

Where It Fits

Industries and use cases

  • Editor reviewing publishing materials beside a laptop

    Knowledge teams

    Assist document-heavy work with source and review controls.

  • Professional-services team member reviewing digital client workspaces

    Customer support

    Explore bounded assistance that routes uncertain answers to people.

  • Software team reviewing product dashboards in a shared workspace

    Software products

    Embed task-specific language features with clear user expectations.

What We Help With

Integration design considerations

  1. Context access

    Limit the information available to each request according to permissions and purpose.

  2. Output handling

    Validate formats, expose uncertainty where relevant, and route sensitive decisions for review.

  3. Operational safeguards

    Plan cost controls, error recovery, evaluation, and change management.

Technology

Tools and technologies

  • LLM APIs symbol

    LLM APIs

    Connect a selected model to a defined application task.

  • Retrieval patterns symbol

    Retrieval patterns

    Supply permitted context when the task requires source material.

  • Output evaluation symbol

    Output evaluation

    Test usefulness, failure modes, and factual grounding.

  • Human review symbol

    Human review

    Keep consequential or uncertain outputs under appropriate oversight.

Frequently Asked Questions

Questions, answered

Can an LLM be guaranteed to give correct answers?

No. Its outputs must be evaluated for the use case, and important decisions need suitable checks or human review.

Does integration require sending all our data to a model?

No. The design should minimize and control what is shared. The appropriate model, hosting, and data arrangement depend on your requirements.

Start a Conversation

Scope an LLM feature with clear boundaries

Tell us the task, data sensitivity, and how your team would review its output.