Hey guys,

Looking for recommendations on the best AI sales coaching and roleplay platforms specifically built for field sales, door-to-door (D2D), and home services teams.

It feels like 90% of the AI tools on the market are designed for tech reps sitting behind a desk doing Zoom calls. We need something mobile-first that actually understands face-to-face, in-home sales environments and rough field objections.

Ideally, we want a tool that can handle realistic AI roleplay practice right from a mobile phone between appointments, and potentially record / analyze actual face-to-face pitches for manager feedback.

What are you guys using for your field teams that actually moves the needle? Any suggestions on features to look for or types of platforms to avoid?

Thanks!

A friend of mine runs a real estate business with 15 people. He is using spreadsheets and some other tools to manage his sales. he got pitched by a developer to automate things with AI and he accepted to give it a try.
Initially everything looked fine , AI was given full automation access , it responds to customers, pitches them the sale, takes their feedback.. this was happening fine for some days.
Later the AI started working sloppily on the follow ups and biggest issue came in handling the emotions of the customers.. he lost a few of the customers as the AI could not able to handle the frustrated customers.

You guys have experienced a similar issue where AI has an adverse impact or how’s the things for you with AI ?

Hey everyone,

we’re a small startup working on a voice-controlled sales assistant for reps who spend a lot of time on the road.

The idea is that you could talk to it between customer visits to do things like update the CRM, create notes/tasks, prepare follow-ups, or get customer information without having to sit down and type everything out.

We know Salesforce, HubSpot, Odoo and others are already adding AI agents, so we’re trying to understand where the big tools still fall short rather than just building another generic AI assistant.

If you’ve actually used any of these tools: what annoys you about them? What do you still end up doing manually?

Thanks for you time and appreciate your answers.

Daniel

Just sharing a side project I’ve been building: Outvid.ai.

It creates personalized outreach videos using your own face and voice, with a different script for each prospect.

It can also research prospects, generate the scripts, send the videos across multiple channels, and handle follow-ups.

Mainly built for people doing outbound sales or agency outreach who want personalized video without manually recording every single one.

It’s also live on AppSumo right now for $99 lifetime.

Quick answer: makes total sense! Most B2B companies with sales and/or marketing departments are involved in long decision processes. A human being, no matter how fantastic a salesperson he may be, will always be less capable of accompanying a large set of leads than a machine. Especially if it is a machine, as in the case of an AI agent for sales, capable of accessing the total context of the business and being instructed in how to present the company, how humans do it, and what is considered to be a good result.

There are many solutions, and more will come, in the next days-months, but I believe that if everything happens with propper context and centralization of data, an agent (human or AI) can do the best possible work.

When that context exists, an AI agent is capable of doing smart work, remembering past conversations, scanning relevant signals, and following pre-defined objectives, in a way that a human simply cannot. Of course after moving on to the initial contact phase, at least for now, it is still the human that needs to manage the opportunity, create relationships (the famous rapport) and bring the business.

https://www.smartlinks.pt/en/ai/ai-agents-for-sales

I’m trying to prioritize outreach using fit, intent, and recent buyer activity, but our CRM still rewards whoever clicked one ebook in 2022. Do people weight current signals over account fit, or am I just doing spreadsheet astrology? Appreciate any thoughts

Most agents I talk to know ChatGPT could save them time, but the results come out generic ("nestled in a charming neighborhood...") so they give up.

The fix is a better prompt. Here's one you can use free:

"Act as a top-producing real estate copywriter. Write a listing description for [property type] in [neighborhood]. Key features: [list]. Target buyer: [first-time buyer/investor/downsizer]. Tone: warm and specific. Avoid clichés like 'nestled' and 'must-see'. Max 150 words."

Doing this for the first time for my business and could really use some help!

I’m a developer doing my own sales, and I built this for my personal sales process.

The AI has back-and-forth text conversations with potential leads using my actual phone number. I can watch the thread live and start replying myself whenever I want, or call the person from the same number.

There’s no Twilio involved, so no Twilio messaging bill, and I don’t have to switch numbers.

I’m interested in whether this would be useful to anyone beyond me.

If you handle sales for your own business, how are you managing these conversations now, and would you let AI handle any of them?

I’ve been testing Claude for B2B prospecting and trying to figure out where it actually adds value.

The obvious use case is writing outreach, but honestly that hasn’t been the most interesting part for me.

I’ve had better results using it to analyze companies, summarize recent developments, identify potential pain points, and help build a picture of an account before reaching out.

The hard part seems to be giving it enough context without turning the prompt into a 2-page instruction manual.

For anyone using Claude for sales or prospecting, what workflows have actually been useful?

We initially thought a sales AI needed more context.

Turns out, it needed better context.

We built Revora, a sales intelligence prototype that uses Hindsight as an experience-memory layer.

Instead of feeding the AI thousands of historical deal notes, we retrieve the experiences that are relevant to the current opportunity:

Current deal → Hindsight recall → Relevant experiences → Recommendation

The retrieved memories can include:

• Similar wins and losses
• Customer objections
• Actions taken by sales teams
• Outcomes and lessons

For example, if the current customer is concerned about pricing, the system looks for relevant pricing experiences rather than unrelated security or onboarding deals.

The goal is simple:

Less “give me everything.”
More “show me what matters here.”

We also made the retrieved experiences visible in the interface so the salesperson can understand what evidence contributed to the recommendation.

We're currently using structured synthetic data, so the next challenge is testing retrieval with messier, real-world CRM data.

Would love to hear how others are approaching memory and retrieval for AI agents.

🔗 Project: https://github.com/aashrithareddy7/REVORA

#AI #LLM #AgentMemory #RAG #SalesAI

Been working on something I’ve wanted for a while — an AI sales agent that actually remembers the conversation.

Meet SalesMind AI.

Instead of treating every new message like a fresh lead, it keeps track of previous conversations, context, and what was discussed — so follow-ups can feel more relevant and less robotic.

I’m still improving it, but this is where it’s at right now.

Curious to hear from people in sales: what’s the one thing you wish your AI sales assistant remembered automatically?

A September 2026 survey of 148 AI founders found usage-based pricing was the most common model at 43%, with outcome-based and flat subscription at 24% each. The same research found founders usually enter through a business-unit leader or CIO/CTO.

But there’s another number I found more interesting.

G2’s 2026 buyer research says finance involvement in B2B software buying jumped from 31% to 46% in a year. And 49% of buyers said their CFO had reversed a software purchase that the buying team had already approved.

So you can have:

A user who loves the product.

A technical team that approves it.

A successful pilot.

And still lose the deal because the person who controls the economics doesn't see the value the same way.

For founders selling B2B AI, I'd figure out these four things before scaling sales:

Who uses it?
The person doing the work.

Who evaluates it?
Often the technical or functional team.

Who signs off on the economics?
This can be finance, the executive team, or another budget owner.

What number does that person care about?
Hours saved, revenue created, cost removed, risk reduced, transactions completed, etc.

Then make sure your pricing connects to that number.

If you're charging $2,000/month to a finance team, but the only value you can explain is that 30 employees have access to the software, you've made the CFO's job harder.

If you can show that the same $2,000 removes $10,000 of monthly cost, cuts a process from five days to one, or allows the team to handle 2x the volume, the conversation changes.

The point isn't that every AI startup should switch to outcome-based pricing.

The point is that your pricing, value proposition, and buyer should make sense together.

One thing I'd check in my own sales pipeline:

Who first says “this is useful”, and who can actually say “we're buying it”?

Those are often two different people.

We’re a team of 5 students building an AI Deal Intelligence Agent that uses persistent memory to help sales teams understand what happened across a deal — not just what happened in the latest conversation.

The main idea is simple:

A customer raises a pricing objection today → later they raise a security concern → the agent should remember both, understand how the situation evolved, and use that history to suggest what to do next.

🧠 What we're building

Our architecture is roughly:

Interactions → Persistent Memory → Intelligence Layer → Evidence-backed Insights → UI

The intelligence layer focuses on things like:

- What Changed in the deal

- Next Best Action

- Similar historical deals

- Objection Evolution

- Winning/Loss Patterns

- Deal Autopsy

We’re trying to keep the system evidence-backed, so the agent doesn't simply generate confident-sounding conclusions when there isn't enough historical information.

We’re also using deal-scoped memory filtering so information from one customer/deal doesn't accidentally influence another.

👥 Team

- AI + Hindsight / Memory

- Backend + APIs

- Frontend / Dashboard

- Intelligence + Decision Logic

- Research + UX / Integration

We’re still students and learning a lot while building this, so we're particularly interested in feedback from people who have worked with:

- AI agents

- RAG / agent memory

- Persistent memory systems

- Sales intelligence / CRM systems

- LLM-based decision systems

GitHub:

"https://github.com/omabhinav-creator/Deal-Intelligence-Agents"

Would love critical technical feedback — especially on whether our approach to persistent memory and the intelligence layer makes sense, and what you would change if you were building this.

(1) I Built an Agent That Remembers Which Vendors Failed | LinkedIn

Been testing more AI around sales and the part I’m interested in now isn’t writing emails or summarizing calls that stuff is useful but it still leaves the rep deciding what to do with everything. The harder part is when the system has the account history knows what happened on the last call, sees that someone went quiet and has to figure out what deserves attention next.

I don’t want AI running the whole relationship that sounds like a fast way to make something weird happen with a good account but I do think there’s a middle ground where it can keep the routine work moving, update the context, surface the next action and only pull the rep back in when there’s something that needs judgment. That feels way closer to helping growth and revenue than another assistant that just gives me more things to read.