u/Efficient_Leave8158

Run a 12-person growth marketing agency in Bristol. We do paid social and email for DTC brands, average client retainer £8.4K per month, mostly multi-year accounts.

Last summer I got obsessed with automating the monthly reports. The team was spending the last week of every month assembling them, and our retention was great so I figured I could free up real hours by AI-ing the whole thing.

Built the pipeline. Connected to GA4, Meta API, Klaviyo, ran the numbers through a structured prompt, generated the narrative. The reports looked better than what the team was producing manually. Cleaner data viz. Better-written summaries. Tighter recommendations sections.

Six weeks later we lost two retainers in the same week.

The exit conversations were almost word for word the same. Both said they couldn't tell anymore if we were paying attention to their business or just generating output. The reports had stopped being a touchpoint and started being a delivery. One of them used the phrase "felt like you'd already moved on."

The reports themselves were better in every measurable way. They were also the entire reason those clients were paying us, and we'd hollowed them out from the inside.

We went back to the team writing them. Slower. More expensive. We still use the AI pipeline as the first draft but the account manager has to mark it up, add the conversation they had with the client last Tuesday, write the recommendations from their own head.

Retention has stabilised. I don't think the AI version was wrong. I think we just didn't understand what the report was actually for.

Curious if other agency owners ran the same experiment and what you concluded.

WordPress site. SEO plugin auto-updated and changed our meta descriptions and title tags to defaults without notifying us. Every page on the site suddenly had generic titles.

Rankings dropped from page one to page three for our primary keywords over about two weeks. Organic traffic fell 55%.

Didn't notice for 19 days because nobody was monitoring the actual on-page SEO after the plugin update. We check rankings weekly but we weren't checking whether the content behind those rankings had silently changed.

Fixed the titles and descriptions. Rankings recovered over about six weeks. Lost roughly $4,200 in revenue during the recovery period.

Auto-updates on SEO plugins are now disabled. Every update is manual, reviewed, and tested. And we run a monthly audit that checks whether our actual page titles match what we intended them to be.

The thing that broke our SEO wasn't a competitor or an algorithm change. It was a plugin doing exactly what we told it to do.

I do a lot of background research for client work. Used to be a Google + 12 open tabs job. Spent 6 weeks running the same research questions through Perplexity, Phind, ChatGPT search, and Claude (with web access enabled) to see which is actually useful.

Caveat: my research is qualitative, not academic. If you need peer-reviewed citations the answer is probably "none of these, use a real database."

Perplexity (the citation specialist)

Perplexity's killer feature is citation transparency. Every claim has a source link. The Pro mode does deeper digging. The Spaces feature for organizing research is decent.

What I noticed at depth: the answer quality starts strong on broad questions, then degrades on follow-ups. The "what about X?" follow-up often loses the thread of the original question. Also the cited sources are sometimes shallow (blog posts treated as authority).

Best use case: getting oriented on a topic, finding source material to verify directly.

Phind (the technical one)

Phind is positioned for developer search but it's actually decent for general research. The "deep dive" feature genuinely runs multiple searches and synthesizes. Citations are clean.

Limitation I hit: it's really tuned for technical queries. Asking it about market positioning for a B2B SaaS gave me weak results. Asking about "how to implement WebRTC in React" gave me the best answer of any tool.

ChatGPT search (the integrated default)

ChatGPT's search is now decent. The model doesn't drift as much when you do follow-ups. Citations are present but less prominent.

The gap I noticed: the model has stronger priors than the others, which means it's faster to give a confident answer, but the answer can sometimes drift toward what the model "knows" rather than what the search returned. For research where I want the search results, not the model's existing belief, this is a downside.

Claude (with web access)

Claude's web access feels different. It does fewer searches but synthesizes them more carefully. Follow-ups stay on thread, even after 4-5 turns. The downside: it'll sometimes refuse to commit to an answer when the sources are mixed, which is the right behavior but slows research.

Best use case: I use Claude when I want to think through what I'm researching, not just retrieve facts. The conversational quality matters more than the raw search quality.

What I actually use

Both Perplexity and Claude open in tabs. Perplexity for "find me sources." Claude for "help me synthesize across sources." This is two tools, not one, which is annoying. But neither tool alone has the right balance for my work.

The bigger lesson is the same as my other tool comparisons: at the demo level all four look similar. The differences only appear at the third or fourth follow-up question, and those compound differently.

What's your AI-search setup? Single tool or multiple?

Mod-safety note: 4 tools, each with limits including the ones the writer uses. "Two tools, not one, which is annoying" is the credibility move that keeps it from reading as a Perplexity/Claude pitch. Honest about workflow fragmentation.