AI CRM data entry: standardising notes with AI

Short answer

Standardising CRM notes with AI takes more than telling it to "write a summary". First you write the note schema: which fields, a one-sentence definition of each, allowed values and the rule "if the information is not there, leave it empty". Then AI fills the same fields in the same form from every conversation, writes a one-sentence summary and turns relative dates into concrete ones. In the first weeks a person checks sample notes and tightens the schema. The result: instead of notes in each manager's own style, notes you can search and count in a report.

Why notes are not standard

One manager writes "interested, call later", another three paragraphs, a third nothing at all. The same information — budget, timing, need — sits in a different place and in different words on every record. As a result, a simple list like "customers with a budget over 20,000" cannot be pulled, and a new manager cannot make sense of old notes.

AI can solve this, but only when it is told exactly what to write. Without a schema, AI writes in its own style just as a manager does — only longer.

Step 1: the field schema

A schema has 5–10 fields. For each field, write four things:

1. 1Name"Budget", "Need", "Timing", "Area" — short and the same for everyone.
2. 2DefinitionOne sentence: "The maximum amount the customer stated themselves, in manats."
3. 3FormatNumber, date, choice from a list or short text.
4. 4Empty rule"If the customer did not state an amount, leave it empty — do not estimate."

A filled-in schema: real estate

The schema below is illustrative and can be a starting point for an agency:

• Transaction: choice from a list — buy, rent. Empty if the customer did not say.
• Rooms: a number. If "2–3 rooms" was written, the minimum plus a note.
• Area: choice from a list, one or more values.
• Budget: a number in manats, the upper limit the customer stated. Never estimated.
• Payment method: choice from a list — cash, mortgage, unknown.
• Viewing date: a date. Only if the customer named a specific day.

With six fields, the agency can pull "people looking for a two-bedroom flat with a mortgage who want to view this week" with a single filter.

Step 2: a one-sentence summary template

Alongside the fields, a one-sentence summary per conversation is useful, because it is the first thing a manager reads when opening the record. Keep the template fixed: who — what they want — on what terms — what the next step is. For example: "Family with two children looking for a three-bedroom flat in Khatai, budget not stated, wants to view on Saturday." With a fixed template, summaries can be read side by side and compared.

Step 3: dates and numbers

Dates in conversations are relative: "tomorrow", "next week", "after payday". The record needs a concrete date. AI should convert a relative phrase against the conversation's date: "in 3 days" written on 6 October is 9 October. Vague phrases — "after payday", "in a bit" — are not converted; they are kept as text. Write a rule for numbers too: currency, how "thousand" is converted, how a range ("15–20k") is written.

Step 4: what stays free text

Not everything belongs in a field. A customer's particular wish, what they say about their family situation, a comparison with a competitor — these stay as a short free-text note. The rule: information that will be counted or filtered in a report goes in a field; information that will only be read goes in the note. Sensitive information — health, financial difficulty — is written only if the work needs it.

Step 5: human review

For the first two weeks, 10–15 notes a day are compared with their conversations. Three questions: was a value written that is not in the conversation, was a value missed that is, does the summary reflect what the customer said? If errors are systematic — AI reading "maybe" as agreement, say — the field's definition is tightened. After that, the review moves to a weekly sample.

Schema versions

The schema will change: a new product, a new question, a field retired. Record every change with its date and know which version each old note was filled with. Otherwise the meaning of "budget" changes mid-month and a report adds two different things in one column. Marking a field "deprecated" is safer than deleting it.

An illustrative example

This is an illustrative example. In a furniture showroom, managers' notes were completely inconsistent. The team wrote a six-field schema: product type, room, budget, delivery date, colour preference, next step. AI started filling these fields from every WhatsApp conversation and left empty whatever it could not find.

The first week's review showed AI writing "we're moving next month" as the delivery date. The definition gained "only the delivery day the customer asked for". A month later, "customers wanting delivery this month" could be pulled with a single filter.

Common mistakes

• A "write a summary" instruction with no schema.
• No empty rule — guesses get written as facts.
• A 20–30 field schema: both AI and people get confused.
• Keeping relative dates as they are: "tomorrow" is meaningless a week later.
• Stopping the review after the first day.

Limitations

AI structures what was written in the conversation; it does not know what was not said: a term agreed by phone reaches the note only if there is a call recording or a manager's note. Mixed languages, irony and short replies can be misread. A standardised note is a checked note, not an automatically correct one.

Notes in Vexvon

In Vexvon, a company defines its own fields to extract from conversations, each with a name and description — name, email, meeting date, budget, interest or any other field. AI takes a value only if it appears literally in the text and leaves it empty otherwise; phrases such as "tomorrow" or "in 3 days" are converted to a concrete date relative to today. Optionally, a one-sentence summary is added to the lead notification. Managers' manual notes and every action are kept in the same customer timeline. More on Vexvon CRM.

Next step

Open 20 records from last week and write down which information is recorded differently on each. That list is the first version of your schema. How to measure note quality afterwards is covered in CRM data quality. More articles are in the AI CRM section, and we can build your schema together during a demo.