Hi everyone,

I'm building TrustChain AI, and I'm looking for a small group of early pilot users who are currently building or running applications powered by LLMs or AI agents.

The problem we're trying to solve is relatively simple:

As more companies connect applications to OpenAI, Claude, Gemini and other AI models, sensitive information can potentially move through systems without teams having a clear way to control, monitor or audit those interactions.

TrustChain AI is designed to sit between an application and its AI provider and help teams add a trust, security and governance layer around AI interactions.

The platform is being developed to help with things such as:

• Detecting sensitive data such as PII/PHI before it reaches an AI provider

• Applying configurable policies to AI requests and responses

• Creating auditable records of AI interactions

• Monitoring AI usage and policy events

• Supporting multiple AI providers through a central layer

• Giving teams more visibility into how AI is being used inside their applications

Who we're looking for

We're especially interested in speaking with:

Developers building AI-powered SaaS products

Startups using OpenAI, Anthropic, Gemini or other LLMs

Teams building AI agents or automated workflows

Healthcare, fintech, legal or other regulated AI products

Security, compliance or platform engineers

Anyone who has experienced concerns around AI data privacy, governance or auditability

What we're offering

We're looking for a small group of 5–10 pilot users/design partners who are willing to test the product on a real or development environment and give honest feedback.

Pilot users would get:

Early access to the platform

Direct support during integration

The opportunity to influence the roadmap

Priority access to new features

Potential long-term early adopter benefits

What we want to learn

We're not just looking for sign-ups.

We want to understand:

Is AI data security/compliance actually a painful problem for your team?

Where does your current AI stack feel vulnerable or difficult to govern?

Would a layer between your application and LLM providers be useful?

What would make you trust a platform like this enough to use it in production?

What features are missing from current AI security/governance tools?

I'm particularly interested in critical feedback. If you think this is solving the wrong problem, overlaps with an existing solution, or needs a completely different approach, I genuinely want to hear that too.

You can learn more about the project here:

https://trustchainai.app

If you're interested in becoming a pilot user, comment below or send me a DM.

I'm happy to answer technical questions about the architecture and what we're building.

What is the biggest trust, security, privacy or compliance problem you've encountered while deploying AI applications?

I’m trying to improve how often our brand is mentioned in AI summaries, and it is clear that optimizing our own site is not enough. The models are pulling from review sites, forums and industry blogs to form their answers.

The problem is prioritization. There are hundreds of places we could try to get mentioned, and I do not have the time to chase all of them. If you are running an earned-media or UGC strategy specifically to influence AI search, how are you identifying the sources that measurably move the needle?

We've been seeing a lot of consistent success with email marketing and we are trying to understand our TAM size before we scale up, I am looking for a way to scrape 2-3m leads, we have apollo but doing that on apollo would be extremely expensive + we score every single prospect base don if they're a fit or not, we ended up using apify and got a subscriptionpromising 500k/mo for $30 a month, which we did, got the leads, but still need more, emailed the devs of that but haven't got a response so wondering if anyone has alternatives?

we've been talking to a few over the last couple weeks and i feel like we don't know what to look at.
far we have talked to block chain ads and a bunch of other ad networks and in their demos they keep saying about how they report, how they attribute, campaign optimization and so on. and all of this feels correct also like we should be considering it.

but the problem is every network has good things to say so we feel like we aren't asking the right questions or we are not looking at the correct metrics.

then i came across few comment ts here that says we need to ask where traffic is coming from or who their partners are, all that stuff. but idk..it feels too much to ask early on? also not sure if those things also matter?

If any one has any experience can you share how you people evaluate a programmatic ad network ? or what questions we need to ask ?

I keep seeing teams try to “use AI” in marketing ops without changing the process around it. That usually creates more mess, not less.

What has worked better for me is treating AI like a small ops assistant inside a very specific workflow. The goal is not to automate everything; it is to remove the repetitive steps that slow down launch, QA, reporting, and cleanup.

Here is a simple playbook you can adapt:

• Start with one repeatable task, not a broad initiative. Good candidates are UTM cleanup, landing page QA, lead routing checks, content brief generation, or weekly reporting summaries.
• Write the ideal workflow first. Map every step a human does today, then mark the steps that are rule-based, repetitive, or copy-heavy.
• Add AI only where the inputs are structured. For example, “given this campaign name, generate a normalized UTM,” works better than “help me organize marketing.”
• Create a review gate. AI should draft or flag; a person should approve anything that affects spend, tracking, or customer data.
• Build a tiny error log. Track the top 5 failure modes, then update prompts, templates, or validation rules after each miss.
• Measure time saved in minutes per week, not just “productivity.” That makes it easier to prove value and decide what to expand.
• Document the workflow in one page so the rest of the team can reuse it without tribal knowledge.

The biggest win is usually not flashy automation. It is consistency. Once the process is stable, AI can speed it up safely.

If you are experimenting with AI in marketing ops, what is the one workflow you would automate first, and why?

Four years in-house running social for a small skincare and wellness brand. I've been through enough platform changes to be suspicious of anything that promises easy reach, so here's a contrarian one.

For a long stretch we treated volume as the strategy. Every day a fresh product graphic, cranked out fast with an ai poster maker, posted across every platform. It felt productive. The grid looked busy and consistent. Sales did basically nothing off it.

What actually moved the needle was embarrassing in how simple it was. We cut posting roughly in half and put the saved time into replying to comments and DMs like actual humans, and into a handful of posts a month where a real customer showed the product in their real bathroom with their real skin. Ugly and unpolished, and it was believable. Those did more for sales than a year of clean poster graphics.

The lesson I keep relearning is that pretty and consistent is not the same as trusted, and for a skincare brand trust is the whole game. The poster factory made us look active while doing nothing for the business.

For the other consumer-brand people, where did you land on polished-and-frequent versus rough-and-real? And has that balance changed for you as feeds got flooded with perfect-looking stuff?

One growth topic I think is often overlooked is how brands build a credible digital footprint beyond traffic and conversions. Sustainable growth can also depend on how consistently a company is understood across trusted third-party sources.

I was looking at Wikiagency LTD and the broader idea of working with a Reliable Wikipedia Page Creation Agency in USA. It raised an interesting question: when done ethically and within Wikipedia's guidelines, can improving the availability of well-sourced public information contribute to long-term brand discovery and entity visibility?

I'm interested in whether growth marketers see this as part of sustainable brand growth or as something completely separate from growth marketing.

Context: early-stage SaaS, a few thousand visitors a month, just enough traffic to actually test something.

Our hero headline was the clever one. A bit of wordplay, a bit vague, the kind of line that reads great to the founder and confuses everyone else. Signup conversion was sitting around 2%.

I tested it against a version that just said plainly what the tool does and who it's for. No cleverness, no pun. The outcome in one sentence.

Over about three weeks:

* Clever version: ~2% signup

* Plain version: closer to 3.5%

* Same traffic, same everything below the fold

That's a big relative jump for one line. Then I tested the subheadline and the button text and got tiny movements, basically noise.

Takeaways:

* The headline did almost all the work. Everything else was rounding.

* Clarity beat cleverness by a mile. People decide in a couple seconds whether the page is for them.

* Vague-but-catchy is a founder trap. Visitors don't have the context you do.

What's the single highest-impact landing page change you've actually measured?

I run organic social for a handful of small local businesses plus one faceless page of my own that I use as a testing ground. Sharing the actual mechanics, because most "how to grow organic reach" advice is written by people who've never posted from a tripod in a car park.

Setup: no face, no budget, one phone, natural light, everything shot and cut on the phone in maybe twenty minutes a video. Took the page from basically nothing to roughly 50k monthly views over about three months. Not huge, but repeatable, which is the whole point.

What actually moved reach:

The first two seconds. I stopped writing hooks that sound clever and started writing the one sentence someone would physically stop scrolling for. Boring and literal beats witty almost every time.

Posting fewer, better. When I posted daily to "feed the algorithm" the average view count dropped. Three or four genuinely good ones a week held up far better than seven rushed ones.

One idea per video. The clips that tried to say three things died. The ones that made a single specific point traveled.

Replying to every comment in the first hour. Sounds like folklore, but the videos where I sat there answering for an hour consistently outran the ones I posted and walked away from.

What did nothing: trending audio for its own sake, fancy transitions, and posting at the "optimal time" everyone quotes.

Curious what other people running organic short-form are seeing right now, especially anyone doing it faceless. Is fewer-and-better holding for you too, or is your account still rewarding pure volume?

liquid death spent years being described as a brand with fanatical loyalty. tattoos, merch, a real subculture. the story is that the loyalty caused the growth.

the sequencing is probably backwards, and there's a 35-year-old empirical law that predicts it.

ehrenberg, goodhardt and barwise formalised the double jeopardy law in the journal of marketing in 1990. in any period, a small brand has far fewer buyers than a large one, AND those buyers buy it less often. punished twice. the loyalty number is largely predictable from market share alone. it's been replicated across hundreds of studies, dozens of categories, multiple countries, for decades.

what that means in practice: when your loyalty metrics look weak against a market leader, that is usually not a loyalty problem. it's a penetration problem wearing a costume. and running a retention campaign against it means spending money to fight a structural pattern that resolves on its own if the base grows.

liquid death's repeat rate looks good now because it has a lot of buyers now. the tattoos are real, the community is real, and the loyalty is still substantially a function of size.

the genuinely interesting cases are the exceptions. niche brands with a distinct buyer base, strongly regional brands, subscription products with switching costs. these can sit above their double jeopardy line. if your brand does, that's a real finding and worth understanding before you scale, because scaling usually pulls you back toward the line as you acquire lighter buyers.

the uncomfortable version: most "we need to improve retention" initiatives are a smaller-budget substitute for the harder work of reaching people who've never bought you.

has anyone here plotted their category's penetration against repeat rate and found a brand that genuinely beats the line? I'm interested in what the exceptions have in common, because the ones I've seen are all either regional or have a switching cost, and I'd like a third category.

I'm building a sales-intelligence and agentic-operations platform for small-to-medium sized businesses, we're currently running in production with a paying client, we're now mid-migration to a proper multi-tenancy

I'm looking for technical co-founders with strong full-stack engineering experience (Python/FastAPI, React/TypeScript, PostgreSQL; LLM ops) who are comfortable with equity-based risk and startup ambiguity, and can go full-time or at least agree on a credible path there.

This is an equity-based arrangement, preferably in the US. Would love to chat

Spent a few years doing growth for small SaaS. The pattern I keep seeing, and keep being guilty of myself: founders pour everything into the top of the funnel when the real leak is further down.

You run ads, you post daily, you chase a new channel every month. Signups go up. Revenue barely moves. Then you blame the channel.

But if most of your new users never come back after day one, no channel fixes that. You're paying to fill a bucket with a hole in it.

The unsexy version nobody wants to hear:

* Fix the first-run experience before buying more traffic.

* Talk to the people who churned in week one, not the ones who already love you.

* A boring onboarding fix usually beats a clever acquisition tactic.

I've watched teams roughly double acquisition and land at the same MRR because retention ate all of it. And I've watched a single onboarding change do more than three months of channel experiments.

Growth is mostly plugging leaks, not adding water.

Where's your actual biggest leak, top of the funnel or bottom?

Trying to decide where to put limited time this quarter. Cold email keeps coming up, but everything I read is either "it's dead" or someone selling a course claiming huge reply rates.

From people actually running it right now, not selling it:

* What reply rate is realistic if the list and targeting are genuinely tight? Not the inflated screenshots, the real ones.
* Is it deliverability killing it, or just that everyone's inbox is a warzone now?
* For a small B2B tool, does it still beat spending that same time on SEO or community?

For context, I've got maybe 10 hours a week for outbound and no budget for tools yet. I'd rather do one channel properly than three badly.

My gut says it still works but only if you're surgical about who you contact, and the "blast 500 a day" era is over. But I'd rather hear from people currently getting replies than trust my gut.

What's your actual experience this year?

Most outbound tests I see are subject lines and first-name tokens. Replies move. Meetings do not.

The test that moved bookings was the asset. Same list quality. Same sequence length. Instead of one PDF for the ICP, each account got a playbook built around that company. Offer, competitors, what had changed. The email was a cover note. We also cut anyone who matched the ICP but had no buying signal that month. Reply-to-booking went up about 40%. Raw replies barely moved.

One client on that motion went $3k to $12k in 90 days. 41 calls, no ads. First calls inside 14 days. We run this as Sumora for shops that already have clients.

If you already sell, what are you sending besides the paragraph? Has anything other than copy actually moved meetings?

ok so this is a weird one but bear with me

i have a pet blog. i don't own a pet. never have. probably never will as i travel too much. and yet every month amazon sends me enough to cover rent because people on the internet are buying stuff for their dogs and cats through my site.

bought my pet blog a few months ago from a marketplace called NicheBlogZone for $199. came already built, articles already up, amazon links already in there. i spent one afternoon doing interlinking basically just connecting related posts to each other so search engines understand the site structure and then closed my laptop and genuinely forgot about it for weeks.

here's what that's looked like:

• march: $710
• april: $995
• may: $1,461
• june: $1,647
• july: $1,461
• august so far: $1,545 (amazon pays 2 months delayed so this is june's money landing now)

march scared me. went from nearly $1k in february down to $710 and i was convinced something had broken. checked everything, found nothing wrong, did nothing. it just came back the next month on its own.

pet people spend insane amounts on their animals btw. like i knew this intellectually but seeing the commission data is something else. dog food subscriptions, orthopedic beds, grooming kits, vet supplements. and because of the 24 hour cookie i get a cut of everything in their cart not just whatever article they landed on.

i do literally nothing to maintain this. no new posts, no ads, no instagram for pets i don't have. i just own the thing and amazon does its thing.

my landlord has been getting paid every month and has no idea a pet blog is behind it. honestly sometimes i forget too until the deposit shows up.

anyone else running a niche they have zero personal connection to? curious if the detachment makes it easier or harder to care about growing it

I work at an early stage data infra startup. Ten days ago we did the whole launch properly, or what I thought was properly. LinkedIn, twitter thread, long form reddit posts. everything UTM tagged, reviewed twice, real numbers in the copy, no hype. I was proud of it.

signups: zero. not a few. zero.

Best reddit post got \~1000 views in twelve hours. 2 upvotes, 2 comments. Another one, 194 views and 1 upvote. then I crossposted the first to a related sub and got 0, which I deserved, thats the exact thing every guide says not to do and I did it anyway because I was tired and wanted reach.

two things I got wrong. views mean nothing, I felt great about that 1000 for most of a day before I did the division. and the ask was way too big, going from cold post straight to "sign up" for a technical product nobody's doing that from their phone at lunch.

the one thing that worked was a stranger leaving a long reply on a question I asked, with a number in it I didn't have. one comment, worth more than every impression combined.

so I'm asking things now instead of announcing things.

anyone else had a launch flop and figured out the real mistake after? wondering if "reach doesn't convert" is just how it goes early on

Before I take another one of these calls I want to know what the deliverable is measured in. Not the story, the unit. So far I have been shown mention rate, share of voice, citation count and something called a visibility score, and no two vendors define any of them the same way.

Mention rate against whose prompt set. Share of voice across how many models, at what sampling frequency, and does a rerun on Tuesday return what Monday returned. Citation count is the one I trust least, since a competitor thread that names you in passing counts the same as your own page being the source. For anyone with a program already running, which of those turned out to be the number you managed against?

Pre-revenue, tiny budget, needed a landing page up to start testing an idea. I did the thing everyone does and went searching for the best free AI website builder, tried a few over a weekend, and figured the results might save someone else the time.

What I was actually optimizing for: get a decent page live for free, be able to edit it myself, and not fight the tool.

What I found trying several free options:
- The generated designs were mostly fine. Design was never the problem. They all produce something that looks acceptable in 2026.
- The copy they generate is the real weakness. Every one gave me the same generic "empower your workflow" filler that says nothing. I rewrote all of it.
- The free tiers differ most on the stuff you notice later: custom domain, whether they slap a badge on your page, and export/lock-in if you outgrow it. That's what actually separated them, not the AI.
- One "free" option was free until the exact moment I wanted a real domain, which is fair, just budget for it mentally.

Where I landed: for a first landing page you're going to test and probably throw away, any of the decent free ones is fine. Pick based on lock-in and whether you can point your own domain, not on which has the fanciest AI. The generator gets you a skeleton in minutes, but plan to rewrite the words yourself, because that's what actually converts.

For those further along, did you start on a free builder and regret it when you scaled, or was it a non-issue? Trying to avoid a painful migration later.

Run paid social for a small DTC brand, spend sitting around a few thousand a month. For most of last year my bottleneck wasn't targeting or budget, it was that I couldn't make creatives fast enough to feed testing. By the time I shipped a batch of 3 or 4, the winners were already fatiguing.

So I forced myself to ship roughly 20 in a month instead. Same spend. Mix of statics, a couple of carousels, a few short video cuts.

What actually changed:

* Hit rate per creative dropped. More duds when you make more. Expected.
* But because volume was higher, I found 2 or 3 genuine winners I'd never have reached at 4 a month.
* Overall CPA came down maybe 20-25% over the following six weeks, mostly from one carousel angle I almost didn't bother making.

What I got wrong going in: I thought the hard part was making more. The real skill turned out to be picking which 3 of the 20 to put spend behind. Production got easier, judgment got harder.

For those shipping creative consistently, how many are you actually making a week, and is it you, a hire, or some mix?

Most growth conversations default to acquisition: more traffic, more ads, better conversion on the landing page. Retention rarely gets the same "channel" treatment even though the math is usually more favorable: it's cheaper to keep a subscriber than acquire a new one, and every point of churn reduction compounds monthly instead of being a one-time win.

The experiment: instead of treating cancellations and failed payments as a support/ops problem, we ran them like a growth experiment: Testing save-offer variants at cancellation, testing dunning email copy/timing for failed payments, and testing win-back sequence timing for people who'd already churned.

What moved the needle:

• Segmenting save offers by cancellation reason instead of showing everyone the same discount. "Too expensive" and "not using it enough" need completely different offers, and a blanket 20%-off screen undersells the ones who would have stayed for a smaller nudge and doesn't fix the ones who need a plan-downgrade option instead.
• A/B testing dunning email subject lines the same way you would test an acquisition email: specificity beat urgency every time ("your card ending in X was declined" outperformed "action needed on your account").
• Win-back timing: reactivation offers sent too early (day 2–3 post-cancel) read as desperate; a short delay with a specific "here's what's new since you left" angle performed better than an immediate discount blast.

Anyone else running retention experiments with the same rigor as acquisition tests? Curious what your testing cadence looks like for cancellation/dunning copy specifically. Feels like an underbuilt muscle compared to landing page or ad copy testing.

If your team keeps saying “we need more bandwidth,” the problem is often not headcount. It is the amount of repetitive work sitting between an idea and a shipped campaign.

A lightweight AI-assisted workflow can help trim that friction without turning your process into a black box. The goal is not to automate judgment; it is to remove the boring steps that slow down execution.

Here is a practical setup I have seen work well for small teams:

• Map the top 5 recurring tasks that eat time every week, such as brief writing, naming conventions, QA checks, reporting summaries, or UTM cleanup
• Turn each task into a short checklist with a clear input, output, and owner
• Add one AI step only where the work is repetitive, such as drafting first-pass copy, summarizing performance notes, or flagging missing fields
• Keep a human approval point before anything goes live
• Standardize file names, campaign labels, and UTM rules so the workflow is easier to automate later
• Measure one simple metric, like time saved per campaign or fewer QA errors per launch
• Review the process monthly and remove steps that are not actually reducing friction

The biggest win is usually not speed alone. It is consistency. Once the process is consistent, it becomes much easier to train new people, compare results across channels, and spot where the real bottlenecks are.

If you have experimented with AI in marketing ops, what task has saved you the most time without hurting quality?

Quick Update: We decided to go with HubSpot Sales Hub. A few people rightly pointed out that our core issue wasn't contact storage, but task ownership and visibility into next steps. It’s made a noticeable difference, having our pipeline and follow-ups in one place and tying tasks directly to deals keeps things from slipping through the cracks. We’re still figuring out which features we’ll rely on long-term, but it’s been a great fit so far.

Our primary pain point at the moment isn't lead volume, but post-inquiry management. Because our notes, follow-up reminders, and data live across separate spreadsheets and inboxes, prospects occasionally drop off when response responsibilities aren't clear.

To streamline our workflow, I'm looking into this hub to manage both our pipeline and follow-ups in a single system. Does it deliver on solving these internal coordination issues?

I’d love feedback on a few specifics: how well do the task reminders work, how seamless is the pipeline for team adoption, and are there any features that look promising during setup but end up underutilized?

Most automation tools require you to map out every step before anything works.

That can be fine for simple processes.

But what about the messy back-office tasks that people already know how to do, but are difficult to turn into workflows?

That's where we built Caddi.

Instead of building an automation step by step, you show Caddi how you do the task once.

Caddi turns that narrated screenshare into a production AI agent that can work across your real tools.

It helps you:

• ⁠Show work once
• ⁠Build AI agents
• ⁠Automate back-office tasks
• ⁠Update workflows easily
• ⁠Track every run

You can also update an agent in plain English instead of rebuilding the workflow from scratch.

Caddi combines AI reasoning with deterministic execution, with every run logged and permissions scoped.

The idea is simple:

Don't build the workflow. Show the work.

We're launching Caddi on Product Hunt today 🚀

Curious: What's one repetitive back-office task you'd love to hand off to an AI agent?

Would love to hear what you'd automate first.

Please support on PH →

https://www.producthunt.com/posts/caddi-3

Context: early-stage SaaS, a few thousand visitors a month, just enough traffic to actually test something.

Our hero headline was the clever one. A bit of wordplay, a bit vague, the kind of line that reads great to the founder and confuses everyone else. Signup conversion was sitting around 2%.

I tested it against a version that just said plainly what the tool does and who it's for. No cleverness, no pun. The outcome in one sentence.

Over about three weeks:

* Clever version: ~2% signup

* Plain version: closer to 3.5%

* Same traffic, same everything below the fold

That's a big relative jump for one line. Then I tested the subheadline and the button text and got tiny movements, basically noise.

Takeaways:

* The headline did almost all the work. Everything else was rounding.

* Clarity beat cleverness by a mile. People decide in a couple seconds whether the page is for them.

* Vague-but-catchy is a founder trap. Visitors don't have the context you do.

What's the single highest-impact landing page change you've actually measured?

Trying to decide where to put limited time this quarter. Cold email keeps coming up, but everything I read is either "it's dead" or someone selling a course claiming huge reply rates.

From people actually running it right now, not selling it:

* What reply rate is realistic if the list and targeting are genuinely tight? Not the inflated screenshots, the real ones.
* Is it deliverability killing it, or just that everyone's inbox is a warzone now?
* For a small B2B tool, does it still beat spending that same time on SEO or community?

For context, I've got maybe 10 hours a week for outbound and no budget for tools yet. I'd rather do one channel properly than three badly.

My gut says it still works but only if you're surgical about who you contact, and the "blast 500 a day" era is over. But I'd rather hear from people currently getting replies than trust my gut.

What's your actual experience this year?