AI Prospect Research: A Worked Example from Company Name to Seller Brief

AI prospect research is most useful when it turns a messy research process into a repeatable, inspectable brief. The quality test is not whether the output sounds intelligent. It is whether a seller can see what is known, what is inferred and what still needs checking.

This article shows that process using a fictional company.

Worked-example note: Northbank Systems Ltd is a fictional composite created to demonstrate the research method. The facts below are invented for the example and are not claims about a real company.

The starting input

Assume the sales team provides only:

Company: Northbank Systems Ltd
Offer: outsourced revenue-operations support
Target profile: UK B2B software companies with 75 to 500 employees and an established sales team

The research task is not “write a personalised email”.

It is:

Is this account worth seller attention, who appears relevant, and what evidence should shape the first conversation?

Stage 1: establish identity

The system first needs to make sure it is researching the right company.

Suppose it finds:

• northbanksystems.example as the company website
• UK headquarters
• B2B workflow software product
• approximately 180 employees across public company profiles

Output

Observed facts

• UK B2B software company
• sells workflow software to enterprise operations teams
• public headcount sources place it roughly inside the target size range

Inference

• appears to match the basic market profile

Unknown

• exact current employee count
• current revenue-operations structure

Already, the distinction matters. “180 employees” should not become a false precision claim if sources disagree.

Stage 2: test fit before deep research

The account passes the cheap fit checks:

• correct geography
• correct broad sector
• plausible company size
• apparent B2B sales motion

That earns a deeper research pass.

If Northbank were a 12-person consumer app, the workflow should stop here.

AI saves time partly by knowing when not to keep researching.

Stage 3: find relevant people

Suppose public profiles show:

• Chief Revenue Officer, joined five months ago
• Head of Revenue Operations, in role for three years
• Sales Operations Manager, joined two months ago

A weak system might return the CRO simply because they are most senior.

A better output maps likely roles:

Economic influence: CRO
Functional owner: Head of Revenue Operations
Operational context: Sales Operations Manager

The seller can then decide who belongs in the conversation.

Stage 4: look for recent change

The system finds three pieces of public evidence:

1. company announcement: a new CRO joined five months ago
2. careers page: six open sales roles across UK and Germany
3. one RevOps job description mentions “standardising forecasting and pipeline processes across regions”

This is where AI systems can become dangerous.

A fluent but weak conclusion would be:

“Northbank is actively looking for revenue-operations support as it expands internationally.”

The evidence does not support that.

A better interpretation is:

Supported inference: Northbank appears to be standardising parts of its revenue operation while expanding sales capacity across two markets.

And then:

Unknown: whether external RevOps support, new software or internal hiring is part of that programme.

The unknown is commercially useful. It becomes a question for the seller.

Stage 5: verify the material claims

Before the brief is produced, the workflow checks the strongest claims.

New CRO

Prefer the company’s own leadership announcement or the executive’s public profile.

Sales hiring

Check the careers page directly rather than relying only on a job aggregator.

Forecasting standardisation

Retain the job-description URL because this phrase is central to the commercial interpretation.

If that vacancy has disappeared and no cached or corroborating source remains, confidence in the claim should fall.

Stage 6: produce the seller brief

The final output should be much shorter than the process above.

Northbank Systems Ltd: example brief

Who they are
UK B2B workflow-software company serving enterprise operations teams. Public sources indicate roughly 180 employees.

Why the account fits
Sector, geography and company size match the target profile. The company appears to operate an established B2B sales organisation.

What changed
A new CRO joined five months ago. Northbank is recruiting sales roles in the UK and Germany. A recent RevOps job description references standardising forecasting and pipeline processes across regions.

People likely to matter
Head of Revenue Operations appears the closest functional owner. CRO may have economic influence. A recently appointed Sales Operations Manager may have current implementation context.

Commercial hypothesis
Northbank may be formalising revenue processes as the sales organisation expands across regions.

What we do not know
Whether that work is being handled entirely in-house, whether tooling is changing, or whether external support is being considered.

Useful first question
“As the sales team grows across the UK and Germany, how are you approaching the standardisation work around forecasting and pipeline process?”

Evidence
Links to the company announcement, careers page, relevant job description and current leadership profiles.

That is a useful research output because it helps the seller enter a conversation without pretending to know the buyer’s internal plan.

What AI did in this example

AI helped with four kinds of work.

Retrieval

Finding relevant public sources.

Extraction

Pulling out roles, dates, locations and statements.

Classification

Separating fit evidence, people evidence and recent change.

Synthesis

Turning multiple facts into a concise brief.

None of those tasks require the model to invent a purchase decision.

Where human or explicit judgment rules still matter

What counts as fit

The system needs the seller’s market definition.

Which changes matter

Hiring is relevant to some offers and irrelevant to others.

How strong an inference can be

The workflow needs rules about evidence quality, recency and corroboration.

Whether outreach is appropriate

Public evidence can inform the decision, but it cannot reveal every internal circumstance.

What happens if the evidence is weak?

Change the example.

Suppose the only evidence found is:

• one sales vacancy
• a nine-month-old podcast mentioning growth
• a generic technology flag from an aggregator

The correct output is not a thinner version of the same confident brief.

It should say:

Fit: plausible
Recent change: insufficient evidence
People: partially identified
Why now: unresolved
Action: monitor or research manually if account value justifies it

That is what “fail closed” looks like in a research system.

AI prospect research vs AI personalisation

This workflow happens before email generation.

The research determines whether there is a worthwhile account and what the seller actually knows.

Only then should a message be written.

That order prevents AI from becoming a machine for creating beautifully worded reasons to contact accounts that were never properly qualified.

What to ask an AI research vendor

Use this worked example as a test.

Ask the vendor:

• Can I see the sources behind the brief?
• Does the system identify unknowns?
• What happens when sources conflict?
• Can I define my own fit and exclusion rules?
• Can a weak-evidence account finish with no “why now”?
• Are people mapped by likely relevance rather than seniority alone?
• Can I inspect why a conclusion was made?

If every input produces a polished positive answer, the system may be optimised for completion rather than truth.

For the principles behind this approach, see the BuyerState research methodology.