HomeAI Agency AcademyLesson 06
Module 02 · Lesson 06

Research conversations, not just competitors

Turn customer language and workflow evidence into a useful problem inventory.

Last updated August 5, 202615–25 minutesFree AI agent course
What you will learn

Make a clear, safer operating decision.

You will be able to collect evidence from operators and buyers, separate a stated preference from an observed problem, and use it to improve an offer.

Why this matters

Good agent work is useful before it is impressive.

Competitor sites show what companies say they sell. They rarely show where a team hesitates, what data is missing, or which exception makes a workflow painful. Real language from calls, reviews, tickets, and demos is more useful.

Field note 06

Make the relationship visible.

AI AGENTS · FIELD NOTE 06Conversations → patterns → tested offerTHE EVIDENCE01Call notes02Tickets + reviews03Repeated pattern04Offer testOriginal visual framework for Research conversations, not just competitors.AI AGENTS · FIELD NOTE 06Conversations → patterns → tested offer01Call notes02Tickets +reviews03Repeated pattern04Offer test
Use this framework to make research conversations, not just competitors visible before you build.
Core concepts

The language that keeps the work clear.

Problem evidenceA concrete example of delay, rework, loss, or customer friction—not a vague opinion about AI.
Verbatim languageWords a buyer or operator uses to describe the problem, outcome, fear, or current workaround.
PatternA theme that appears across several independent conversations or records.
HypothesisA testable explanation of how a bounded workflow could improve a named problem.
The practical method

Work through the decision in order.

Collect raw examples

Ask for a recent case, then trace what happened, who acted, and what made it hard.

Capture the language

Write short phrases exactly as said. Do not translate everything into product jargon.

Group evidence carefully

Separate pain, desired outcome, current workaround, risk, and buying constraint.

Test a small statement

Turn the strongest pattern into an offer sentence and ask whether it matches the buyer’s actual situation.

Worked example

A realistic, bounded implementation.

A property-management team says it needs ‘an AI assistant for tenants.’ In six conversations, the repeated issue is more specific: maintenance requests arrive incomplete, so vendors cannot be dispatched quickly.

The useful phrases are ‘we spend all morning chasing photos’ and ‘we cannot send a contractor without the unit and access detail.’

The offer becomes a guided intake and routing workflow that requests missing details, records consented contact information, and flags urgent cases for a person. It is based on observed work, not a generic chatbot category.

Build it in practice

Use this copyable working template.

Adapt it to the client’s evidence, policy, people, and tools. Do not treat placeholders as approved instructions.

Evidence source: [call, ticket, review, observation]. Exact phrase: ‘[quote]’. Observed situation: [case]. Current workaround: [workaround]. Pattern count: [number]. Hypothesis: [tested change].
Spacebrain implementation

Put the operating system around the agent.

Store discovery notes, call summaries, objections, and lifecycle data on the contact record so research can improve the offer and delivery system over time.

Practice

Before you move on

  • Collect five pieces of raw workflow evidence.
  • Group them without forcing them into a solution.
  • Write one offer sentence using the customer’s language.
  • At least one operator supplied a recent real example.
  • The offer language has not been invented from competitor headlines.
  • Patterns are separated from one-off complaints.
  • The hypothesis can be disproved or refined.

Build the operating layer around your agent.

Use the free Spacebrain workspace to keep contact context, handoffs, tasks, automation, and reporting together.

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