Adding AI Steps to a Business Workflow

Most of a business workflow does not need AI. Moving data between systems, checking formats and applying rules work better as ordinary workflow steps, because they behave the same way every time. AI earns its place in the steps that need judgment over messy input. This article explains how Octacer decides where an AI step belongs and how its output is checked.

Typical AI steps in a workflow:

• Classify: decide what an incoming email, ticket or document is, so it reaches the right person.
• Extract: pull fields from an invoice, form or PDF into a structured record.
• Draft: prepare a reply or summary for a person to review.
• Score: rank leads or cases so the important ones are handled first.

Before building, agree what happens when the model is unsure or wrong:

Ask the model for structured output, for example JSON with named fields, and validate it before anything uses it. Check that required fields are present, that values have the right type, and that categories come from an allowed list. If validation fails, the run should stop or go to review. It should never write half a record. Our knowledge-base article on enforcing valid JSON in GPT no-code steps covers the details.

A model answering from general knowledge produces confident, plausible text. For answers about your products, policies or customers, give it your own content and records, and have it say when the answer isn't there. If an agent has already given a customer a made-up answer, the hallucination containment playbook covers the first steps.

Collect real examples before go-live, including the awkward ones: incomplete documents, unusual formats and ambiguous requests. Run the AI step against the same set every time you change the prompt or the model, and trace any failed case before shipping. That is how an AI step gets signed off: on your examples, not on a demo.

We build with OpenAI, Claude and Gemini models, chosen for the task. AI usage is billed to your own accounts by the provider. We estimate the running cost during scoping, so you know it before the build starts.

• One workflow with an AI step: an Automation Sprint. An AI step with guardrails is part of the Complex tier, quoted from $2,500 after scoping.
• AI features inside your product: an Applied AI Engineer through Engineering Capacity, from $2,900/month.
• A new AI product: MVP Build, at a fixed quote.