How to automate competitor research with AI

Automate competitor research by defining a recurring question, collecting a bounded set of sources, extracting comparable facts, and asking AI to draft a source-linked brief. Review the claims that affect a decision before sharing the result. Keep the previous collection so the next run can explain what changed.

Imagine a product marketer at a US software company preparing a weekly pricing update. “Research the market” is too open to evaluate. “Tell sales what changed in these five competitors’ public US plans since last Friday” creates a workable job.

Start with a research contract

Write down the companies, geography, sources, comparison fields, time window, and intended reader. Specify the decision the brief supports. A pricing update for sales and a market-entry study for the CEO need different evidence.

• Scope: five named competitors and their public US pricing and product pages.
• Fields: plan, currency, billing period, seat minimum, included usage, overage terms, and source URL.
• Output: a comparison table, material changes, and questions that need checking.
• Limits: no inferred private revenue, no invented contract prices, and no claim that an unread page is unchanged.

Separate collection from interpretation

A collection tool should return the source, retrieval time, relevant content, and any access failure. The analysis step should use that material. Preserve an authorized copy or a reference sufficient to recheck the claim; a link alone may point to different content next week.

There are existing tools for this collection layer. Apify’s market research walkthrough, for example, combines website and pricing collection with an AI report and recurring runs. Its tutorial demonstrates a vendor workflow; it does not establish that the resulting recommendations are accurate for your business.

For a first version, manually collect the five pages and test the analysis before adding a schedule. This reveals whether the brief answers a useful question without mixing collection failures into the assessment.

Compare like with like

An illustrative offer at $20 per user per month with annual billing is not equivalent to a $25 monthly plan with no commitment. Neither can be compared fairly without checking seat minimums and usage limits. Preserve the original terms alongside any normalized figure.

Give the drafting step a narrow instruction:

Using only the supplied sources, compare these plans. Attach a source and retrieval date to each factual claim. Separate observed changes, interpretations, and unknowns. If a page is unavailable or a term is missing, say so. End with the three checks most likely to change our decision.

This is a starting prompt, not an enforcement mechanism. Test whether the actual output follows it. Open the cited pages, check the quoted terms, and verify that an interpretation has not been presented as a company statement.

Make the next brief build on the last

Save the source set, extraction rules, comparison table, reviewer corrections, and final brief together. On the next run, compare dated observations before asking AI to explain changes. If a previously available source fails, mark the comparison incomplete.

Track factual corrections and review effort, not just pages collected or words generated. If the report repeatedly misses a seat minimum, improve the extraction field and the check. Do not rely on the next conversation remembering the correction.

Amphra’s current website example turns supplied competitor notes into a text brief. Live web collection and scheduled research are not available in that preview. The example points toward the same goal: work that keeps useful context and becomes easier to repeat. Read why repeated agent tasks can drift for a way to evaluate that reliability.