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