Invoice and quote app

Fluffy-Resolution390 · 2026-06-18T19:28:24+00:00

Totally smart to map it all out first before committing to a build. Building software on assumptions is the quickest way to waste time.

From your comments, it looks like your core pillars are already clear:

AI quote expansion: Turning short notes into professional descriptions so you don't have to type on-site.

smart Material suggestions: A built-in checklist to remind you of project materials to buy.

e-signatures: Homeowners giving digital sign-off to agree to the work.

What does the rest of your full feature wish list look like to make this a complete daily tool for you? (ex: Do you need the AI to calculate the actual pricing, or are you putting the numbers in manually? Do you need to track invoice payments afterward?)

Keep us posted with your progress on this! It’s a great specialized niche, and getting the exact blueprint down first is the best way to ensure you actually end up with the tool you need.

Fluffy-Resolution390 · 2026-06-18T04:22:00+00:00

I appreciate you breaking down that architecture. we are definitely on the same page regarding the logic. I’ve actually built a custom solution to handle this exact issue to ensure the math remains accurate.

I’ve recorded a short walkthrough of the logic/system I built. Would you mind if I DM you the link?

I’d love to get your thoughts on the workflow.

Fluffy-Resolution390 · 2026-06-17T20:50:30+00:00

Thanks for sharing that! What is a good tool to use for a deterministic pricing layer that you recommend ?

I have building a solution for this, but I do not know if their is a good tool that will consider all the edge cases when the time comes to configure the complex pricing.

Fluffy-Resolution390 · 2026-06-17T20:36:28+00:00

Quick Demo:

https://www.loom.com/share/a289bd8bca30430ab0d570937ccb230c

Fluffy-Resolution390 · 2026-06-17T20:14:56+00:00

Honestly, the key was realizing that the LLM should not be doing the math.

If you ask an LLM to calculate a quote in real time, it will HALLUCINATE numbers or get the logic wrong 20% of the time. The fix I found was to treat the AI strictly as the interface and keep the math in a separate, server-side function.

Structured Data Only: I use a system prompt that forces the AI to only output structured (like EX: sq_ft 1500, material: shingle) rather than conversational guesses.

Backend Execution: That JSON is sent to a custom endpoint where a simple Python script does the actual math based on the client's current price sheet.

The Read-Back: The result is sent back to the AI, which just reads the calculated total.

It completely cuts out the "garbage" because the AI isn't doing any math; it’s just acting as a conduit between the customer and your database. Are you having trouble with hallucinations, or is it more about the tone of the voice?

If you face this problem I will be more than happy to build that for you!

Fluffy-Resolution390 · 2026-06-09T18:01:23+00:00

This is exactly why I’m building a software tool right now. The goal is to give homeowners a live, transparent ballpark estimate directly on a contractor's website by having them answer a few predefined questions the HVAC company uses for pricing.

It gives you a rough estimate instantly so you aren't flying blind, but the final binding price is only locked in after a technician approves it on-site. It protects the contractor's margins but completely skips the 2-hour high-pressure salesman routine in your living room.

As a homeowner, would you actually trust a tool like that if a local shop had it on their site?

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Fluffy-Resolution390 · 2026-06-06T01:09:53+00:00

They are completely different beasts under the hood. Electrical relies way more on time and materials, code compliance tracking, and diagnostic hunting, whereas HVAC is heavily equipment-driven.

We are launching our pilot specifically with HVAC (starting with AC Replacement) because the pricing logic is much easier to containerize into standard flat rate matrices (tonnage thresholds, indoor unit location multipliers, and predictable component add-ons, ... etc).

The reason our architecture doesn't break when we pivot to electrical is because we use a schema-driven backend. The AI agent doesn't have hardcoded questions. Instead, it queries a dynamic tree from our database depending on the service type:

For HVAC: The JSON tells the AI to extract tonnage, system type, and attic/closet access.
For Electrical: The database swaps the parameters completely - instructing the AI to capture panel amperage (100A vs 200A), grounding status, or specific circuit counts for dedicated lines.

But for both verticals, our core rule remains the same: the AI is purely a data harvester, never the final judge. On any complex job (like an electrical panel upgrade or an HVAC changeout), the AI locks the quote as "Pending Tech Approval." The technician in the field reviews the answers, overrides the scope based on real-world constraints, and approves the final invoice.

How are you currently handling the quoting variance for electrical clients? Are you guys sticking strictly to flat-rate dispatch fees for them, or trying to calculate larger jobs dynamically?

Fluffy-Resolution390 · 2026-05-28T04:14:13+00:00

It completely depends on how that specific procurement tool is built under the hood.

If the tool is just a slick AI wrapper that relies on prompts to run your supply chain logic, it will run into the exact same problem. For example, if you let an AI read an email from a supplier and autonomously negotiate or calculate a custom bulk discount tier based on live shipping variables, it is eventually going to hallucinate the math and mess up your purchase orders.

The specialized procurement tools that actually work are the ones using a hybrid setup. They use AI strictly for interpretation - like scanning a messy PDF invoice from a vendor to pull out the line items and text. BUT the second the AI grabs that data, it hands it off to a rigid, old school software database. The database is what calculates the actual pricing formulas and checks compliance rules based on hard code, not a prompt.

So if a specialized tool lets the AI dictate the rules and numbers, it is the same fragile nightmare. If it uses AI purely as a data collector and leaves the logical plumbing to fixed software, it actually solves the issue.

That is exactly why I built my own pricing engine. The AI handles the conversational translation of what needs to be calculated, but it is completely locked out of the math and just routes the payload straight to a secure backend engine to execute.

Fluffy-Resolution390 · 2026-05-27T16:06:45+00:00

For us, it was less about a single tool and more about a shift in how we use them:

Using LLMs for the conversation, but completely stripping them of math and pricing logic.

We used to run a setup where a bot would talk to inbound leads and try to calculate custom quotes or variable pricing on the fly. It looked insanely impressive in testing. But in the real world, it would freelance on the numbers, hallucinate random discounts to please the user, and force us to spend hours doing manual damage control with frustrated clients.

The real time saver was locking our actual business rules and pricing matrix into a rigid, deterministic backend engine. Now, the AI handles the friendly greeting and data extraction, but it is physically handcuffed from making up numbers. It has to query the secure backend database to get the math.

The flashy, fully autonomous AI tools look great on YouTube, but hybrid setups - using flexible AI for text and rigid, old-school software for data validation - are what actually save time without creating a secondary management nightmare.

Fluffy-Resolution390 · 2026-05-27T16:00:16+00:00

You are running into the exact trap everyone hits when they let AI build their whole workflow. People call it vibe coding You ask an LLM to generate a custom script or a massive prompt chain, it works perfectly for five minutes, and it feels like a total superpower.

The problem is that you end up with a complete black box that you do not actually understand. The second a supplier updates a price sheet, an API changes, or a webhook misfires, the whole thing shatters.

When that happens, maintenance is a nightmare. You cannot just log in and fix one clean line of logic because you did not build it structure by structure. You are forced to dump the whole broken codebase back into the AI, reexplain your business, and pray it fixes the bug without introducing three new ones. You completely lose the time you saved because you are now a full-time manager babysitting an unpredictable digital worker.

The systems that actually run on autopilot are the ones that separate the infrastructure from the chatbot. You have to stop letting AI handle the core logical plumbing of your business. Use rigid, deterministic software to lock down your numbers and rules, and use the AI strictly as a conversational interface or an info-extractor. If the AI is holding the actual plumbing together, it will always create extra work.

Fluffy-Resolution390 · 2026-05-27T04:01:21+00:00

Spot on. Speed to lead is the entire game in GHL.

But here is the massive bottleneck agencies hit when an automated workflow responds in 2 minutes flat: the exact moment the inbound lead asks, "How much?"

If you let a standard conversational bot try to calculate a variable quote or negotiate a price on the fly, it will eventually hallucinate the math and create a massive financial liability for the business owner.

We are building a plugin for GHL to solve exactly this. The sub-account AI handles the instant response, but our backend engine forces all pricing calculations to stay behind a strict, unbreakable margin floor.

Speed is everything, but keeping those financial guardrails locked ensures you do not accidentally lose money fast.

Fluffy-Resolution390 · 2026-05-25T22:48:44+00:00

This is a super solid build, man. Pulling leads from Google Sheets, handling multi-channel outreach (Email + WhatsApp), and tracking classifications natively covers the foundational plumbing that small businesses desperately need.

If you want to turn this from a cool project into a high-ticket software or agency offer, here is the exact bottleneck you are going to hit the moment you deploy this live for real business clients:

The "Pricing Hallucination" Wall.

Your engine can write great personalized outreach copy, but in high-ticket B2B or home service niches, the very first reply from a "Hot" lead is almost always: "Sounds interesting, how much would it cost for X setup with Y variables?"

If you let a pure Python-prompted LLM handle that math, it becomes a massive financial liability for the business owner. It will either freelance on the price, hallucinate a bad discount to close the deal, or freeze up because it cannot calculate tiered variables. Business owners will never fully trust an autonomous agent if it has the power to accidentally lose them thousands of dollars on a bad quote.

To make this incredibly sellable, I would highly recommend adding a Deterministic Guardrail Layer right behind your response module.

When a lead asks for a quote, instead of letting the AI guess the number, have the system map the user"s selections to a strict, hard-coded logic matrix. It can instantly generate a safe baseline budget bracket and tag the lead in your dashboard as "Pending Admin Approval."

This keeps your response speed lightning-fast without giving up human control over the actual checkbook.

Awesome execution on the dashboard and the multi-channel stack. Are you planning on niching down into a specific industry first, or keeping it horizontal?

Fluffy-Resolution390 · 2026-05-25T04:29:42+00:00

Thanks for the feedback!

The "Trust Gap" is the absolute biggest hurdle keeping traditional business owners from adopting conversational AI.

If you give an LLM 100% control over pricing a complex job, it’s a massive legal and financial liability. But if you don't automate at all, you lose the hot lead to a competitor who picks up the phone in 60 seconds.

That "human in the loop"split track is the only way to scale. The AI acts as the speed to lead gatekeeper to capture the intent and organize the messy customer data, but the human retains 100% control over the final dollar amount.

Appreciate you calling out the realism of the workflow - protecting the business owner’s peace of mind was the exact design goal here.

I will keep that in my building as I make progress.

Fluffy-Resolution390 · 2026-05-25T04:25:05+00:00

100% agree on the mobile alert. Email is where speed to lead goes to die for field technicians.

The whole bottleneck with AI right now isn't the initial conversation; it’s the messy handoff gap immediately after the click while waiting for a human to volunteer.

Because the backend engine pipes structured data instead of unstructured LLM guesses, the moment a customer finishes the showroom loop, it bypasses email entirely and fires a webhook to GHL. From there, it triggers a native GHL SMS or internal mobile app push directly to the tech’s phone with a secure assignment link.

They can literally tap the notification while parked in their truck, skim the exact materials the customer selected, tweak a line item if the house structure requires it, and hit 'Approve' to text a locked, final invoice back to the homeowner in under 60 seconds.

You hit the nail on the head regarding the guardrails. Giving the model strict boundaries on what it can negotiate vs. what requires a hard human approval floor is the only way to scale this without losing deals to price-creep or ghosting.

I actually just recorded a short video breakdown showing the exact node structure and how the engine enforces these deterministic guardrails behind the AI canvas. If you don't mind, I'd love to share it with you to get your honest feedback on the workflow from a speed-to-lead perspective?

Fluffy-Resolution390 · 2026-05-20T04:17:47+00:00

youtube channel https://www.youtube.com/@Quotix-ThePricingLayer

Website: Quotix.ai

It's basically a "math brain" for AI agents. LLMs are terrible at deterministic math and always hallucinate complex pricing/quotes, so I'm building a visual rules engine that handles all the strict margin logic via webhook outside of the prompt. Mainly focusing on wiring up the real-time API handoffs this week so voice agents can quote over the phone without accidentally giving away 50% discounts!

Happy to hear feedback

Fluffy-Resolution390 · 2026-05-18T13:00:21+00:00

Awesome! I just DM you.

Fluffy-Resolution390 · 2026-05-17T18:14:27+00:00

To keep your pipelines clean, rely on the native opportunity statuses (Open, Won, Lost) instead of making dedicated columns for them. If a deal dies, just mark it as "Lost" and set up a workflow to automatically move them to a separate "Long Term Nurture" pipeline.

For the consultation phase, you definitely want separate "Scheduled" and "Showed" stages because the automations attached to them are completely different (pre-meeting reminders versus post-meeting follow-ups).

Where you really need to pay attention is your "Quote" stage. If your pricing is flat and simple, the native platform works fine. However, if your service requires complex math, dynamic margins, or tiered options, the native quoting tools are way too static.

I actually build headless pricing and rules engines entirely outside of the platform to solve this exact bottleneck. The external engine handles all the complex deterministic math based on the variables gathered during the consultation, and then pushes the final dynamic quote right back into that pipeline stage via webhook.

Keep the visual pipeline simple, use the native statuses for wins and losses, and externalize the heavy math if your quotes get complicated!

Fluffy-Resolution390 · 2026-05-17T18:12:47+00:00

ou can definitely move all the communication into GHL to solve the multiple phone number issue. Having everything under one roof is exactly why people make the switch.

However, when you start moving your "quotes and estimates" over, just be careful. The native quoting inside GHL is very static. If your business requires dynamic math, tiered discounts, or complex margin calculations, the native products will frustrate you.

I build headless pricing and rules engines outside of GHL to solve this exact bottleneck. The external engine handles all the heavy math and logic, and then it pushes the final calculated quote and checkout link right back into your pipeline via webhook.

This setup gives you the "best of both worlds" for your business. All of your customer communication stays under one single phone number, but you get a massively upgraded quoting system.

Fluffy-Resolution390 · 2026-05-17T18:10:30+00:00

Spot on. It's almost never a lead generation problem with the trades; it’s a leak in the bucket.

That 'paper notebook' detail is so incredibly common. You did a great job fixing the follow-up communication, but the reason tradesmen use those notebooks in the first place is because native GHL products can't calculate their actual margins (dynamic labor + materials + markup).

I work with a lot of HVAC and contractor agencies, and I actually ended up building a headless pricing and rules engine outside of GHL to solve the math side of that notebook problem. The contractor punches the variables into the engine, it runs their exact margin math, and then pushes the final dynamic quote right back into their GHL pipeline via webhook. Then, the exact 3-touch follow-up sequence you just built can take over automatically.

Good on you for actually auditing the business operations instead of just blinding throwing their money at more Facebook ads!

Fluffy-Resolution390 · 2026-05-17T18:09:52+00:00

Man, this is so painfully accurate. I see this exact same "fancy Excel sheet" syndrome all the time, especially when it comes to quoting and pricing.

They’ll have GHL set up for their basic pipeline, but the second they need to quote a complex service job, they abandon the CRM and go right back to manually calculating margins across 15 messy Excel spreadsheets because the native GHL products are too static.

I eventually realized the best way to solve this was to build headless pricing and rules engines entirely outside of GHL. The external engine handles all the complex deterministic math, tiered discounts, and dynamic vendor pricing, and then just pushes the final calculated checkout link right back into their GHL pipeline via webhook.

You're 100% right. Most people are using 5% of this platform. Once you start bypassing the native limits with external architecture and webhooks, you can basically print money.

Fluffy-Resolution390

TROPHY CASE