AI contact research turns public material into a bounded contact brief. The brief should separate what the person has said, what you infer from it, and what you still need to ask. AI can sort through data; your expertise and judgment decide what is meaningful.1 Use the model to surface useful signals and gaps, then decide whether each signal belongs in an outbound message.
Set the boundary
Keep the job small enough to verify. Build a source-backed view of one contact and leave any private priority unconfirmed until you can test it.
Secondary research means finding, aggregating, and citing primary content that other people have already created.2 Large language models operate on existing content and do not generate new facts.3 Primary research is intended to generate new facts through surveys, scientific experiments, data analysis, and interviews.4
Use the model to organize public material, compare signals, and prepare questions. Use the conversation to test what the public material cannot establish.
| Stage | What you are trying to learn | Example question |
|---|---|---|
| Scope | whether this is the right contact to research | "Who should I be engaging?"5 |
| Collect | what the contact has said, published, or done publicly | What material can I inspect directly? |
| Map | what the contact appears to own and where the role connects to your message | What responsibilities are visible in the material? |
| Test | which signals may point to a current priority | Which observation would be worth checking in conversation? |
| Verify | which statements are supported and which remain hypotheses | Can I show the passage behind this conclusion? |
Build the source pack
Give the model material it can inspect and tell it to distinguish direct evidence from inference. A short source pack produces a better working brief than a broad request to "research this person."
Some AI line-writing tools research contacts through websites, LinkedIn, and sometimes online history before writing up their findings.6 Use public professional material you can defend in a conversation, such as the contact's website, LinkedIn content, authored work, interviews, or public activity. Leave irrelevant personal material out of the pack.
Ask the model to do this:
"Read only the supplied material. Extract each direct statement about the contact. For every statement, record the source, what it shows, and whether it is explicit, inferred, or unknown. Do not fill gaps with assumptions."
First, ask the model to show you what it found instead of asking for a personalized message. This lets you see whether the material supports a role hypothesis at all.
Map the contact's role
Role mapping turns scattered public material into a working hypothesis about what the contact handles. Keep the wording provisional until a source states the responsibility directly.
Ask for four outputs: the apparent role, responsibilities visible in the material, decisions or projects the person may influence, and the connection between those areas and your message. The useful result is the explanation of how the challenge connects to the lead's role.7
Use this prompt:
"Based only on these sources, describe what this person appears to own, the responsibilities visible in their work, and the decisions they may influence. Quote or point to the supporting passage for each statement. Mark every inference and list what cannot be determined."
If the material suggests several people or departments are involved, ask the model to map them separately. AI-powered tools can automate insights retrieval to identify key players and involved departments.8 Treat that output as a map for further checking. Check each listed person before putting them in the first message.
Move on when you can explain why this contact could care about the problem in terms of their work. If you can only describe the industry or company, keep researching the role.
Find priorities worth testing
A public signal can remain a hypothesis before the conversation, as long as it gives you a specific question to ask.
AI can surface a prospect's recent activities, interests, and industry trends so you can tailor the approach more precisely.9 Ask the model to rank each signal by recency, directness, connection to the role, and usefulness as a conversation starter. These categories keep a topical detail from becoming an unsupported claim.
For each signal, ask:
- Identify what changed or appeared in the material.
- Note which part of the contact's work could be affected.
- Ask what would show whether it matters now.
- Ask what would make the signal irrelevant.
Use the output to review contact selection too. Sales teams can use AI to help ensure that representatives call the right people.10 If the role connection remains weak after this pass, remove the contact from the message queue or find a better route into the account.
Use a research dialogue
A single prompt encourages a polished answer. A sequence of narrow prompts makes the model show its reasoning and gives you places to challenge it.
Perplexity generates a series of links with its answers and supports dialogue that can make answers successively more useful.11, 12 It maps what you write to similar meanings alongside the literal words.13 A research mode that returns sources helps you move from a broad role question to a precise check on one statement.
Run the dialogue in this order:
- Extract statements about the contact source by source.
- Build a role and responsibility map, separating explicit and inferred points.
- Separate observations connected to the role from those that are merely topical.
- Challenge the conclusions and list alternative readings.
- Generate questions that would confirm or disprove the remaining hypotheses.
Keep the original source pack beside the output. Use the model's summary to navigate, then inspect the source before using a detail in outreach.
Verify before writing
Verification connects research and messaging. Every usable observation should pass a source check and a relevance check.
Understanding where AI hallucinates and how to design prompts can improve its use in research.14 Before keeping an observation, check that the source refers to the same person, says what the model claims, and gives you a reasonable connection to the person's work. Record the passage, the interpretation you drew from it, and the question that would test it.
Assess the quality of the information you receive and be honest about how you use AI tools.15 If a source is weak, stale, ambiguous, or impossible to inspect, downgrade the observation to a hypothesis or remove it. If the model cannot show where a conclusion came from, do not let that conclusion shape the message.
The final brief should give you a short contact description, a role and responsibility hypothesis, one or two priority signals, the source behind each signal, and the questions still open. Stop the research when another pass produces more wording than new understanding.
What not to do
These shortcuts turn a useful research pass into an unsupported message.
- Do not use AI as a primary research method. AI is not a good tool for primary research.16
- Do not skip checking information quality or being honest about how you use AI tools.15
- Do not judge a research tool by speed alone. AI tools work well when you understand what good research output looks like.17
- Do not treat a polished contact summary as sufficient support for a claim when the underlying source is unavailable.6