Twenty AI answers recommended fifty-seven agency names. Thirty-two appeared only once. Ours did not appear at all.
That was our recorded sample from ChatGPT, Gemini and Google’s AI Overview between 20 and 26 September 2026, looking for agencies that automate business processes for SMEs. Even the most frequent names appeared in only seven answers.
As an agency, Pharen has an obvious interest in that result. For a company choosing a supplier, the more useful question is what to do with a shortlist that changes so much before anyone has looked at the actual job.
What the count establishes
DR Automation and Weyler & Bitzer Systems each appeared seven times in the recorded sample. OptimusFlow Consulting appeared six times. Der Prozessmeister, Digi Match and LABTRIX appeared five times each.
That describes the answers observed. It does not test any provider’s projects, measure customer satisfaction or predict the next answer. The count can help form an initial list. It cannot serve as a quality ranking.
The distinction matters because a repeated name becomes familiar. Familiarity may help you organise research. It does not establish that a team understands a particular integration or can hand over a process effectively.
The providers are selling different work
The websites examined in the original comparison described different offers. Some emphasised individual workflows, others larger packages or monthly support. A published starting price could therefore refer to a different service from the amount on the next website.
Selecting the lowest number might compare a first setup with an ongoing managed process. Without scope, the figures have limited meaning.
Begin with a short note for each candidate: which problem does it describe, what work does it offer, and which claims have a traceable example or reference? Sources from the original provider research are listed below this article. Vendor descriptions are a starting point for investigation, not independent evidence of delivery.
Bring the comparison back to one job
Choose a case to show suitable candidates in the same form. For example, an enquiry that should become a proposal after review. Explain what information arrives, where existing customer data lives and who approves the proposal.
Differences now become useful. One agency may connect your existing software. Another may suggest a new system. Both should explain why their approach fits the workflow and what additional work it creates.
Include an incomplete enquiry. A demonstration supplied with every required field cannot show how the process handles missing information. The article on where automation should stop explains that test.
The less exciting questions govern the running system
Whose account contains the workflow? Who pays for services? Who sees an error and who may fix it? What does the team receive at handoff?
These are harder to fit into a short recommendation than names and product categories. They still need concrete answers in the proposal. Include changes as well. A form will not stay the same forever, and adding an approval can affect several steps.
Turn the names into a few assessable proposals
You do not need fifty-seven introductory meetings. Use the list to identify relevant candidates. Then a clear case shared with a few providers is more useful than another generated ranking.
Compare how they bound the task, which uncertainties they identify and how they describe handoff. When those answers make sense, you can assess effort and cost.
What Pharen brings to a concrete proposal
Pharen Hub provides Agents with defined tasks and permitted sources, alongside Workflows for the review and next step. We can use that workspace when building a process with a team. Whether it fits depends on the required data connections and decisions, which belong in the proposal. It does not follow from appearing in an AI recommendation.
