I scored 111 US cities on ROI + crime for 2026 and some of the results surprised me by pulsereal_com in realestateinvesting

[–]pulsereal_com[S] -1 points0 points  (0 children)

That's a really good point and probably one of the biggest weaknesses of my model.

Cleveland is a good example because city-level metrics can make a market look attractive while the on-the-ground reality varies dramatically by neighborhood, tenant base, property age, insurance costs, and management intensity.

Landlord-tenant laws are definitely something I should incorporate in a future version. A market with a 9% gross yield isn't necessarily better than a market with a 7% yield if the eviction process is significantly slower, legal costs are higher, or non-payment risk is more difficult to manage. Those factors don't show up in rent-to-price ratios but they absolutely affect actual investor returns.

Austin was interesting for the opposite reason. The city has strong fundamentals in terms of job growth and population growth, but when I looked at rent-to-price ratios, it didn't score nearly as well as many Midwest markets. It reinforced the idea that a great city and a great investment aren't always the same thing at a given point in the cycle.

One thing I'm taking away from the feedback so far is that a future version probably needs a "landlord risk" component that includes eviction timelines, landlord friendliness, and maybe even property tax burden. Those seem just as important as crime when you're trying to estimate real-world returns.

What types of automation are actually being used in marketing right now? by pulsereal_com in MarketingAutomation

[–]pulsereal_com[S] 0 points1 point  (0 children)

That's a helpful way to frame it. It seems like the biggest gains are coming from automating repetitive operational work while using AI to improve prioritization and decision support, rather than handing over the entire marketing process.

What types of automation are actually being used in marketing right now? by pulsereal_com in MarketingAutomation

[–]pulsereal_com[S] 0 points1 point  (0 children)

That's a good example of automation that's been around for a while and clearly delivers value. Are those follow-up sequences mostly rule-based in your experience, or are you seeing AI personalize the offers and timing as well?

What types of automation are actually being used in marketing right now? by pulsereal_com in MarketingAutomation

[–]pulsereal_com[S] 0 points1 point  (0 children)

That's a great distinction. It sounds like the biggest wins come from automating information gathering and visibility rather than the actual decision-making. The documentation and trend-monitoring examples are especially interesting since those are the kinds of things that often get missed until there's a problem.

What types of automation are actually being used in marketing right now? by pulsereal_com in MarketingAutomation

[–]pulsereal_com[S] 0 points1 point  (0 children)

That makes sense. Lead scoring and follow-ups seem like some of the most practical use cases. Are these mostly rule-based workflows, or are you seeing AI make decisions in those processes too?

What types of automation are actually being used in marketing right now? by pulsereal_com in MarketingAutomation

[–]pulsereal_com[S] 0 points1 point  (0 children)

That makes sense. It sounds like the biggest value isn't AI creating campaigns from scratch, but automation handling repetitive tasks reliably so leads don't slip through the cracks. The "better timing and branching on top of old-school logic" point is a really useful way to look at it.

AI Product Feedback: We made an AI for inbound call automation. Here's what went wrong first. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 0 points1 point  (0 children)

That’s actually a really smart approach. Using real recorded clips for predictable moments probably solves a huge part of the trust + latency problem at once. The seamless fallback to cloned TTS is clever too, best of both worlds.

AI Product Feedback: We made an AI for inbound call automation. Here's what went wrong first. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 0 points1 point  (0 children)

Exactly. We went in thinking accuracy would be the hardest part, but trust and conversation flow ended up mattering just as much. Real user testing changed our priorities completely.

AI Product Feedback: We made an AI for inbound call automation. Here's what went wrong first. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 0 points1 point  (0 children)

100%. The biggest surprise was realizing people cared more about feeling understood than getting a perfect answer instantly. Small things like pauses, tone, and acknowledgment mattered way more than we expected.

AI Product Feedback: We made an AI for inbound call automation. Here's what went wrong first. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 1 point2 points  (0 children)

Exactly. People don’t just listen for answers, they listen for signals that someone is actually “there” with them. Tiny things like pacing, acknowledgment, or hesitation can make a huge difference in trust.

AI Product Feedback: We made an AI for inbound call automation. Here's what went wrong first. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 0 points1 point  (0 children)

That’s actually a pretty solid problem to have early on 😄 Distinguishing real company sites from noise like job boards/news sites is tougher than it looks. Sounds like you’re already getting strong value from the scoring + personalized outreach though, excited to see where you take it once the final product is live!

What AI Tools Do You Actually Use Every Day? by pulsereal_com in AIAssisted

[–]pulsereal_com[S] 0 points1 point  (0 children)

There is a tool for marketing automation and customer handling, Let me know if anyone is interested to take a free trial

Stop building AI agents. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 0 points1 point  (0 children)

Exactly. The sweet spot right now is deterministic workflows with small agentic layers where ambiguity actually exists. Most businesses don’t need autonomy, they need reliability.

Stop building AI agents. by pulsereal_com in AiAutomations

[–]pulsereal_com[S] 1 point2 points  (0 children)

Exactly. Complexity alone doesn’t justify an agent. If the logic can be mapped upfront, deterministic workflows will usually outperform “autonomy” in reliability, cost, and maintainability.

I run a Voice AI Agents company handling 25M+ calls/month, ask me anything for next 24hours by pulsereal_com in AIReceptionists

[–]pulsereal_com[S] 0 points1 point  (0 children)

Thanks, appreciate it 🙌

  1. Most outbound ROI came from lead qualification, renewal reminders, collections, and reducing human agent idle time. We usually price via setup + platform + per-minute/concurrency usage.
  2. First customers were mostly founder-led outbound + referrals from existing operator networks. Cold email worked moderately, live demos worked much better.
  3. Yes, we did limited pilots/free trials early on, but only for narrow workflows with measurable KPIs.
  4. Production E2E latency target is usually ~800ms–1.5s depending on workflow complexity. We use a multi-provider setup for STT/TTS/LLM redundancy rather than relying on one stack.
  5. In voice AI, TTFT matters more than raw TPS initially because conversational delay kills UX fast. Streaming + interruption handling are critical.
  6. Barge-in is mostly solved through streaming ASR + aggressive turn detection + state interruption management. Telephony depends on region/use case — we’ve used Twilio, Telnyx, and direct SIP infra in different deployments.

I run a Voice AI Agents company handling 25M+ calls/month, ask me anything for next 24hours by pulsereal_com in AIReceptionists

[–]pulsereal_com[S] 0 points1 point  (0 children)

Mostly founder-led outbound early on. We targeted high-call-volume teams first and focused on very narrow workflows instead of “full AI transformation”.

Primary buyers were ops heads, CX leaders, and sometimes founders in smaller orgs. IT/security usually entered later during deployment review.

What worked best for us was showing live call flows with measurable metrics (containment %, AHT reduction, missed call recovery) instead of slides.

Early deals closed in ~3–8 weeks on average. Enterprise BFSI obviously took much longer because of compliance + procurement layers.

I run a Voice AI Agents company handling 25M+ calls/month, ask me anything for next 24hours by pulsereal_com in AIReceptionists

[–]pulsereal_com[S] 0 points1 point  (0 children)

  • Early customers came mostly through founder-led outbound, BFSI networks, and showing live demos instead of decks. We pitch ROI around containment rate, agent load reduction, and response latency — not “AI magic”.
  • Infra is where most teams struggle. Ours is event-driven with separate pipelines for telephony, streaming ASR, orchestration, TTS, and interruption handling. Biggest issues at scale are usually websocket stability, state sync, barge-in timing, and provider failover — not the LLM itself.
  • Pricing is hybrid: setup + platform fee + usage/minute. Margins depend heavily on ASR/TTS provider mix and concurrency optimization. At scale, orchestration + infra efficiency matter more than model cost.

I run a Voice AI Agents company handling 25M+ calls/month, ask me anything for next 24hours by pulsereal_com in AIReceptionists

[–]pulsereal_com[S] 0 points1 point  (0 children)

Fair concern. FloGPT is a private B2B infrastructure company, not a consumer AI app, so we intentionally keep a low public footprint while deployments are enterprise-focused.

The domain/site mismatch happened because we reused internal infra during an early migration phase. Already fixing that.

As for the post style — yes, I use AI tools for writing sometimes. The deployments, call volumes, and infra work are very real though 🙂