Enriching Contacts, Companies & Deals

KarmaFlow can auto-fill custom fields on a record from your own data plus optional web search — no third-party enrichment account required. This article walks through the AI-research field workflow end to end.

How AI-research fields work

An AI-research field is a custom attribute you define once (per entity type) and the platform fills automatically on demand. The pipeline:

1. Pull the record's own card (name, email, company, tags, recent notes/activities).
2. Retrieve matching context from the per-tenant vector store (your calls, chats, emails, SMS, notes, activities), optionally from web search, optionally from contact memories.
3. Send the prompt plus the retrieved context to Gemini.
4. Persist the answer onto the record's customFields, along with the list of sources the model actually used.

The sources are stored alongside the value so every enrichment is auditable — anyone reading the field can see exactly what context produced it.

Defining an AI-research field

1. Open CRM → Setup → Custom Fields (/settings/custom-fields).
2. Pick the entity tab — Contacts, Leads, Companies, or Deals.
3. Click + New field.
4. Set the Friendly name (e.g. "Industry"). The slug auto- fills from the name; override only if you need a specific dbName.
5. Change Field kind to AI-research (auto-filled). An AI research configuration section appears below.
6. Fill in:
   • Prompt — the question for the LLM. Be specific. Examples:
     • "What industry is this company in? Reply with one short phrase like 'B2B SaaS' or 'Healthcare provider'."
     • "Summarise this contact's role at their company in one sentence."
     • "List the top three product features they've discussed, comma-separated."
   • System prompt (optional) — tone / format instructions that apply to every fill. Example: "You are a sales-research assistant. Cite your sources. If you don't have enough information, reply 'unknown' instead of guessing."
   • Sources — which retrieval channels to use:
     • Vector store (own data) — notes, activities, calls, chats, emails, SMS attached to the record.
     • Web search — public web results, summarised by the LLM.
     • Contact memories — persistent memory entries written by AI agents over time.
   • Refresh interval (days) — 0 for on-demand only; anything higher schedules an automatic refresh.
   • Model — defaults to gemini-2.0-flash-001. Leave this alone unless your workspace has a specific model preference.
7. Click Save.

The field now appears in the Custom Fields card on every record of that entity type, with an AI chip next to its label and an Enrich button on the right.

Enriching a record

Open any contact, company, or deal. In the Custom Fields card on the right column:

• The first time you see an AI-research field on a record, the value box says "Not yet filled" and the button reads Enrich.
• Click Enrich. The button spins for a few seconds while the LLM runs.
• The value appears in the box. A small N src · view link appears next to the button — click it to open a popover that shows:
  • When the value was generated and which model ran.
  • Each source the model pulled in, with its metadata (call ID, channel, timestamp, etc.).
• Need to re-run? The button is now labelled Refresh. Click it any time to regenerate the value with the same prompt and sources. The new sources replace the old in the popover.

Editing AI-research fields after the fact

AI-research fields are read-only in the Custom Fields card — they only update via the Refresh button. If you want to override a value manually, the cleanest path is to:

1. Edit the field definition in Settings → Custom Fields and change the Field kind back to Manual.
2. Save. The existing value stays on every record.
3. Edit the value inline on each detail page as needed.
4. Optionally flip the field back to AI-research later — the manually-entered values remain unless someone clicks Refresh.

Bulk enrichment

The bulk-edit dialog on the spreadsheet view doesn't yet drive the AI pipeline — for now, enrichment happens one record at a time via the Enrich button. The scheduled refresh (refreshIntervalDays) handles bulk catch-up over time without manual clicking.

Practical recipes

A few prompts that work well in production:

• Company → Industry: "What industry is this company in? Reply with one short phrase." Sources: web + vector store.
• Company → Recent news: "In one sentence, what's the most recent notable news about this company from the last 90 days? If none, reply 'no recent news'." Sources: web only.
• Contact → Role summary: "Summarise this contact's role and seniority in one sentence based on conversations on file." Sources: vector store + memory.
• Deal → Risk flag: "Based on the call transcripts and notes attached to this deal, list any concerns the buyer has expressed about pricing, timing, or alternatives. Reply 'none detected' if there's no signal." Sources: vector store.
• Contact → Compliance flag: "Has this contact ever asked about HIPAA, SOC 2, GDPR, or another compliance topic? Reply with the specific term they mentioned, or 'no'." Sources: vector store.

When AI-research won't help (and what to use instead)

• Real-time data (current price, today's weather, this morning's news) — refresh interval applies; the value reflects the last time the field was filled.
• Cross-record aggregates ("how many deals does this contact have open?") — use a CRM dashboard widget instead.
• Public B2B data (LinkedIn role, company headcount, funding round) — the LLM can fetch some of this via web search, though a dedicated data provider is usually denser.
• Hard facts you already capture (email, phone) — leave those as manual fields. AI-research is for the judgement calls that humans usually make from context.

Where it shows up next

When you bring an AI-research field into a saved view as a column, list pages display the AI chip next to its column header so the user knows the value was auto-filled. The value still shows the most recent fill — no automatic refresh on page load to keep cost predictable.