The problem this solves
The gap between an AI idea and a working AI process is where most initiatives die. Someone pilots a prompt in a chat window, it works once, and then it turns out nobody specified where the inputs come from, what happens with low-confidence outputs, who reviews anything before it reaches a customer, or how the process behaves when the model is wrong. Unspecified workflows do not survive contact with real volume.
How we work
We decompose the use case into a concrete workflow: triggers, inputs and their sources, processing steps, outputs and their destinations, plus the failure paths that separate a production process from a demo: missing input data, low-confidence output, volume spikes, and model errors.
The AI layer gets specified in detail: what context and data the model needs to produce useful output, prompt and instruction design, and model and tooling selection, using HubSpot Breeze features where they cover the job and external or custom tooling where they do not. Human-in-the-loop points are placed deliberately, with clear accept and reject criteria.
The design closes with measurement: quality checks, sampling reviews, and the metrics that show whether the workflow earns its keep once the novelty wears off.
Deliverables
- End-to-end workflow specification including failure paths
- Data input and context requirements
- Prompt and instruction design
- Tooling and model selection with rationale
- Human review point design with accept and reject criteria
- Quality measurement plan
What buyers ask before scoping.
Is this only for HubSpot Breeze use cases?
No. Where Breeze covers the workflow we design on it, because capability already inside your HubSpot license is usually the cheapest path to production. Where it does not, we design on external tooling or a custom build. The design method is identical either way; only the execution layer changes.
What do we need before this module starts?
One or a few prioritized use cases with a stated value hypothesis. If you have not prioritized yet, the AI Use Case Prioritization module comes first, because detailed design of an unvalidated use case is expensive guessing. Rough priorities are enough; the design will sharpen them.
How do you decide where humans stay in the loop?
By consequence of error. Customer-facing and irreversible outputs get review gates; internal drafts and suggestions can run lighter. The design states the criteria explicitly, so the review load is a deliberate trade-off you can adjust as trust builds, not an accident of whoever built the workflow.
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