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AI agents with review gates

Tool-using agents for bounded tasks, with queues, monitoring, and approval where risk requires it.

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Recent project: Rautenberg Pitch EngineRead the case study

When this is the right step

Your team retypes what already exists digitally

Emails, PDFs and forms are read, sorted and entered into a system. The work repeats every day and ties up your best people.

AI trials stay stuck in the chat window

A few people use ChatGPT for drafts. It never became a workflow with data, permissions and review.

Without approval nobody trusts the result

An agent that writes or sends unchecked is a risk. The point where a person approves sensitive steps is missing.

What changes for your team

  1. Repetitive processing gets handled by the workflow, with people reviewing instead of typing
  2. Response and handoff times shrink because routing happens automatically
  3. Triage, extraction, and reporting come out consistent across every case
  4. Every AI decision stays visible and reviewable, with overrides logged

The Approach

The work starts by separating what AI should draft, classify, extract, or suggest from what a person should approve. The review loop, data flow, prompts, model calls, fallbacks, and operational interface are then designed around that boundary.

The Outcome

Your team gets a faster workflow without handing important judgement to a black box. AI handles repetitive steps, people stay responsible for decisions.

Rautenberg Pitch Engine: A Claude Code Research Studio for a Documentary ProducerRautenberg Pitch Engine: A Claude Code Research Studio for a Documentary ProducerRautenberg Pitch EngineRead the case study

What you get

Build focus

  • Document extraction and structured data capture from messy inputs.
  • Support, lead, or request triage with confidence-aware routing.
  • Approval queues where AI drafts and humans review before action.
  • Reporting drafts, summaries, and internal handoff notes.
  • RAG systems and custom assistants grounded in your own knowledge base.
  • Monitoring, logging, and fallback paths for production use.

Included

  • Input capture from forms, inboxes, documents, or databases.
  • Prompt, model, and tool orchestration for the specific task.
  • Human review interface with approve, edit, reject, and escalate actions.
  • Structured outputs saved back into the system of record.
  • Evaluation checks, confidence signals, and error handling.
  • Analytics for throughput, accuracy, and manual override patterns.

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Frequently Asked Questions

Do you build fully autonomous AI agents?
Only when the risk profile supports it. Most business workflows work better with human-in-the-loop automation, where AI drafts, extracts, classifies, or recommends and a person approves important actions.
Can you use our documents or internal knowledge?
Yes. Retrieval-based workflows and assistants that use your approved knowledge sources can be built. Access control, data handling, and update processes are scoped up front.
How do you avoid hypey AI experiments?
The project starts from the workflow and business outcome. If AI does not improve speed, quality, consistency, or cost in a measurable way, it should not be the centre of the build.