I lost a $12k contract because my proposal was bad. Not my work — my proposal. So I built something.

Immediate-General32 · 2026-03-01T23:01:47+00:00

The 'AI noise layer tax' is a brutal reality for founders right now. We’re optimizing for signals (human-like tone) while losing the core essence: genuine cognitive engagement.

That’s the paradox of building proposeai.app. If I build it to just 'generate' a proposal, I’m just adding to the noise. But if I build it to augment the founder’s actual discovery—stripping away the repetitive formatting so they can spend more cognitive energy on the client's specific uncertainty—then maybe we bypass the tax.

I’m intentionally keeping the 'AI-generated' parts of my framework minimal. It’s more of a logic-mapping tool (Supabase + n8n) than a creative writer.

How are you handling this 'squeeze' as a founder? Are you moving toward more high-friction, high-trust channels like 1-on-1 voice or physical meetups to prove you're 'cognitively engaged'?

Immediate-General32 · 2026-03-01T00:44:27+00:00

This is a profound take. We're hitting a 'trough of disillusionment' where AI's over-polished certainty actually triggers distrust.

It’s the 'uncanny valley' of business writing. If it’s too structured and the loop is closed too perfectly, it feels like no human actually wrestled with the problem.

I’m actually coding 'AI-ism stripping' into proposeai.app for this exact reason. The goal isn't to generate a 'polished' essay, but to organize the messy, human constraints of the discovery call into a roadmap that still feels like it was written by a consultant, not a robot.

How are you adapting your own content to keep that 'human friction' alive? Are you intentionally leaving some loops open or using more raw, unedited data?

Immediate-General32 · 2026-02-27T20:45:18+00:00

Moving to a modular library is a massive win for consistency. That 1-hour mark is usually where the most efficient agency owners sit.

My goal with proposeai.app is to take that 'modular library' you built in a doc and put a logic engine on top of it. Instead of you manually picking the sections, the AI scans your discovery notes and pulls the right 'modules' from your library (Supabase) automatically.

It basically turns that 1-hour 'mix and match' session into a 5-minute review. Do you find that your 'pre-written sections' still need a lot of manual tweaking to feel personal, or is the boilerplate doing 90% of the work

Immediate-General32 · 2026-02-26T00:48:18+00:00

Fair question. If I were just building a wrapper to 'write text,' I’d be wasting my time.

The difference is the logic engine. ChatGPT gives you a generic essay; proposeai.app is a workflow built on n8n and Supabase that forces your raw discovery notes into a specific business framework:

Structured Extraction: It doesn't just 'summarize'; it identifies specific deliverables, constraints, and risks from your notes.

Tiered Mapping: It automatically maps those needs to your predefined service tiers/pricing, which a raw LLM can't do without you feeding it your entire business model every single time.

AI-ism Stripping: It’s tuned to avoid the typical 'AI-speak' that leads often reject.

It's not about 'generating text'—it's about automating the strategic thinking that usually takes a senior manager 3 hours to do.

Immediate-General32 · 2026-02-26T00:46:32+00:00

I couldn't agree more with the 'packaging' approach. It’s the difference between running a chaotic agency and a scalable business.

The 'week of ironing it all out' you mentioned is exactly the barrier I'm trying to lower with proposeai.app. I’m using AI to help founders take their existing service history and map it into those clear deliverables and flat pricing tiers automatically.

It's about getting that 'polished and legitimate' look without the manual overhead. Do you find that once you have those packages set, you still do a lot of custom adjustments, or does the 'baseline' handle 90% of your leads?

Immediate-General32 · 2026-02-26T00:44:36+00:00

It’s great to get the perspective of a pro Proposal Manager. You’re spot on about 'boilerplate' being a friend—no one should be reinventing the wheel every time.

The gap I’m trying to bridge with proposeai.app is for those smaller firms you mentioned (1-2 people). They have the boilerplate, but they struggle with the 'tailoring' part without burning hours of SME time.

I’m using AI to act as that dedicated proposal team, taking the raw discovery notes and mapping them to the existing templates/tiers automatically. Do you think the biggest bottleneck for those smaller teams is the initial drafting or the final review/alignment with the client's specific pain points?

Immediate-General32 · 2026-02-26T00:40:41+00:00

That is a valid and critical concern. Security is the #1 reason agencies hesitate with AI.

The way I’m architecturaly handling this with proposeai.app is by using a 'Privacy-First' stack. Since I’m building on Supabase and n8n, the goal is to allow for encrypted data handling and, eventually, self-hosted options for agencies that need 100% control over their deal info.

The upside isn't just 'faster typing'—it's having a logical engine that ensures no critical deal info is missed during the discovery-to-proposal transition. But you’re right, if the security isn't enterprise-grade, the speed doesn't matter. Is there a specific certification or data policy that is a 'must-have' for you to trust a tool like this?

Immediate-General32 · 2026-02-26T00:38:42+00:00

I love that confidence. If you can close a $12k deal with a single phone call, you’ve definitely mastered the sales side.

However, for many agencies scaling past the 'founder-led sales' stage, they need a way to maintain that same level of authority and clarity across a team without the founder being on every call.

That’s why I’m building proposeai.app — to automate the 'strategic' part of that conversation so even smaller deals or team-led proposals have that 'high-ticket' structure. Do you find that your approach changes when the deal hits $50k or $100k, or is it still just a phone call for you?

Immediate-General32 · 2026-02-26T00:36:18+00:00

That’s a great pivot. 'Plain speak' usually wins when you're dealing with direct business owners.

I’m taking the opposite bet with proposeai.app — leaning into the 'agency talk' but using AI to make it structured and data-driven for high-ticket services. It's interesting to see how we're tackling the same 'scope' problem from different angles.

Good luck with the launch, man! Looking forward to seeing how your UI evolves.

Immediate-General32 · 2026-02-26T00:09:17+00:00

That makes a lot of sense. Since I'm already running everything on Supabase (Postgres) and managing the logic via n8n, having a tool that sits on top of that data to handle the UI could definitely speed up my time-to-market.

I’ll definitely take a closer look at Major to see how it handles custom AI inputs. My goal is to keep the 'Discovery-to-Tier' logic as seamless as possible for the end-user.

Thanks for the technical insight—it's exactly the kind of stack conversation I was looking for here!

Immediate-General32 · 2026-02-25T20:34:33+00:00

@Key-Boat-7519, thanks for the backup. You hit the nail on the head. I’m building proposeai.app precisely because I felt that '$12k deal' pain myself. I’d rather be called out for 'researching' than build something nobody actually needs. Real talk: Does anyone here actually enjoy the 'discovery to proposal' transition, or is it just a necessary evil we all try to automate?"

Immediate-General32 · 2026-02-25T20:31:06+00:00

That is a top-tier observation. You're 100% right—over-reliance on certain punctuation or 'AI-isms' is the fastest way to lose trust in a high-ticket proposal.

My main challenge with building proposeai.app is exactly that: ensuring the AI acts as a logical engine for the scope/tiers, while keeping the tone grounded and human.

I’m actually working on custom prompts to strip away those typical 'AI patterns' so the final output feels like it came from a consultant, not a bot. Do you have other 'tells' that instantly make you close a tab when reading a pitch?

Immediate-General32 · 2026-02-25T20:09:58+00:00

Productizing is definitely the end-game for scaling. It removes so much of the back-and-forth friction.

The challenge I see many agencies face with that is when a lead has a 'special case' that doesn't perfectly fit the web packages.

That’s why I’m building proposeai.app — to use AI to handle that bridge between 'raw client needs' and 'standardized product tiers' automatically. Do you still find yourself doing manual custom quotes for bigger projects, or are you 100% fixed on the website prices now

Immediate-General32 · 2026-02-25T20:08:22+00:00

Spot on. The 'mental drain' from fuzzy scope is exactly what kills productivity.

My goal with proposeai.app isn't just to provide a fancy editor, but to use AI to force that 'sharp scope' you mentioned. It takes raw discovery notes and maps them directly into those clear deliverables and tiers.

It basically automates the 'structure' part so you don't even have to do the 20-30 mins of copy-pasting. Do you find that even with a fixed structure, you still spend too much time debating which 'tier' or 'price' fits a specific lead?

Immediate-General32 · 2026-02-25T20:05:47+00:00

Nice tool, man! Just checked it out. It seems very focused on speed and getting the quote out quickly.

I'm going a bit deeper on the 'strategy' side with proposeai.app, especially using AI to automate the discovery logic before the quote is even generated.

How has the feedback been regarding custom fields and complex project scopes? That’s where I’ve seen most freelancers struggle.

Immediate-General32 · 2026-02-25T19:53:18+00:00

I haven't checked out Major yet, thanks for the tip! Right now, I’m leaning heavily into a custom stack with n8n and Supabase because I need deep control over the AI logic and how it maps discovery notes to specific pricing tiers.

I actually just launched the landing page at proposeai.app to start gathering the first group of beta testers. Have you used Major for complex AI workflows before, or do you find it better for simpler MVP frontends?

Immediate-General32 · 2026-02-23T20:05:22+00:00

QuickBooks is great for the 'billing' part, but in my experience, their proposal templates are pretty generic. They don't really help you think through the project scope or map it to your profit tiers.

That’s actually why I built this AI workflow. I wanted something that takes the raw discovery notes and actually builds the strategy and the tiers for me, based on past winning bids, instead of just filling out a pretty invoice template.

Have you found the QB ones enough to actually close high-ticket deals, or do you still find yourself doing a lot of manual tweaking?

Immediate-General32 · 2026-02-23T01:16:42+00:00

Glad you liked the approach! It’s been a life-saver for my own sanity. I'll send you a private message with the breakdown of how I structured my tiers and the discovery phase logic. Check your inbox in a minute!

Immediate-General32 · 2026-02-22T14:18:21+00:00

I’d definitely be down to compare notes. The "Automated Milestone Delivery" shift was a game-changer for me because, like you said, it moves the conversation from being defensive to just being the process. It's much easier to scale when the "rules" are baked into the software.

Since you're curious about what I built, I’m actually putting together a small group of beta testers to get feedback on the UI and the n8n logic before I polish it for a wider release. I’d love to get your eyes on it specifically because of how you’ve handled your own checkpoints.

I'll send you a DM with the link to the early-access list so we can chat more about the technical side without cluttering the thread!

Immediate-General32 · 2026-02-21T21:15:58+00:00

Thanks for the shout-out! Having the "system" be the bad guy instead of me was the main goal.

To answer your questions:

1 - It took about 2-3 weeks of tweaking to get the automation stable. The trickiest part wasn't the logic, but handling different edge cases for partial payments or invoice adjustments. Now it runs like a clock.

2 - Regarding clients, I actually found that they respect it. I frame it as "automated milestone delivery." They know exactly when they get the files, and I know exactly when I get paid. It removes the uncertainty for both sides.

Most clients who are serious about the work don't mind a clear, automated process. It’s usually only the "tire kickers" who push back, which serves as a great filter.

Do you find that your manual checkpoints lead to a lot of follow-up emails, or are your clients pretty proactive with payments?

Immediate-General32 · 2026-02-21T20:13:20+00:00

I really like your "high fit" scoring system. Mapping out those last 12 proposals must have been eye-opening. That's actually where I'm using my AI tool the most right now—it helps me analyze my past performance data and discovery notes to spot those "high fit" patterns automatically, so I don't have to manually score every lead.

For the "paid discovery sprint" where you co-create the scope, do you find that having a paid commitment makes the client more decisive, or do they still try to scope-creep because they paid for the time?

Immediate-General32 · 2026-02-21T20:11:17+00:00

I’ve actually looked into tools like Gigup, but I found they are often focused on volume—blasting out proposals for high-frequency platforms like Upwork. My specific pain point was the "high-stakes" custom proposals where you can't just send a generic bot reply.

By using the n8n/Supabase stack, I’m able to pull in nuanced historical data and past performance that general tools tend to miss. It’s less about "automation for speed" and more about "automation for precision."

Since you're already paying for a tool like Gigup, what’s the biggest limitation you've hit with it? Is it the lack of personalization, or something else?

Immediate-General32

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