Podcast Summary: Just Now Possible

Host: Teresa Torres
Episode: Building AI Sales Reps: How ShowMe Orchestrates Voice, Video, and Multi-Agent Workflows to Close Deals
Date: February 19, 2026

Overview

In this episode, Teresa Torres interviews Yuri (CEO) and Kike (Lead Product Engineering & Customer Success) from ShowMe—a company building AI-powered digital sales reps, primarily for inbound sales organizations. The discussion chronicles how ShowMe spotted this opportunity, built their initial prototypes, iterated through real-world deployment, and architected their multi-agent, multi-modal workflows to close deals efficiently. The episode is a deep dive into product discovery, agent orchestration, technical trade-offs, and the ongoing challenge of evolving AI sales tools with the market.

Main Discussion Points

1. Origin Story and Product Focus

• AI-First from Day One:
  • ShowMe was founded in April 2025 as a natively AI-focused company, directly inspired by their prior experience noticing website visitors not converting unless a human interacted with them.

    "We actually started the company April last year, April 2025. The idea came from...a lot of people were coming to our website and...not converting. What we found is that when we were able to put one of our sales reps...in front of our visitors...they basically converted." — Yuri [04:00]

• Inbound > Outbound:
  • The team intentionally specialized in inbound sales, contrasting with "spammy" outbound practices that degrade user experience.
• Role of AI Digital Workers:
  • ShowMe’s sales reps have two skill areas: sales skills (video calls, calls, demos) and operational skills (reporting, performance metrics, etc.), designed to function as a co-worker within the client’s sales team.

2. First MVP and Early Insights

• MVP Construction:
  • Launched within weeks; focused on demoing products with basic video, voice Q&A, and manually ingested documentation.

    "You had some videos. You could select which video you wanted to see...and then there was an agent...talking about...the product...Very simple, Very clunky as well. But yeah, that was the first version." — Kike [08:26]

  • Early user surprise: even basic voice interaction was novel mid-2025.
• Manual Foundations, Iterative Process:
  • Early knowledge base was built using RAG (retrieval-augmented generation) from scraped docs, product recordings, and help centers.
  • Rapid learning: product needed to support more complex buyer journeys beyond demos—slide presentations, qualification, customer stories, and dynamic guidance.

3. Building Trust & Affordances in AI Sales Agents

• Overcoming Skepticism:
  • Users were initially unsure whether the AI was capable or trustworthy, treating it like a "dumb chatbot."
• Affordances & Interaction Design:
  • They experimented with making the agent look and act more human (e.g., with realistic video avatars via HeyGen, and replicating familiar UIs like Google Meet).

    "When they saw the avatar, it makes them think, hey, so this could have the knowledge of a real person. ...their brain instantly has that connection." — Yuri [15:20]

• Mimicking Human Behaviors:
  • Explicitly designed agents to deflect “connect me to a human” requests by convincingly demonstrating capability, thus winning user trust.

4. System Architecture & Orchestration

• Agent/Workflow Overview:
  • Agents: Primary interface with users—via voice, chat, WhatsApp, email.
  • Workflows: Deterministic processes above agents, handling multi-turn, multi-day engagement journeys (e.g., demo → follow-up email → call).
  • Movement toward more dynamic “orchestrator agents” for less rigid, adaptive workflows.

    "This workflows is like a layer that is above these agents and ... have both deterministic and indeterministic actions." — Kike [21:10] "We're step by step automating and automating each part of the process." — Kike [21:10]

• Coworker Metaphor:
  • Internally and with clients, ShowMe strives for a model where their AI rep behaves—and is interacted with—like a team member, especially via Slack.

5. Multi-Agent Approach: Specialization for Latency, Cost, and Quality

Conversation Agents (Sales Reps)

• Decomposition for Performance:

  • Complex tasks broken into smaller specialized agents for greetings/discovery, qualification, pitching, product walkthroughs, and closing.
  • Orchestration agent routes conversation based on call stage.

    "We build...different agents actually inside it with different prompts. One is going to take over the greetings...one is going to be expert in qualifying..." — Kike [34:16]

• Tool Sets Per Agent:

  • Each agent gets a tailored set of tools (e.g., slide presentation, calendar, Stripe, product videos, knowledge base access).

• Personalization/State Maintenance:

  • System collects and accumulates interaction context (a "dossier") to inform follow-ups and avoid redundant questions.

Creator Agent

• Prompt/Pipeline Construction:
  • Converts customer-provided docs, transcripts, call data into tailored prompts, knowledge base entries, and clean data for conversational agents.

    "The creator is to create the conversational agents...all the documentation...and creating the prompts...plus creating the documents..." — Kike [47:47]

Evaluator Agent

• Automated Assessment & QA:
  • Evaluates each conversation for qualification, next steps, roles, sentiment, and confidence levels.
• Determines When to Hand Off to Humans:
  • Real-time detection of uncertainty or frustration—triggering human intervention if thresholds are met.

6. Training Agents: Generic and Specific Sales Skills

• Two Layers of Skill:
  • Generic sales skills (prompted in): discovery, qualification, pitching, following up.
  • Company-specific skillset learned from call transcripts, onboarding materials, and sales enablement docs.

    "We ask our customers to share with us...transcripts of the calls from their sales teams...And from there...we create for the conversational agents...sales guidance skills for the company." — Yuri [39:43]

• Role-Specific Playbooks:
  • Agents learn which value props and slides to deploy based on customer role/ICP, using extracted rules from customer materials.

7. Evaluating Agent Quality & Customer Rollout Process

• Careful Staged Rollout:
  • Always proves efficacy via POCs, A/B tests, starting with low-value leads, then ramping when trust is established.
• Multi-Phase Evaluation:
  • Phase 1: Customer closely reviews most conversations, provides granular feedback and corrections.
  • Phase 2: Evaluation agent surfaces a subset of at-risk/low-confidence conversations for review, reducing manual effort over time to ~5%.

    "At the beginning, every conversation...is reviewed with the customer...A test is created, and the agent tried to go through that conversation again..." — Kike [52:58]

• Automated Recurring Testing:
  • Feedback is encoded as regression-style tests to prevent prompt regression as the system iterates.
• Reporting & Transparency:
  • Dashboards, Slack alerts, CRM integration allow client teams to monitor agent performance in real time.

8. Technical & Product Challenges

• Balancing Determinism and Flexibility:
  • Striving for enough guardrails to avoid hallucinations, but adding agents (orchestrators) to break rigid workflow logic where flexibility is required.
• Latency vs. Quality Tradeoffs:
  • Use of smaller, faster models for real-time voice; larger models for backend eval and context management.
• Scaling Manual-to-Automated:
  • Many processes began manual (“concierge”) and are incrementally automated based on customer usage and feedback.

9. Patterns & Industry Implications

• Emergence of Agent + Workflow Patterns:

  • ShowMe's architecture echoes that of other advanced AI teams (e.g., Gradient Labs) utilizing persistent state/workflow above specialized agent turns for multi-day tasks.

• Humans in the Loop:

  • Both for building/training the agents and for safety/quality guardrails at runtime.

• Future Differentiation Will Be Customer Understanding:

  • Technical capabilities are rapidly commoditizing; deep discovery and ongoing adaptation to real customer workflows is the durable moat.

Notable Quotes & Memorable Moments

• On demonstrating AI’s abilities:

  "Whenever someone really understood how to guide the AI, how to talk to her and so on, they were really good at extracting the value from it and they were converting... So how we solved this...by basically trying to replicate humans..." — Yuri [15:20]

• On the “affordance” of human-like UI:

  "If we try to make it like a video call, same as Google Meet...people are really used to it and they will understand this is like being in a call with a human.” — Yuri [18:30]

• On decomposing AI complexity:

  “Engineers have always had to be good at decomposition, but now we’re learning it in an LLM way... It's like a new skill of decomposition.” — Teresa [35:55]

• On prompt regression testing:

  “Automatically a test is created, and the agent tried to go through that conversation again with that...feedback...we run that test until it's passing that test. This way, we're creating...a battery of tests with...partial conversations that need to be passed.” — Kike [52:58]

Timestamps for Key Segments

• 00:58 — What ShowMe Does: AI Digital Sales Reps Overview
• 04:00 — Company Origin Story & Market Problem
• 08:26 — First MVP and Manual Knowledge Base Approach
• 13:41 — Expanding Beyond Simple Product Demos
• 15:20 — Building Trust and Human-like Affordances
• 21:10 — Architecture Overview and Workflow Orchestration
• 34:16 — Multi-Agent Decomposition for Sales Conversation
• 39:43 — Training for Sales Skills: Generic vs. Specific
• 47:47 — Evaluator & Creator Agents: Roles and Workflows
• 50:49 — Measuring Quality and A/B Rollout Strategies
• 52:58 — Two-Phase Quality Feedback Loop and Test Regression
• 58:35 — What’s Next: Smart Orchestrator, Automation, and PLG
• 61:22 — The Fast Pace of AI Industry Change and Customer-Centric Discovery

What’s Next for ShowMe

• Technical Roadmap:
  • Evolving the orchestrator for smarter, flexible workflows.
  • Building toward a self-serve, DIY/free trial motion (“PLG”).
  • Automating more of the agent creation pipeline.
• Market Vision:
  • “Digital workers” vision—beyond sales reps into customer success, support, and other roles as driven by customer demand.

Takeaways

• ShowMe exemplifies the “concierge-to-scale” playbook: Start manually, learn from each customer, and automate only what truly creates value.
• **Multi-agent systems and orchestration/workflow layers are emerging as a common AI product pattern for complex, multi-turn, real-world use cases.
• **Human-like affordances—both visual (avatars) and behavioral (sales process fidelity)—are crucial for user trust and adoption.
• **Technical excellence alone won’t win—deep customer intimacy and adaptability in product development are decisive differentiators in the fast-evolving AI landscape.

If you're building with AI and curious how ambitious agent-powered workflows are architected, evaluated, and evolved in production, this episode is essential listening.