Vertical Training Data Intake (Onboarding)

Vertical Training Data Intake (Onboarding)

1. 01

   Pull 90 days of real transcripts before writing a single intent

   Request call recordings, chat logs, and SMS threads from the client's existing queue. If the client is launching a new line, substitute 30 days of adjacent-channel history so the intent map reflects how their customers actually phrase problems.

2. 02

   Tag the top 20 contact reasons and their share of volume

   Build a frequency table so the automation roadmap targets the reasons that carry the most minutes, not the reasons the client finds most interesting. Anything under 2% of volume stays with human agents in phase one.

3. 03

   Extract the client's own vocabulary for products, tiers, and error states

   Collect the exact nouns customers use, including misspellings and regional variants. Generic industry phrasing produces misroutes that surface as containment failures within the first two weeks.

4. 04

   Map every regulated or contractual phrase that must never be paraphrased

   Flag disclosures, pricing commitments, and cancellation language. These get locked strings in the knowledge base rather than generative responses, which keeps the client's compliance team comfortable signing off.

5. 05

   Confirm data residency and retention terms in writing before ingestion

   Server-side memory and off-device processing now expand the scope of client data that AI systems hold, so get the client's security reviewer to approve storage location, retention window, and deletion path in the same document.

6. 06

   Score the training set for coverage gaps against the intent map

   Any intent with fewer than 50 representative examples gets a synthetic augmentation pass or a human-only routing rule. Thin training data is the most common reason a vertical deployment stalls at low containment.

7. 07

   Get the client to sign the intent map as the scope baseline

   A signed map converts future scope creep into a change order. It also gives the agency a defensible answer when a client asks why a specific edge case was not automated in month one.