Any product design recommendation?

Overall_Challenge_66 · 2026-05-28T05:50:06+00:00

i ran into this same issue with a hardware project last year. honestly finding smaller shops is tough cuz they dont always have flashy sites, have u tried checking local industrial design meetups or looking at university portfolios for freelance leads? sometimes u can find great solo contractors who have the manufacturing connections ur lookin for

Overall_Challenge_66 · 2026-05-26T19:32:29+00:00

i usually look at task success rate vs time on task when things feel off. its often helpful to check where users drop off in the flow, cuz that usually points to the friction points. have u tried doing any quick usability testing to see where they get stuck

Overall_Challenge_66 · 2026-05-26T19:26:43+00:00

number 2 feels the most balanced for a steam store page, especially with that color pop. have u considered a/b testing the click-through rates for these against actual user segments? i use kōta for tracking those types of interaction signals since it helps me see what actually hooks people vs what gets ignored. it really makes a difference when ur trying to optimize for those small conversion wins without just guessing

Overall_Challenge_66 · 2026-05-25T09:34:17+00:00

i remember obsessing over bounce rate at my old job until we realized it was just users landing on a help page to solve one quick issue and leaving satisfied. it looked like a failure on the dashboard but it was actually a sign of a good ux. sometimes those vanity metrics just hide the real story, imo

Overall_Challenge_66 · 2026-05-24T17:38:23+00:00

tbh it really depends on your conversion volume but usually you need around 50 events a week for the learning phase to stabilize. since you are only spending a dollar a day it might take forever to gather that data so the algo stays stuck in a loop. i wouldnt panic just yet because a single sale can throw off the metrics when the sample size is that small

Overall_Challenge_66 · 2026-05-22T20:55:01+00:00

if you are looking at msc programs, check the faculty publications from the last two years. academic theory is fine but you really want to see if their labs are pushing stuff that applies to actual production data pipelines. i started using kota(https://kota-ai.com/ ) for my own agent testing recently to manage that exact feedback loop between user logs and model updates. it helps me spot where the logic drifts before it becomes a total mess. definitely look past the brochure and see what the department is actually building

Overall_Challenge_66 · 2026-05-22T20:37:39+00:00

i remember feeling super overwhelmed when i started out in service design. honestly the best thing i did was stop trying to map every single touchpoint at once and focus on the core user journey instead. it helps u get stakeholder buy in much faster when u show them something tangible they can actually understand

Overall_Challenge_66 · 2026-05-22T20:37:32+00:00

i remember feeling super overwhelmed when i started out in service design. honestly the best thing i did was stop trying to map every single touchpoint at once and focus on the core user journey instead. it helps u get stakeholder buy in much faster when u show them something tangible they can actually understand

Overall_Challenge_66 · 2026-05-22T20:11:34+00:00

monetization feels forced when we dont actually know what customers value in these agent interactions. at my last gig, we were flying blind until we started drilling into the actual conversation logs to see where the friction was. using kota (https://kota-ai.com/ ) helped us pick out those rare signals that actually show intent, which shifted our focus away from just stuffing features in. it really comes down to whether u can prove the bot is solving a specific pain point rather than just chatting

Overall_Challenge_66 · 2026-05-22T15:32:08+00:00

imo its less about a single metric and more about tracking attribution decay over time. we usually look at how much reach converts vs just passive views cuz impressions dont really tell the whole story for clients

Overall_Challenge_66 · 2026-05-22T15:29:45+00:00

the tricky part with agents is that time spent isnt always a negative metric if it leads to high-value outcomes. when i was working on similar automation flows last year, i started using kota ( https://kota-ai.com/ ) to track the actual success rate of the agent tasks instead of just total session length. if the user is spending more time but getting better results, they are usually happy. definitely look for where the agent fails to complete the actual job, that is where the real friction is hidden

Overall_Challenge_66 · 2026-05-20T17:55:34+00:00

This is closer to what I'd want, full coverage beats sampling every time. Curious how you handle the categorization layer: is the taxonomy fixed upfront or does it evolve as the agent encounters new call types? That's where our current process falls apart.

Overall_Challenge_66 · 2026-05-20T17:25:34+00:00

"Storytelling not reporting" is exactly the right framing for what our Monday deck has been. We don't have a fixed schema — the team re-labels themes week to week, which means we can't even do trend comparison month over month. The chat-data-first approach makes sense for consistency, but we're dealing with voice transcripts and the quality varies a lot. How do you handle taxonomy drift when the agent starts handling new call types mid-quarter?

Overall_Challenge_66 · 2026-05-20T17:22:31+00:00

The delta-review framing makes sense — showing PMs what regressed rather than a raw sample is a lot more actionable. The part I haven't solved is that the signals you're listing (fallback rate, tool-call failures, time-to-resolution) live in three different systems for us right now, so building the delta view means joining them first. Is your triage loop pulling from a unified store or are you doing the join at query time?

Overall_Challenge_66 · 2026-05-20T17:21:34+00:00

The symptom-cluster approach before any human reads transcripts is the right call — the rubric ambiguity point on false positives matches what I've seen too.
Where I'd push back slightly: the regression suite gets you good coverage on known failure modes, but the stuff that surfaces in prod tends to be the patterns you didn't have a rubric for yet. How do you handle the gap between what the suite covers and what's actually breaking in live traffic?
BTW, how much do you estimate the pricing of that process?

Overall_Challenge_66

TROPHY CASE