AI Automation Pilot for Small Businesses

Neaptide helps small teams choose one support or internal-operations task and test whether AI improves it in practice. The pilot includes a defined workflow, a human review point and a before-and-after measurement plan. Its result is a decision to continue, revise or stop, supported by recorded work.

Choose a task with a visible finish line

We begin with a process your team already performs, its actual inputs and an owner who can judge the output. Bring redacted work items and the existing acceptance rules. We use those records to decide what the pilot should cover before selecting tools.

If the process is rare, the source data is unreliable or nobody can define an acceptable output, begin with process or data work. Sometimes a form, a rule or an existing software feature will solve the problem more simply than a language model.

Record the manual baseline

Select representative work items before building. Record the time to complete the task, review and rework separately, and keep a pass/fail decision against the same quality rules. Count unsuccessful items too. Measuring only the fastest successful demonstrations would hide the burden your team faces in normal use.

• Blank pilot charter and measurement sheet · TXT

Build with an explicit review and fallback path

We agree the systems involved and validate the available access before choosing an integration. Our AI work includes agents and workflow tools; the exact connector and permissions are chosen for the task. The process owner defines which outputs require human approval before the pilot starts.

Specify what happens when information is missing, the tool fails or the model proposes an action outside the scope. The manual process must remain usable while the pilot is being evaluated. A successful demonstration does not grant the system permission to change records or contact customers automatically.

Measure accepted work, not generated output

Compare like-for-like items over an agreed period. Where practical, use a held-out set the build was not tuned on and have the same acceptance rules applied to manual and assisted work. Log differences in case complexity or reviewer experience instead of attributing every change to AI.

A useful calculation is total human minutes per accepted item, with failed-item effort retained in the total. Pair it with the acceptance rate and operating cost. A system that produces more drafts but requires more correction may not improve the workflow. A pilot can also be inconclusive if the sample is too small or inputs changed during the test.

Decide what happens after the pilot

• Continue when the agreed quality and effort thresholds are met and an owner can operate the workflow.
• Revise when a specific, testable issue explains the shortfall and the next experiment is worth its cost.
• Stop when the process is unsuitable, failure handling is too expensive or the benefit is not demonstrated.

The handover records the workflow, configuration and agreed implementation assets, the measurements, known failure cases and the operating instructions. Third-party subscriptions and any production hardening are identified separately. Training and ongoing support are scoped for the people who will actually use and maintain the result.

Timing and price follow the scope

Our general AI process describes an initial assessment of one to two weeks and a pilot of roughly one to two months. Those are planning ranges, not a deadline for every integration. Data access, representative examples and review availability can change the schedule. We quote the agreed assessment and build scope before work starts; we do not promise a fixed ROI or a percentage of time saved.

• Our AI consulting process
• AI implementation cost categories
• Agents in business workflows

Start with one recurring problem

Tell us who performs the task, how often it happens and what makes an output acceptable. A few redacted examples and the name of the process owner help us define a useful first experiment. If the work is mainly answering questions from internal documents, see the dedicated RAG scope.

• Custom RAG for internal knowledge