Controlled intelligence inside real workflows

AI Agents and Assistants

Design AI-assisted systems that retrieve knowledge, use approved tools, process information, and coordinate work within explicit boundaries.

Where it fits

Start with the operating constraint.

Scope and feasibility are confirmed after discovery; this page describes capability, not a fixed package.

Problems this addresses

  • Teams spend significant time finding and summarizing internal knowledge
  • Documents require repetitive classification, extraction, or review
  • Operational requests move slowly between systems and people
  • An AI prototype lacks permissions, monitoring, or production controls

Who it can serve

  • Operations teams with repeatable knowledge workflows
  • Support teams handling structured and unstructured requests
  • Organizations integrating AI into an existing product
  • Product teams moving a model experiment toward controlled production use

What can be built

Product shape follows the workflow.

Features are selected because they support an agreed outcome, ownership model, and operating requirement.

Deliverable

Customer support agents

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Deliverable

Internal knowledge agents

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Deliverable

Document processing agents

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Deliverable

Sales qualification assistants

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Deliverable

Operations and reporting agents

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Deliverable

Workflow orchestration agents

Planned, implemented, tested, documented, and released according to the agreed scope and acceptance criteria.

Possible capabilities

Functions selected for the actual use case.

  • Retrieval from approved knowledge sources
  • Tool use through permissioned APIs
  • Structured outputs and validation
  • Human review, escalation, and approval gates
  • Evaluation, traceability, usage monitoring, and cost controls

Implementation

A controlled path to production.

  1. 01

    Map the workflow, decisions, data sensitivity, and failure impact

    Decisions, risks, and review criteria stay visible through this stage.

  2. 02

    Define the agent’s responsibilities, prohibited actions, and escalation rules

    Decisions, risks, and review criteria stay visible through this stage.

  3. 03

    Prototype with representative, authorized data

    Decisions, risks, and review criteria stay visible through this stage.

  4. 04

    Evaluate quality, safety, latency, and cost against agreed scenarios

    Decisions, risks, and review criteria stay visible through this stage.

  5. 05

    Integrate permissions, audit events, and human checkpoints

    Decisions, risks, and review criteria stay visible through this stage.

  6. 06

    Release gradually and monitor real usage

    Decisions, risks, and review criteria stay visible through this stage.

Client outcome

Useful at launch. Operable afterward.

What the client receives

  • A bounded AI workflow with documented limits
  • Clear human ownership for consequential decisions
  • An evaluation and monitoring approach suited to the use case

Support after launch

  • Prompt and workflow refinement
  • Model and retrieval evaluation
  • Usage and cost optimization
  • Knowledge-source maintenance

Questions

Important boundaries, made explicit.

Will the agent operate without human oversight?

Not by default. The autonomy level depends on the task, permissions, reversibility, data sensitivity, and cost of error. Critical or consequential actions should include appropriate human review.

Can you guarantee a model will always be correct?

No. Model outputs can be incomplete or incorrect. We use constrained tasks, validation, evaluation, monitoring, and escalation to manage that limitation.

What determines scope and price?

The workflow, model requirements, data preparation, integrations, evaluation depth, security controls, usage volume, and support needs are assessed before a proposal is prepared.

Next step

Request an Estimate

We start with the job to be done, the cost of error, and the human decision points—then determine where an AI agent is appropriate.

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