u/Due-Guard221

I’m currently building in the ai/ml infrastructure space, specifically around automating model training and fine-tuning workflows: from dataset preparation to training, evaluation, and deployment.

we’re still pre-revenue / early build stage, and I’m trying to understand how experienced founders approached enterprise design partners or paid pilots before they had real traction.

i’ve seen a lot of b2b and enterprise startups validate this way before scaling, but the actual process is still unclear from the outside.

for founders who have built, scaled, or exited b2b / enterprise companies:

how did you get your first design partners or paid pilots?

were they warm intros, cold outbound, existing network, advisors, investors, accelerators, or something else?

how much did you charge for the first pilots, if anything?

what ACV did those early pilots eventually convert into?

were you pre-seed, seed, or completely unfunded when you closed them?

did enterprise buyers take you seriously before funding, or did you need investor/social proof first?

i’m especially curious about the messy early-stage reality, not the polished “we talked to customers” version.

What actually worked? I want to hear the raw version

I’m currently building in the ai/ml infrastructure space, specifically around automating model training and fine-tuning workflows: from dataset preparation to training, evaluation, and deployment.

we’re still pre-revenue / early build stage, and I’m trying to understand how experienced founders approached enterprise design partners or paid pilots before they had real traction.

i’ve seen a lot of b2b and enterprise startups validate this way before scaling, but the actual process is still unclear from the outside.

for founders who have built, scaled, or exited b2b / enterprise companies:

how did you get your first design partners or paid pilots?

were they warm intros, cold outbound, existing network, advisors, investors, accelerators, or something else?

how much did you charge for the first pilots, if anything?

what ACV did those early pilots eventually convert into?

were you pre-seed, seed, or completely unfunded when you closed them?

did enterprise buyers take you seriously before funding, or did you need investor/social proof first?

i’m especially curious about the messy early-stage reality, not the polished “we talked to customers” version.

What actually worked? I want to hear the raw version

i’ve been building ai workflows for small and mid-sized companies, and the same problem keeps showing up everytime like everyone wants automations now. invoice automation, meeting summaries, crm updates, sales followups, support bots, internal agents, ops workflows, all that stuff. and yeah, some of it is useful but not really. I still dont understand why nobody has built a simple saas around it for mid size businessess

but after you build a few of these, you realize the automations are just the top creamy layers and the painfull part is that the company has no 'shared memory'.

From what i've seen most companies adopting ai worflows has everything scattered.

zoom has the calls. slack has random decisions. google drive has docs isolated. notion has half-written SOPs. github has technical context. finance has invoices. ops has vendor/customer context. and the actual important knowledge is usually sitting inside one employee’s head.

then companies plug ai into this mess and expect a lot. obviously the output is generic, wrong, or needs babysitting every single time! the agent is not always the problem. the agent just has no idea how the business actually works and the internals

what worked better in a few companies I helped was building a simple internal memory layer first. not some overbuilt enterprise knowledge graph bullshit. just a structured company library that both humans and agents can use.

the library usually has things like:

• customer and vendor context
• internal SOPs
• meeting decisions
• finance rules
• ops notes
• project context
• prompts and workflows
• agent task history
• past mistakes and edge cases
• active priorities

the important part is not “store more docs.” companies already have too many docs. the important part is making the memory usable by agents. because such companies are shifting to agents a lot now !

so instead of every ai workflow starting from zero, the flow becomes more like this:

meeting happens → transcript gets summarized → decisions and action items get saved where they belong.

customer issue happens → agent checks customer context, past notes, rules, and previous edge cases before doing anything stupid.

invoice comes in → finance workflow checks vendor history, approval rules, exceptions, and routes it properly.

new employee joins → they can understand how the company actually works instead of asking 40 random questions in slack.

agent finishes a task → it writes back what changed, what failed, and what should be remembered next time.

That writeback part is where most people mess up. everyone is building read-only ai. “ask your docs” is fine, but if the system never updates memory after work happens, your ai wakes up every day with brain damage. it keeps starting from zero.

the tech itself can be boring. markdown files, github, drive, notion, postgres, vector search, whatever. the storage is not the magic. the magic is the structure around it.

you need:

• a memory map
• ingestion rules
• retrieval rules
• writeback rules
• permission boundaries
• human review loops
• stale context cleanup

because old context is dangerous too. an agent using outdated company logic is worse than no agent. at least no agent doesn’t confidently create chaos. i have faced this issue while i was getting started in this domain.

this is why i think the ai automation market is being framed too shallowly. and i really think someone should make a saas around this because the pain is validated. This is just a rough example, honestly if u go into mid size businessess the use cases far exceed than i have mentioned here , i cant mention everything here ofc.

i’ve seen companies ask for chatbots, invoice automation, crm updates, meeting summaries, internal copilots, finance workflows, etc. but one layer deeper, the question is always the same:

where does the agent get the correct business context from?

and most of the time the honest answer is:

“somewhere in slack probably.”

i think the bigger saas opportunity is a company memory layer for internal ai workflows. not another chatbot. not another random automation builder. more like the shared brain/spinal cord that connects employees, tools, docs, meetings, and agents.

Everyone wants to sell chatbots, agents, dashboards, automation pipelines, “we can automate your business” type offers. but brother automate what??? That is the part most people skip hard! They don’t spend enough time understanding whether the business even needs the thing they are pitching.

When you actually sit with these businesses, the problem is almost never “we need an ai agent or something like that” it is usually much uglier if u understand.

Their CRM is messy, their team is copy-pasting between tools, customer context is spread across emails, whatsapp, spreadsheets, pdfs, call notes, and random software nobody updates properly. Reports are slow, handoffs are broken, and the whole workflow is held together by one ops person who somehow knows where everything is.

Then, some ai guy walks in and tries to sell them a new chatbot. insane!

The biggest killer of an AI automation is behaviour change. If your system forces the client to open a new app, learn a new dashboard, change how their team communicates, or remember a new process, it is probably dead before it starts. Not because the tech is bad, but because people do not change how they work just because your loom demo looked cool.

The best automations i’ve built were not the sexy ones. They quietly fit into the workflow that already existed. If the team lives in email, build around email. If they live in whatsapp, build around whatsapp. If their sales process runs through spreadsheets, don’t act superior and force them into some fake agentic workspace. fix the spreadsheet flow.

If an insurance team is manually turning quote pdfs into proposals, they don’t need a chatbot or some fancy way to turn natural language into pdfs, what they need is the pdf parsed, the right fields extracted, the proposal filled, and the human only reviewing the final output. If an HVAC team is qualifying calls and checking job context across tools, they need the call, crm, service history, and next action stitched together inside the process they already use.

That is where AI actually gets paid honestly. not because it sounds futuristic, but because the business already feels brutally painful.

same thing we’re learning while talking to my design partners for my current startup. Focus on how easy you can make things for them without them needing to learn a bunch of new stuff. This is where the whole game of money is in automations. It dosent have to be more complicated than this!

Been helping a few builders this week, and this is what mostly breaks in vibe-coded apps. Same patterns keep showing up:

1. Row-level security is off or wrong, and your data is publicly readable. This is the scariest one. CVE-2025-48757 hit 170+ Lovable apps last year because RLS policies were missing or too permissive, attackers dumped full user tables and payment records just by tweaking a query. Lovable's built-in security scan checks whether RLS exists, not whether it actually blocks unauthorized access. If you have users on your app and you've never manually verified your policies, your app is probably exposed. This is the most common fix I do and the one most builders don't know they need.
2. App freezes or shows blank screen after login. Almost always the Supabase onAuthStateChange deadlock. The AI puts an async fetch inside the auth listener, Supabase docs explicitly warn against this. Lovable will try to fix it five or six times by tweaking loading states and never get to the root cause. Real fix is wrapping the async call in setTimeout(() => ..., 0).
3. "Login with Google" works in preview, breaks when you publish. Supabase's Site URL still pointing at localhost or your old preview URL. Two-minute fix when you know where to look, can eat your whole weekend if you don't.
4. Stripe payment goes through, feature doesn't unlock. Webhook handler not verified, edge function missing secrets, or the user record isn't getting updated. Almost always the webhook.
5. The fix-and-break loop. You ask for a filter, the table breaks. You fix the table, the filter disappears. Context-window loss, the AI is rewriting code it can't see. Fix is structural, not a prompt: split features into files, prompt with file references, stop saying "fix the app".
6. Edge functions silently failing on deploy. Missing env vars or secrets not configured in Supabase. Logs tell you exactly which one but most builders don't know to check the Cloud tab.
7. API keys leaking into the frontend. The service_role key is the dangerous one, it bypasses RLS by design. Should never be in your client code, sometimes Lovable puts it there.
8. App doesn't show up on Google. Lovable apps are client-side rendered by default, no SSR. If your product is content/marketing facing, you're invisible to search. Workarounds exist but only if you know to ask for them up front.

If you're stuck on something here or something not on this list, drop your repo or a screenshot in DMs and I'll take a look.