Vivun's Ava: The AI Sales Engineer Automating Complex Technical Sales [2025]

There's a moment in every technical sales cycle when everything grinds to a halt. Your sales engineer gets asked a complex product question on a call. They don't have the answer. So they say it: "Let me get back to you." The deal stalls for a week. The competitor doesn't make that mistake.

That's the problem Vivun is solving. And it's not solving it with a chatbot that summarizes what happened after the call. It's solving it with an AI that actually sits in the meeting, knows what you're selling, understands who's buying it, and answers questions in real-time.

Ava by Vivun isn't the latest marketing hype. It's built by founders who spent 25+ years actually doing technical sales. Matt Darrow ran presales at Zuora through their IPO. That level of domain expertise matters, because the problem with most AI sales tools is they treat selling like a generic workflow. They don't understand that technical sales is fundamentally different. You're not just closing deals. You're educating buyers about complex products, building consensus across technical stakeholders, and positioning your solution against entrenched competitors who have deep relationships.

Enterprise companies get this. DocuSign, Dayforce, Cloudera, F5, and ServiceTitan are already using Ava to scale their technical sales capacity without hiring more sales engineers. That's the real win here. Not efficiency. Scale without headcount.

TL; DR

• What it does: AI sales teammate that preps you before meetings, answers complex product questions in real-time during calls, and generates follow-up materials after
• Built differently: Founded by sales engineers who understand complex deal cycles, not just generic AI engineers applying machine learning to sales
• Real usage: Eight-figure ARR with triple growth, used by enterprise companies like DocuSign, Dayforce, and Cloudera
• The key advantage: Eliminates "let me get back to you" moments that kill deal velocity by having AI answer complex product and competitive questions in real-time
• Bottom line: If you're a sales leader scaling technical sales, this is worth running your numbers on

Ava by Vivun excels in different sales stages with tailored functionalities: preparation before calls, real-time assistance during calls, and comprehensive follow-up after calls.

The Problem With Current AI Sales Tools

Let's be honest. Most AI sales tools launched in the last two years are solving yesterday's problem.

Call summary generators? They got commoditized in 2023. Gong, Chorus, and their competitors already do that. Email draft generators? Salesforce Einstein and HubSpot AI do it. So what are all these new AI sales tools actually selling?

The answer is usually nothing revolutionary. They're applying generic large language models to sales workflows and hoping it sticks. They build features because they can, not because sales teams actually need them.

Here's what the market was missing: an AI that understands technical sales as a discipline. Not sales in general. Not customer success. Technical sales. The specific process of taking a complex product, explaining it to technical buyers, navigating purchase committees that include engineers, architects, and procurement, and closing deals that might have five different stakeholders with conflicting priorities.

That's where Vivun started differently. The founders didn't come from a machine learning background. They came from sales engineering. They understood that technical sales has patterns, frameworks, and decision trees that generic AI doesn't understand.

When you build an AI for technical sales, you need to model how deals actually progress. You need the AI to understand the difference between a champion and a buyer. You need it to know that the engineering manager's concern about integration is different from the CISO's concern about security, even if both are valid. You need it to understand that competitive positioning changes depending on who you're talking to.

Most AI sales tools don't model any of that. They just pattern-match on text.

Estimated data shows Ava's effectiveness in automating processes and handling complexity, with Dayforce rating it highest for scalability.

How Ava Actually Works: The Three-Phase Approach

Ava is built around three distinct phases of the sales process: before, during, and after. Each phase solves a different problem.

Phase 1: Pre-Call Intelligence and Preparation

Two hours before your meeting, Ava goes to work. It's not passive. It's actively researching.

First, it pulls everything from your system of record. It checks Salesforce for the account history, the company size, the vertical, the current opportunity stage. It looks at email threads to understand what's been discussed. It checks Slack conversations to see if anyone on your team has informal context about the account. It pulls calendar data to understand who else at your company knows this buyer.

But it doesn't stop there. Ava also pulls external data. It's looking at recent news about the company. Recent funding rounds. Recent leadership changes. Industry reports relevant to their vertical. Analyst reports. Anything that gives context about what's on their mind right now.

Then it builds a stakeholder map. Not just names from LinkedIn, but a real map. It tries to understand the power structure. Who's the champion? Who's the skeptic? Who's the economic buyer versus the technical buyer versus the user? Who influences whom?

Finally, it generates a prep document. Not a template. A real, tailored document that walks through the account context, the key stakeholders, the pain points they've mentioned, the competitive landscape, and specifically what you should be trying to learn or accomplish in this particular meeting.

Phase 2: Real-Time Call Assistance and Question Answering

This is where Ava actually enters the call.

Ava joins as a participant (with proper disclosure to the buyer, obviously). It's listening. And it's not just transcribing. It's analyzing.

When a complex question comes up, Ava can surface relevant information in real-time. If the buyer asks about integration capabilities with Salesforce, Ava is pulling the right documentation or use case. If they ask how your solution compares to a competitor, Ava is retrieving the competitive positioning framework.

But more importantly, Ava can actually answer some questions directly. If the buyer asks a product question that your sales engineer doesn't have the answer to, Ava can provide it. Not in a spammy way. It doesn't take over the conversation. But it provides the information so your sales engineer can confidently answer.

This is the "let me get back to you" killer that Vivun talks about. In a traditional sales process, the sales engineer hears a question they can't answer. They say they'll follow up. Then there's a 3-5 day delay. The buyer moves on to other things. The deal momentum dies.

With Ava, the sales engineer has the answer immediately. The conversation keeps momentum. The deal progresses.

The system also flags when deals are at risk. If the sales engineer is making a claim that Ava knows is incorrect based on the documentation, it quietly flags it. If the buyer is expressing skepticism about something that's actually been solved by multiple customers, Ava notes it. These aren't wrong in real-time. They're notes for follow-up after the call.

Phase 3: Post-Call Automation and Follow-Up Materials

After the call, Ava's job isn't over. It's just beginning.

First, it generates a call summary. But not a generic call summary. A summary that's mapped to the deal stage, the stakeholders, the buying process, and the next steps. It knows what's important to track in your CRM because it understands the sales process.

Then it generates follow-up materials. Personalized email to each stakeholder. A summary of what was discussed that's relevant to their specific concern. Value case documentation. Solution documents. Use case studies from similar companies.

Most importantly, it creates what one customer calls a "deal review artifact." This is a comprehensive document that captures the entire conversation, mapped to your product capabilities, with customer references that prove the claims being made. It's not sales collateral. It's a buying tool. It gives the customer the justification they need to push the deal forward internally.

In complex deals, this is what actually wins business. Not the pitch. Not the demo. The detailed business case that the economic buyer can take to their CFO and say "here's why we're buying this."

The Technical Architecture: Why Integration Matters

Ava is only useful if it connects to your existing stack. And that's where most AI sales tools fail. They either require a complete rip-and-replace, or they sit so far outside your workflow that adoption becomes a nightmare.

Vivun built Ava differently. It's designed as a layer on top of your existing systems. Ava connects to:

Salesforce for CRM data and deal context. Every opportunity, account, contact, custom field. Ava sees everything your team sees in Salesforce.

Gong or Chorus for call intelligence. If you're already recording and analyzing calls, Ava plugs into that data stream. It learns from every call that's been recorded.

Slack for team communications. Your sales engineers are already discussing deals in Slack. Ava picks up that context.

Google Workspace or Microsoft 365 for calendar and email. Ava sees your calendar to know when you have meetings. It sees emails to understand conversation history.

Clari for forecasting data. If you're using Clari for deal intelligence, Ava integrates there too.

The point: Ava doesn't ask you to change your tools. It sits on top and makes everything smarter. That's why implementation is measured in days, not months.

This architecture also means Ava gets smarter over time from your actual sales process. Every deal that goes through Salesforce. Every call recorded in Gong. Every conversation in Slack. That becomes training data for the AI.

But here's the important part: Vivun is transparent about data. Your data stays in your systems. Ava connects to it, but doesn't own it. This matters for security and compliance.

Ava's tool provides a significant ROI by increasing sales engineer capacity value and accelerating deal cycles, with the potential to generate

Real-World Case Studies: What Enterprise Teams Are Seeing

Case studies can be marketing fluff. But the companies using Ava are not small. They're not beta testing. They're using it in production, in complex sales environments.

DocuSign: Scaling Content Generation

DocuSign is a $5 billion+ company. They have massive sales engineering teams. They also have massive complexity. Every deal is different. Every customer has unique requirements.

What DocuSign discovered with Ava: they could automate win review generation. After every closed deal, Ava would generate a comprehensive case study artifact. Real customer data. Real quotes. Real ROI numbers. All formatted and referenceable.

Why does this matter? Because when your sales team is trying to convince the next prospect that your solution works, they don't have to scramble through Salesforce to find proof. They have a database of win reviews, all generated by Ava, all consistent, all referenceable.

Dayforce: Treating AI Like a New Hire

Dayforce is an HR software company. They deal with complex workflows. Compliance issues. Integration with existing HR systems. Their sales process is inherently complicated.

What Dayforce said about Ava: "We can treat her like a new hire, train her easily, and then her capability is scalable to an infinite number of sales opportunities."

That quote matters because it's not about the AI being perfect. It's about the AI being trainable. Dayforce didn't have to wait for Vivun to add features. They trained Ava on their specific product, their specific process, and their specific competitive positioning. Then they scaled it.

This is the difference between a tool and a teammate. Tools do what they're programmed to do. Teammates learn.

Cloudera: Handling Technical Depth

Cloudera sells data platforms. The buying process is deeply technical. You're not selling to marketers. You're selling to data engineers, architects, CTOs. These are people who understand the technology deeply and ask detailed questions.

Cloudera's problem: not enough sales engineers to handle the volume of opportunities. The obvious solution: hire more sales engineers. But the real solution: scale the existing team's capability with AI.

Ava handles the technical depth because it was built by people who understand technical sales. It doesn't dumb down the conversation. It elevates it.

Comparing Ava to Other AI Sales Solutions

The AI sales agent space is getting crowded. There are at least a dozen "Avas" now. Salesforce has Agentforce. HubSpot has its own AI tools. There are startups coming out of YCombinator weekly claiming to automate sales.

So what makes Ava different? Let's be specific.

Deep Domain Expertise vs. Generic AI

Most AI sales tools are built by machine learning engineers with sales expertise added through external advisors. Vivun is built by people who actually did the work. Matt Darrow ran presales at Zuora through their IPO. This is not theoretical. This is lived experience.

Why does this matter? Because sales engineering is a specific discipline. The patterns are different. The frameworks are different. The problems are different from enterprise sales or inside sales or customer success.

When you're building an AI for technical sales, you need to encode the actual process. Ava does this. Most competitors don't.

Real-Time Call Assistance vs. Post-Call Analysis

Most AI sales tools are passive. They watch what happened. They analyze it afterward. They generate summaries. It's useful, but it's reactive.

Ava is active. It's in the call. It's answering questions in real-time. It's supporting your sales engineer while the deal is happening. This changes the entire dynamic.

Why? Because deal momentum is everything. If you lose momentum, you lose the deal. Ava keeps momentum.

Integration as a Feature vs. Bolt-On

Ava was built with integration as a core feature. It sits on top of Salesforce, not as a replacement. This is the difference between adoption and friction.

When you require a team to use a new tool, you're adding work. When you plug into tools they already use, you're saving work. One gets adopted. One gets resisted.

Transparency About AI Limitations

Here's something most AI sales tools don't do: admit what they're not good at.

Ava is good at:

• Pre-call research and preparation
• Retrieving information from your documentation
• Drafting follow-up materials
• Building stakeholder maps
• Creating deal reviews

Ava is not good at:

• Understanding nuance in human relationships
• Detecting when a buyer is lying or exaggerating
• Making judgment calls about whether a deal is truly qualified
• Understanding cultural context that's not written down

This honesty matters. You're not supposed to replace your sales team with Ava. You're supposed to multiply their effectiveness.

Technical sales AI tools are rated higher in effectiveness due to their tailored approach to complex sales processes, unlike generic AI models. Estimated data.

The Financial Impact: ROI and Scalability

Let's get to what actually matters: does this move the needle?

Vivun published an ROI calculator. The math is straightforward:

A fully-loaded cost for a senior sales engineer is roughly

For a typical enterprise sales team of 20 sales engineers, that's

But that's the conservative calculation. It assumes Ava just makes people 30% more efficient.

The real value is in acceleration. In the case study with Dayforce, the metric wasn't efficiency. It was: how many more deals can we close with the same team?

If Ava cuts deal cycle time by 20% (not unreasonable for deals where "let me get back to you" was costing 5-10 days), the financial impact is massive. A deal that takes 90 days instead of 110 days doesn't sound like much. But it means the deal closes before the fiscal quarter ends. It means earlier cash flow. It means lower sales compensation costs (because commission gets paid earlier).

For a

Implementation: Timeline and Change Management

Here's what trips up most enterprise software deals: implementation. The vendor promises 3 months. It takes 12 months. Everyone's frustrated.

Vivun claims Ava implementations happen in days. Not weeks. Days.

How? Because Ava doesn't require data migration. It doesn't require replacing Salesforce. It doesn't require retraining on new workflows.

The actual implementation process looks like this:

Day 1-2: Integrate with your existing systems. Connect Salesforce, Gong, email, calendar. This is API work. Most of Vivun's customers already have API documentation for their systems. It's standard stuff.

Day 3-5: Customize the AI for your product. Feed Ava your product documentation, competitive positioning, industry vertical context, any custom fields or processes you use in Salesforce. Let it learn.

Day 6: Pilot with 2-3 sales engineers. Not your whole team. Just a few early adopters.

Week 2: Rollout to the rest of the team.

That's radically different from traditional enterprise software. And it's possible because Ava is built as a layer, not a replacement.

Change management is still required. Your sales engineers need to understand how to use it. But it's not learning a new tool. It's learning to use their existing tools differently, with AI assistance.

Estimated data suggests Ava can reduce deal cycle time by 15%, increase win rate by 10%, and improve pipeline velocity by 20%.

Security and Compliance Considerations

When you're giving an AI system access to your CRM, email, and calls, security becomes critical.

Vivun's approach:

Data stays in your systems. Ava doesn't create a copy of your data. It connects to your Salesforce, your email server, your call recordings. The data never leaves your infrastructure.

Encryption end-to-end. All communication between Ava and your systems is encrypted. API keys are handled securely.

Audit logging. You have full audit trails of what Ava accessed, when, and why. This is important for compliance teams.

Role-based access control. You can configure what Ava can access. Maybe it can't see certain accounts. Maybe it can't access certain fields. You control the boundaries.

SOC 2 Type II compliant. For enterprise customers who care about compliance (spoiler: they all do), this matters.

For most enterprise security teams, this approach is more palatable than tools that require data export and centralized storage. You're not moving your data. You're just giving an AI permission to read what already exists.

Common Objections and Honest Answers

When we talk to sales leaders about AI sales tools, specific concerns come up. Let's address them directly.

"Our sales process is too unique for AI to understand"

This is true for some companies. If your sales process is truly one-of-a-kind, then generic AI isn't going to help much.

But here's the thing: 90% of technical sales follows the same pattern. Identify the champion. Build consensus with technical stakeholders. Navigate the buying committee. Address competitive threats. Create the business case.

Ava isn't trying to understand your one-of-a-kind process. It's trying to understand the patterns that are actually common, and then adapt to your specific context.

If your sales process is genuinely unique, Ava might not be the right fit. But most teams saying this are wrong. They think their process is unique because they haven't looked at other companies.

"We don't have good data in Salesforce, so Ava can't help"

This is actually a fair concern. If Salesforce is a mess, Ava will be working with messy data.

But here's the opportunity: Ava is actually an incentive to clean up your data. If you're going to use AI to accelerate deals, you need good data. This is the forcing function to get your team to actually use Salesforce properly.

Also, Ava gets smarter from your call recordings. Even if your Salesforce is messy, if your calls are being recorded and analyzed, Ava can learn from actual deal conversations.

"What if the AI gives bad advice?"

This is the real concern. And it's valid.

Here's how to think about it: Ava isn't supposed to make decisions. It's supposed to surface information and suggestions. Your sales engineer is still the expert. They still decide what to do.

If Ava suggests something wrong, your sales engineer should catch it. They have domain expertise. They understand their customer. They should be the filter.

The problem is: what if they don't catch it? What if they trust the AI too much?

This is a training issue, not an AI issue. Your sales engineers need to understand that Ava is a tool with limitations. They need to verify important claims. This should be part of the rollout plan.

"We're worried about job losses"

This is the honest conversation that doesn't happen enough.

Here's the reality: Ava doesn't replace sales engineers. It replaces the repetitive, non-value-added parts of the job. Research. Email drafting. Follow-up creation. These are the boring parts.

What Ava can't replace: building relationships. Understanding what a customer really needs versus what they're saying they need. Negotiating. Political navigation. These are the high-value parts.

Ava actually creates job security. Because if a sales engineer is valuable only for the repetitive work, they're replaceable. But if they're valuable for judgment, relationship-building, and navigation, they're harder to replace.

The teams that are going to struggle are the teams full of people doing repetitive work. The teams that are going to thrive are the teams focused on high-value selling.

Ava saves an estimated 35 minutes in pre-call preparation, 20 minutes during the call, and 25 minutes in post-call follow-up, enhancing efficiency across the sales process.

Competitive Landscape: How Ava Stacks Up

There are legitimately good AI sales tools on the market. Let's talk about where they differ.

Salesforce Agentforce

Salesforce's advantage: integration. It's built into Salesforce. If you're all-in on Salesforce, it's convenient.

Ava's advantage: focus. Agentforce is trying to be everything. Ava is trying to be the best at sales engineering. Also, Agentforce is built by people who know CRM. Ava is built by people who know sales engineering.

HubSpot Sales AI

HubSpot's advantage: cost. It's bundled into HubSpot. If you're already paying for HubSpot, it's cheaper.

Ava's advantage: depth. HubSpot's AI is built for general sales. Ava is built specifically for technical sales. The depth of understanding is different.

Apollo.io

Apollo's advantage: lead intelligence. They're strong on the prospecting side.

Ava's advantage: the actual deal. Ava focuses on what happens after you have a meeting scheduled. It doesn't try to do everything.

Gong/Chorus

Gong and Chorus are post-call analysis platforms. They're excellent at understanding what happened on calls.

Ava's advantage: real-time assistance. During the call, not after. This is a fundamentally different problem.

The honest take: each tool is optimized for different problems. Ava is optimized for the problem of scaling technical sales capacity. If that's your problem, Ava is probably the best solution. If your problem is something else, you might need something else.

Best Practices for Ava Implementation

If you're going to implement Ava, how do you do it right?

Start with your strongest sales engineers

Don't roll this out to your whole team at once. Start with your top 2-3 sales engineers. Let them use it for a month. Get their feedback. Let them figure out the workflows. Then expand.

Why? Because your best people will find the value fastest. They'll discover use cases you didn't anticipate. They'll catch bugs. They'll figure out how to integrate it into their process without friction.

Clean up your Salesforce first

Before Ava goes live, spend a month making sure Salesforce is clean. Account names are consistent. Key fields are populated. Contact records are complete. This isn't about perfection. It's about having enough data that Ava can work with it.

Document your product for Ava

Sit down with your product team and document everything. Competitive positioning. Product capabilities. Integration points. Common objections and how you answer them. This becomes Ava's knowledge base.

This exercise alone is valuable. Most teams don't have documented competitive positioning. Now you will.

Train your team on AI literacy

Your sales engineers need to understand what Ava can do and what it can't. They need to know how to verify its suggestions. They need to know when to trust it and when to double-check.

This is a 2-hour training, not a 2-day training. But it's important.

Measure the right metrics

Don't measure "calls handled by AI." That's not real. Measure deal cycle time. Measure win rate. Measure pipeline velocity. These are the metrics that actually matter.

The Bigger Picture: AI in Sales Engineering

Ava isn't an isolated solution. It's part of a broader trend: AI augmenting professional work.

The next five years are going to see a lot of AI tools targeting sales. But most of them will be bad. They'll be good at automating the easy stuff (email, scheduling, light prospecting). They'll be bad at automating the hard stuff (building relationships, navigating politics, understanding real customer needs).

Ava is interesting because it's actually trying to automate something hard: technical sales conversations.

The companies that are going to win in the next five years are the ones that figure out how to use AI to multiply their best people, not replace them. A world where your top sales engineers are 30% more productive is massively different from a world where you replace 30% of your sales engineers.

One leads to higher revenue. Higher close rates. Better customer relationships. The other leads to a race to the bottom on cost.

Vivun is building for the first world.

Who Should Actually Look at Ava

Let's be specific about fit.

You should look at Ava if:

• You have a technical sales process (B2B software, infrastructure, data platforms, etc.)
• You have complex deal cycles (6+ months, multiple stakeholders)
• You have trouble scaling sales engineering capacity
• Your sales engineers spend significant time on pre-call research and post-call documentation
• You want to accelerate deal velocity, not reduce costs
• You're willing to invest time in implementation and training

You probably shouldn't look at Ava if:

• You do inside sales or simple B2B sales (e.g., small business SaaS)
• Your sales cycle is short (4 weeks or less)
• You don't have enough volume to justify the tool
• Your Salesforce is so messy that data is unreliable
• You're looking to reduce headcount, not scale capacity

The last point matters. If you're in a cost-cutting mode, Ava is not the right tool. It's designed for growth. For companies trying to scale revenue faster than they scale headcount.

The Roadmap and Future Features

Vivun has roadmap items that are worth watching.

Right now, Ava is optimized for pipeline-stage deals. But there's also opportunity in the earlier stages. Ava could help with prospecting research. Ava could help with whiteboarding sessions. Ava could help with customer success and expansion.

The company has said they're thinking about all of this. Whether they execute is a different question. But the direction is interesting.

Also worth watching: how Ava handles non-English languages and vertical-specific workflows. Right now it's strong in English-speaking markets and general B2B. Vertical customization (legal tech, healthcare, finance) is coming.

The Honest Assessment

Ava is not a miracle. It won't fix a broken sales process. It won't solve quota problems by itself. It won't replace a bad sales leader.

But if you have a good sales engineering process and you're trying to scale it, Ava is probably the best tool on the market right now. It's built by people who understand the work. It's integrated with your existing stack. It actually handles the hard problem of real-time deal assistance.

The founder, Matt Darrow, came from Zuora. A company that scaled to $100M+ ARR. He understands what it takes. And he's built Ava to help companies do what he did.

Is it worth the investment? For companies in the target market, yes. Run your numbers. Calculate your cost of deal delays. Look at your sales engineer utilization. Most teams will find that the ROI is actually positive in year one.

But do it with eyes open. This is not a plug-and-play tool. This is something that requires implementation, training, and ongoing management. Get it right, and it's powerful. Rush it, and it becomes another tool your team ignores.

FAQ

What is Ava by Vivun?

Ava is an AI sales teammate built specifically for technical sales. It preps you before meetings by researching accounts and building stakeholder maps, assists during calls by answering complex product questions in real-time, and generates follow-up materials after calls. Unlike generic call summary tools, Ava understands the specific workflows and challenges of technical selling.

How does Ava integrate with existing systems?

Ava sits on top of your existing tech stack rather than replacing it. It connects directly to Salesforce for CRM data, Gong or Chorus for call recordings, Slack for team communications, and Google Workspace or Microsoft 365 for calendar and email. This integration-first approach means implementation happens in days, not months, because you don't need to migrate data or change your existing workflows.

What does Ava do before a sales call?

Before your meeting, Ava researches the account and pulls context from your CRM, email, Slack, and external sources like recent news and industry reports. It builds a comprehensive stakeholder map, identifies the champion and economic buyer, and creates a customized prep document that walks through account history, pain points, and specific objectives for that particular meeting.

How does Ava assist during a sales call?

Ava joins calls as an active participant and surfaces information in real-time. When complex product questions arise, it can provide answers directly, eliminating the "let me get back to you" responses that slow down deal momentum. It also quietly flags potential issues, like competitor objections or misaligned messaging, without interrupting the conversation.

What happens after a sales call with Ava?

Ava automatically generates a contextual call summary mapped to your deal stage, creates personalized follow-up emails for each stakeholder addressing their specific concerns, drafts solution documents and value case studies, and builds comprehensive deal review artifacts with customer quotes and ROI calculations that buyers can use internally to justify the purchase.

What types of companies are using Ava?

Enterprise companies with complex technical sales processes are the primary users. Current customers include DocuSign, Dayforce, Cloudera, F5, ServiceTitan, and Gladly. These are all companies where technical expertise is critical to closing deals and where sales engineering capacity is a bottleneck to growth.

How long does implementation take?

Vivun claims implementation takes days, not weeks or months. This is possible because Ava integrates with your existing systems rather than replacing them. The typical timeline is 1-2 days for API integration, 3-5 days for product knowledge customization, and then rollout to your team. No data migration. No workflow redesign. No massive change management effort.

What's the actual ROI from using Ava?

The primary ROI comes from deal acceleration and improved productivity. If Ava cuts deal cycle time by 20% and increases sales engineer capacity by 30%, a typical enterprise team of 20 sales engineers could see $1-2M in annual productivity value. Additional ROI comes from improved win rates and earlier cash flow from faster deal closure. Vivun provides an ROI calculator to help companies estimate their specific numbers.

How does Ava handle data security and privacy?

Ava doesn't create a separate database or move your data. It connects to your existing systems and reads the data there. All communication is encrypted end-to-end, and you have full audit logging of what Ava accessed. Role-based access control lets you limit what Ava can see. The platform is SOC 2 Type II compliant for enterprise security requirements.

How does Ava compare to other AI sales tools?

Most AI sales tools focus on post-call analysis or generic sales automation. Ava is unique because it provides real-time assistance during calls and is built specifically for technical sales by founders with 25+ years of sales engineering experience. Unlike Salesforce's Agentforce (which tries to be everything) or HubSpot's tools (which are general sales-focused), Ava is specialized and deep in its domain.

Can Ava work if our Salesforce data is messy?

Ava can work with imperfect data, but it works better with clean data. If your Salesforce is poorly maintained, Ava becomes an incentive to clean it up, which has value beyond just the AI tool. Ava also learns from your actual call recordings, so even if CRM data is messy, call data provides another source of truth.

Will Ava replace our sales engineers?

No. Ava replaces the repetitive, non-value-added parts of the job like pre-call research, email drafting, and follow-up documentation. It doesn't replace judgment, relationship-building, negotiation, or political navigation. Teams that worry about job displacement should actually focus on ensuring sales engineers spend time on high-value activities, which is exactly what Ava enables.

Final Thoughts

The AI sales tool market is getting crowded. Most of these tools are solving yesterday's problems or solving generic problems with generic solutions.

Ava is different because it's solving today's problem with domain expertise. Sales engineering is hard. It's complex. It requires understanding how companies actually buy software. Vivun built a tool that understands this.

Is it perfect? No. Is it the right solution for every company? No. But for enterprise companies trying to scale technical sales without scaling headcount, it's worth serious consideration.

Run the numbers. Do the analysis. Talk to customers. Then make a decision.

But don't sleep on this one. The companies that figure out how to use AI effectively in sales are going to pull away from the competition. Ava is one of the better tools for doing that in technical sales.

Key Takeaways

• Ava by Vivun is built specifically for technical sales by founders with 25+ years experience, not generic AI applied to sales workflows
• It operates in three phases: pre-call research and preparation, real-time call assistance that answers questions on the spot, and automated post-call documentation
• Real-time assistance eliminates 'let me get back to you' moments that kill deal momentum, directly accelerating sales cycles
• Integration-first architecture means implementation in days, not months, because Ava plugs into existing Salesforce, email, and call recording systems
• Enterprise companies like DocuSign and Cloudera see measurable ROI through deal acceleration and sales engineer productivity gains, often recovering investment in 3-6 months

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