u/ur_piyo_a_hoe

My friend is an attorney who was losing 10+ hours every week to a painful routine: open an incoming PDF, find 3 specific data points, reformat them, paste them into his case management system, and repeat.

He wasn’t analyzing legal strategy but instead he was literally acting as a human copy-paste bridge between two software tabs.

You don't need a massive enterprise setup to fix this. While using ChatGPT or Claude directly works fine for one-offs, recurring document tasks just need a simple background pipeline:

Raw Document > LLM Extraction (Structured JSON) > Case Management / CRM

Instead of an associate manually reviewing dozens of standardized PDFs, the system auto-processes the batch and only flags weird edge cases for human review.

Where this actually delivers ROI:

• Client Intake: Parsing incoming client emails, extracting key dates/requests, and auto-creating tasks.
• Batch Extraction: Pulling liability caps, governing law, and effective dates from 50+ contracts into a clean sheet.
• Record Sync: Moving structured form data straight into client profiles without manual entry.

Practical AI isn’t about replacing attorneys or having a bot hallucinate legal arguments. It’s about stripping out the boring administrative friction around the actual work. The data is already digital—stop paying employees to move it around by hand.

If your firm is losing hours to repetitive document entry, drop a comment or shoot me a DM.

I've been seeing a lot of people rack up pretty insane API bills building with AI coding agents in the big 26, so I wanted to see how far I could get building an MVP using only free and open source AI tools instead of the usual Claude Code or Codex setup.

The app itself wasn't anything crazy. Just a simple food delivery MVP with authentication, a dashboard and a handful of CRUD endpoints. The goal was to see whether "basically free" is actually good enough for the kind of apps most people are building.

Before someone says "just run it locally", that's not realistic for most people. Running these models properly still requires decent hardware. This was about using hosted providers that serve open models without paying proprietary model prices.

I used OpenCode as the coding agent, then OpenCode Zen and NVIDIA Build for inference. Both support OpenAI-compatible APIs and let you use models like DeepSeek V4 Flash without owning a GPU.

I used DeepSeek V4 Flash for almost the entire project. Is it as good as Claude Opus or GPT-5 on difficult coding tasks? No. But for building a standard CRUD app, wiring up auth, generating boilerplate and fixing common bugs, it performed much better than I expected.

The biggest thing I took away wasn't that open models are better. It's that the gap has closed much faster than I expected. If you're building an MVP, you're mostly creating forms, APIs and business logic, not solving research problems. For that kind of work, a cheap open model through a hosted provider gets surprisingly close, and the cost difference is hard to ignore.

I've been seeing a lot of people rack up pretty insane API bills building with AI coding agents in the big 26, so I wanted to see how far I could get building an MVP using only free and open source AI tools instead of the usual Claude Code or Codex setup.

The app itself wasn't anything crazy. Just a simple food delivery MVP with authentication, a dashboard and a handful of CRUD endpoints. The goal was to see whether "basically free" is actually good enough for the kind of apps most people are building.

Before someone says "just run it locally", that's not realistic for most people. Running these models properly still requires decent hardware. This was about using hosted providers that serve open models without paying proprietary model prices.

I used OpenCode as the coding agent, then OpenCode Zen and NVIDIA Build for inference. Both support OpenAI-compatible APIs and let you use models like DeepSeek V4 Flash without owning a GPU.

I used DeepSeek V4 Flash for almost the entire project. Is it as good as Claude Opus or GPT-5 on difficult coding tasks? No. But for building a standard CRUD app, wiring up auth, generating boilerplate and fixing common bugs, it performed much better than I expected.

The biggest thing I took away wasn't that open models are better. It's that the gap has closed much faster than I expected. If you're building an MVP, you're mostly creating forms, APIs and business logic, not solving research problems. For that kind of work, a cheap open model through a hosted provider gets surprisingly close, and the cost difference is hard to ignore.

One thing I don't see many Voice AI tutorials talking about is email capture.

Getting an AI to capture someone's email sounds simple until you actually build it. Email addresses are one of those things where a single wrong character makes the whole thing useless. Unlike names, you can't really get away with being "close enough". Even if your STT is good, there are still quite a few places where things can go wrong.

One issue I ran into was how different voice models pronounce emails. The LLM would extract the email perfectly, but the TTS would read it back in a way that made the user think it was wrong. For example, an email would sometimes be spoken as "john hyphen smith at gmail dot com" or with random pauses between words, even though there was never a hyphen in the actual email. The backend had the correct email, but the user immediately interrupted to correct something that wasn't actually wrong.

After a bit of testing, I made a few changes that noticeably improved my email capture rate.

The biggest one was giving users a reason before asking for their email. Instead of asking "Can I have your email address?", the assistant now says something like "Perfect, I'll send the quote over. What's the best email to send it to?" It's a small change, but people are much more likely to answer naturally when they know why you're asking.

I also stopped making users repeat their entire email if only one part was unclear. If the assistant was unsure about the domain, it would just ask "Was that gmail.com?" instead of asking them to spell everything out again. It made the conversation feel much more natural and removed a lot of unnecessary friction.

It's one of those problems that doesn't seem important until you deploy an agent in production. The LLM might have done everything correctly, but if the user doesn't trust what they heard, they'll keep correcting an email that was already right. Small details like these don't make flashy demos, but they make a huge difference in how reliable a Voice AI assistant actually feels.

P.S There is also another way where you can send the email address to the AI assistant over SMS while on call, havent tried that yet but will do it as well.

One thing I don't see many Voice AI tutorials talking about is email capture.

Getting an AI to capture someone's email sounds simple until you actually build it. Email addresses are one of those things where a single wrong character makes the whole thing useless. Unlike names, you can't really get away with being "close enough". Even if your STT is good, there are still quite a few places where things can go wrong.

One issue I ran into was how different voice models pronounce emails. The LLM would extract the email perfectly, but the TTS would read it back in a way that made the user think it was wrong. For example, an email would sometimes be spoken as "john hyphen smith at gmail dot com" or with random pauses between words, even though there was never a hyphen in the actual email. The backend had the correct email, but the user immediately interrupted to correct something that wasn't actually wrong.

After a bit of testing, I made a few changes that noticeably improved my email capture rate.

The biggest one was giving users a reason before asking for their email. Instead of asking "Can I have your email address?", the assistant now says something like "Perfect, I'll send the quote over. What's the best email to send it to?" It's a small change, but people are much more likely to answer naturally when they know why you're asking.

I also stopped making users repeat their entire email if only one part was unclear. If the assistant was unsure about the domain, it would just ask "Was that gmail.com?" instead of asking them to spell everything out again. It made the conversation feel much more natural and removed a lot of unnecessary friction.

It's one of those problems that doesn't seem important until you deploy an agent in production. The LLM might have done everything correctly, but if the user doesn't trust what they heard, they'll keep correcting an email that was already right. Small details like these don't make flashy demos, but they make a huge difference in how reliable a Voice AI assistant actually feels.

P.S There is also another way where you can send the email address to the AI assistant over SMS while on call, havent tried that yet but will do it as well.

One thing I don't see many Voice AI tutorials talking about is email capture.

Getting an AI to capture someone's email sounds simple until you actually build it. Email addresses are one of those things where a single wrong character makes the whole thing useless. Unlike names, you can't really get away with being "close enough". Even if your STT is good, there are still quite a few places where things can go wrong.

One issue I ran into was how different voice models pronounce emails. The LLM would extract the email perfectly, but the TTS would read it back in a way that made the user think it was wrong. For example, an email would sometimes be spoken as "john hyphen smith at gmail dot com" or with random pauses between words, even though there was never a hyphen in the actual email. The backend had the correct email, but the user immediately interrupted to correct something that wasn't actually wrong.

After a bit of testing, I made a few changes that noticeably improved my email capture rate.

The biggest one was giving users a reason before asking for their email. Instead of asking "Can I have your email address?", the assistant now says something like "Perfect, I'll send the quote over. What's the best email to send it to?" It's a small change, but people are much more likely to answer naturally when they know why you're asking.

I also stopped making users repeat their entire email if only one part was unclear. If the assistant was unsure about the domain, it would just ask "Was that gmail.com?" instead of asking them to spell everything out again. It made the conversation feel much more natural and removed a lot of unnecessary friction.

It's one of those problems that doesn't seem important until you deploy an agent in production. The LLM might have done everything correctly, but if the user doesn't trust what they heard, they'll keep correcting an email that was already right. Small details like these don't make flashy demos, but they make a huge difference in how reliable a Voice AI assistant actually feels.

P.S There is also another way where you can send the email address to the AI assistant over SMS while on call, havent tried that yet but will do it as well.

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

When I started, every time my agent did something wrong I'd just add another instruction or another step in the call flow, "be more conversational" / "be more empathetic" / "don't interrupt" / "Ask follow-up questions."

A few weeks later I had this massive system prompt... and somehow the assistants actually got worse. They had more latency, more inconsistency, and sometimes it'd completely ignore the instruction I actually cared about and miss a step in the call flow (like not asking for email etc).

One thing I realized is that voice is just a completely different medium than chat. A response that looks perfectly fine in chat can sound really awkward over the phone. Long sentences, bullet-point style answers, formal wording.

I also stopped writing stuff like: "Use a warm professional tone" OR "sound excited."

In my experience, that barely changed anything.

What worked a lot better was prompting for behavior instead like tell it to say "That's awesome!" when someone shares good news. Use contractions and keep responses short (either through prompting or just setting the max\_tokens param). Throw in the occasional "Hmm, let me check that." Suddenly it feels way more human, even though the voice itself hasn't changed.

When I started, every time my agent did something wrong I'd just add another instruction or another step in the call flow, "be more conversational" / "be more empathetic" / "don't interrupt" / "Ask follow-up questions."

A few weeks later I had this massive system prompt... and somehow the assistants actually got worse. They had more latency, more inconsistency, and sometimes it'd completely ignore the instruction I actually cared about and miss a step in the call flow (like not asking for email etc).

One thing I realized is that voice is just a completely different medium than chat. A response that looks perfectly fine in chat can sound really awkward over the phone. Long sentences, bullet-point style answers, formal wording.

I also stopped writing stuff like: "Use a warm professional tone" OR "sound excited."

In my experience, that barely changed anything.

What worked a lot better was prompting for behavior instead like tell it to say "That's awesome!" when someone shares good news. Use contractions and keep responses short (either through prompting or just setting the max_tokens param). Throw in the occasional "Hmm, let me check that." Suddenly it feels way more human, even though the voice itself hasn't changed.

One thing I spent way too much time optimizing was tool call latency.

My voice agent makes multiple API calls during a conversation, and as the number of tools grew, so did the delay. I assumed it was my prompts, APIs, or the LLM itself. I optimized prompts, reduced tokens, and sped up my APIs. It helped, but there was still an awkward pause after tool calls.

Initially when starting with voice AI, I was using Vapi. It worked well overall but once my agent became more tool-heavy, the latency + the call quality dropped significantly.

I migrated the exact same agent to Telnyx. Same prompts, same tools, same APIs but I chose and open source llm (kimi 2.5)

The difference was immediately noticeable. Tool calls felt much snappier, the conversation flowed better, and overall latency dropped quite a bit. I also like that Telnyx hosts a number of open source LLMs, so having everything under one provider simplifies the stack.

Has anyone else compared providers while keeping everything else the same? I'd be interested to hear if you've seen similar results.

P.S Not saying that telnyx is objectively better than vapi, but in my personal experience it provided a much better experience

One thing I spent way too much time optimizing was tool call latency.

My voice agent makes multiple API calls during a conversation, and as the number of tools grew, so did the delay. I assumed it was my prompts, APIs, or the LLM itself. I optimized prompts, reduced tokens, and sped up my APIs. It helped, but there was still an awkward pause after tool calls.

Initially when starting with voice AI, I was using Vapi. It worked well overall but once my agent became more tool-heavy, the latency + the call quality dropped significantly.

I migrated the exact same agent to Telnyx. Same prompts, same tools, same APIs but I chose and open source llm (kimi 2.5)

The difference was immediately noticeable. Tool calls felt much snappier, the conversation flowed better, and overall latency dropped quite a bit. I also like that Telnyx hosts a number of open source LLMs, so having everything under one provider simplifies the stack.

Has anyone else compared providers while keeping everything else the same? I'd be interested to hear if you've seen similar results.

P.S Not saying that telnyx is objectively better than vapi, but in my personal experience it provided a much better experience

I’ve been building in voice AI for a bit now and when I started, I genuinely thought it’s just three simple layers. Speech to text, LLM, text to speech. Plug them together and you get a working voice agent.

But in production it’s nothing like that. The real gap between demo and something that actually feels human is huge.

Some things I learned from actually working on it:

1. Voice choice matters a lot more than I expected I used to think any decent 11labs voice would work, but in real calls most voices still feel synthetic or “off” after a few minutes. Small things like tone stability, pacing, and naturalness matter more than clarity alone. Right now I’ve been using the 'Jessica' voice and it’s the first one that consistently feels natural in production for me.
2. Filler words are not optional I used to remove them to make responses cleaner. That was a mistake. Humans naturally say things like “hmm”, “let me see”, “right”, and without that the AI feels robotic even if the content is perfect.
3. Prompt size directly affects latency more than I expected Even though prompt bloating does not change how human the response sounds, it changes how the experience feels. I reduced system prompt size and saw around 100 to 200 ms latency improvement, especially with faster models like Haiku 4.5 and GPT 4.1. In voice, that delay is very noticeable.
4. Turn detection is probably one of the most important settings This is underrated. If it is too aggressive, the AI interrupts the user. If it is too slow, the user ends up interrupting the AI or waiting awkwardly. Getting this balance right changes the entire “feel” of the conversation.

Overall, I expected voice AI to be mostly model work, but it is actually more like tuning a conversation system. Small UX level details matter just as much as the models themselves.

I’ve been building in voice AI for a bit now and when I started, I genuinely thought it’s just three simple layers. Speech to text, LLM, text to speech. Plug them together and you get a working voice agent.

But in production it’s nothing like that. The real gap between demo and something that actually feels human is huge.

Some things I learned from actually working on it:

1. Voice choice matters a lot more than I expected I used to think any decent 11labs voice would work, but in real calls most voices still feel synthetic or “off” after a few minutes. Small things like tone stability, pacing, and naturalness matter more than clarity alone. Right now I’ve been using the 'Jessica' voice and it’s the first one that consistently feels natural in production for me.
2. Filler words are not optional I used to remove them to make responses cleaner. That was a mistake. Humans naturally say things like “hmm”, “let me see”, “right”, and without that the AI feels robotic even if the content is perfect.
3. Prompt size directly affects latency more than I expected Even though prompt bloating does not change how human the response sounds, it changes how the experience feels. I reduced system prompt size and saw around 100 to 200 ms latency improvement, especially with faster models like Haiku 4.5 and GPT 4.1. In voice, that delay is very noticeable.
4. Turn detection is probably one of the most important settings This is underrated. If it is too aggressive, the AI interrupts the user. If it is too slow, the user ends up interrupting the AI or waiting awkwardly. Getting this balance right changes the entire “feel” of the conversation.

Overall, I expected voice AI to be mostly model work, but it is actually more like tuning a conversation system. Small UX level details matter just as much as the models themselves.

We’re a team of 3 Fullstack AI Engineers + a UI designer running a web + AI agency. We build websites for local businesses and also help them stop missing calls with voice AI agents + improve visibility through Ask Engine Optimization.

We’ve been getting clients from Upwork/Fiverr so far but that isnt too stable, now moving into cold outreach and need someone focused on booking calls.

What you’ll do:

• Reach out to local business owners via cold calls
• Start conversations and follow up
• Book qualified calls for us

We have 2-3 niches for now, as we can build good and targeted websites for them.

Commission only for now, but we’re open to moving into a monthly retainer + higher commissions once things are consistent.

If you’re good at talking to people and can actually bring in conversations, DM me with a bit about your experience.

We’re a team of 3 Fullstack AI Engineers + a UI designer running a web + AI agency. We build websites for local businesses and also help them stop missing calls with voice AI agents + improve visibility through Ask Engine Optimization.

We’ve been getting clients from Upwork/Fiverr so far but that isnt too stable, now moving into cold outreach and need someone focused on booking calls.

What you’ll do:

• Reach out to local business owners via cold calls
• Start conversations and follow up
• Book qualified calls for us

We have 2-3 niches for now, as we can build good and targeted websites for them.

Commission only for now, but we’re open to moving into a monthly retainer + higher commissions once things are consistent.

If you’re good at talking to people and can actually bring in conversations, DM me with a bit about your experience.

We’re a team of 3 Fullstack AI Engineers + a UI designer running a web + AI agency. We build websites for local businesses and also help them stop missing calls with voice AI agents + improve visibility through Ask Engine Optimization.

We’ve been getting clients from Upwork/Fiverr so far but that isnt too stable, now moving into cold outreach and need someone focused on booking calls.

What you’ll do:

• Reach out to local business owners
• Start conversations and follow up
• Book qualified calls for us

Commission only for now,, but we’re open to moving into a monthly retainer + higher commissions once things are consistent.

If you’re good at talking to people and can actually bring in conversations, DM me with a bit about your experience.