How to Design an AI Research Workflow

Illustrative design, not a client case study or a description of Paradise Capital’s proprietary system.

An AI research workflow should connect a defined question, appropriate information sources, reviewable findings, and a useful handoff. This guide explains how I would think through that design. It does not describe a completed engagement or claim measured results.

Suppose a team needs to investigate a set of companies for a recurring assignment. Today, someone gathers information from approved sources, compares it with the business’s criteria, and prepares a result for review.

The opportunity might be to reduce repeated research while making the output easier to check and reuse.

The design would need to change around the actual business, its data, and its requirements.

1. Define the research question

Start with the job: which companies should be considered, what information is required, and what would make a result useful?

Capture both inclusion and exclusion criteria. Identify what can be checked directly and what requires interpretation.

For this hypothetical workflow, a record might include the company, the evidence collected, the reasons it appears relevant, and the questions that remain unresolved.

A detailed score is not necessary to begin. It can be more useful to make the underlying evidence visible than to produce a number whose meaning has not been established.

2. Choose appropriate information sources

The system should use information the business is authorized to access and process. Available sources might include licensed data, public company information, and suitable internal records.

Record where information came from and when it was obtained. Decide how missing, conflicting, or outdated material should be handled.

An external page is information to evaluate, not an instruction the system should obey. Design the workflow so retrieved text cannot quietly change permissions or trigger unrelated actions.

That boundary matters especially when research tools can also write to internal systems or contact people.

3. Separate extraction from interpretation

Some steps may be straightforward data handling. Others may involve an AI model organizing text or assessing potential relevance.

Keep those roles understandable. A recorded address is different from a model’s interpretation of strategic fit. A company’s own claim is different from independently verified evidence.

The output should preserve those distinctions rather than flattening them into one confident summary.

Anthropic’s engineering guidance distinguishes predefined workflows from more flexible agent behavior.¹ For this illustration, I would begin with a constrained workflow and add flexibility only where the research task justifies it.

4. Give the reviewer useful control

The reviewer should be able to inspect supporting material, correct a record, reject a result, and identify what further work is needed.

Do not assume that reviewing hundreds of generated results is faster than doing the original task. The interface should help the reviewer focus on the information that determines the decision.

For uncertain cases, the system could mark the missing evidence rather than forcing an answer. For conflicting information, it could present the conflict explicitly.

Before use, test whether people understand those signals and can recover from mistakes.

5. Deliver approved work to the right place

The appropriate destination might be a CRM, an internal application, a shared report, or a structured export. There is no universal requirement for a dashboard or a messaging channel.

Decide when a result is allowed to leave the review stage and what can be written downstream. Prevent duplicate records and record whether the handoff succeeded.

Research and outreach are separate permissions. Generating an account brief should not automatically authorize sending a message to that account.

Use access and approval rules appropriate to the business consequences.

6. Evaluate the entire workflow

Before broader rollout, compare the proposed process with representative assignments from the existing approach.

Review relevant results, missed results where practical, factual corrections, source freshness, and the time required to reach an approved output. Include failures and difficult cases, not only the best demonstration.

Also measure the ongoing cost of information access and operation. Set limits on repeated research or retries, and make errors visible to the responsible person.

The objective is useful completed work, not the largest volume of generated records.

7. Learn from use without hiding uncertainty

Feedback may show that some criteria are unclear or that an information source is less useful than expected. It may reveal a need for a different review process or a simpler solution.

Revise the approach around that evidence. Do not treat a model score as validated just because the system has been running for a while.

The same principle applies to growth claims. Faster research may create capacity, but additional revenue would require its own evidence.

For a comparison of buying and extending a product, see Apollo versus a custom research system. The design question remains the same: decide what matters, make the work inspectable, and choose useful automation.

Fidelis can help investigate whether an approach like this belongs in your business, or whether a simpler change would be more appropriate.

Discuss your research workflow

Related reading

Apollo vs. a Custom Research System: What to Compare

AI Implementation for Small Businesses: From Plan to Working System