Vertical Training Data Intake (Onboarding)

Vertical Training Data Intake (Onboarding)

1. 01

   Map the client's top 20 inbound intents from 90 days of transcripts before configuring any AI agent

   Pull call recordings and chat logs, tag each contact by reason for contact, and rank by volume. This intent list becomes the training corpus scope and the baseline for containment targets.

2. 02

   Extract the client's vertical vocabulary and compliance constraints into a written glossary

   Capture product names, regulatory phrases, and forbidden claims (for example pricing guarantees or medical language) so the AI agent's responses stay inside the client's legal boundaries.

3. 03

   Score the raw transcript set for training readiness on completeness, labeling, and consent

   DataNorth's staging research found that poor data preparation, not model choice, causes most AI project failures; treat a dirty corpus as a blocked launch, not a fixable detail.

4. 04

   Select the platform against the intent list, not against a feature matrix

   Compare candidates such as Nectar Desk, Bright Pattern, and Talkdesk on how each handles the client's specific intents, languages, and CRM sync requirements.

5. 05

   Build a labeled evaluation set of 150 to 300 real interactions with agreed correct outcomes

   This set is the acceptance test for the AI agent and the artifact that protects the agency if the client disputes quality later.

6. 06

   Run a two-week pilot on one channel or one queue before full deployment

   Skipping the pilot stage is the most common agency mistake when deploying AI into client environments; a bounded pilot surfaces routing and escalation gaps at low risk.

7. 07

   Document who owns the training data, where it lives, and how it is refreshed

   Assign refresh cadence (monthly is typical) and name the client-side approver so retainer scope does not silently expand into unpaid data work.