Scale Without AI Engineers: Build AI Agents That Work

🎤 Full Interview: Tyler Postle, Co-founder & CEO @ Voker AI

"Empowering product teams to build sophisticated AI capabilities without needing full AI engineering—unlocking AI for SMBs and reshaping product development."

Founder Story & Vision

Who they are & what they’re building

Tyler Postle is a data scientist turned entrepreneur who, after leading AI and analytics at high-growth startups, co-founded Voker AI —a Y Combinator-backed platform designed for SMBs lacking AI engineering teams. Voker empowers product managers and business experts to rapidly design and deploy AI agents through an intuitive no-code UI, bridging the gap between AI possibility and operational reality.

Why now & what’s the big bet

With AI technologies evolving rapidly, most SMBs struggle to build AI solutions due to costly engineering talent and complex infrastructure. Tyler’s big bet: by lowering the technical barrier, enabling subject experts to configure AI workflows with built-in guardrails, and simplifying deployment through API integrations, Voker can accelerate AI adoption among SMBs and transform how companies build intelligent products.

Connect with Tyler Postle

LinkedIn | Website

🧩 Real-World Use Cases

How it works in the wild

Healthcare Workflow Optimization: Clients in veterinary tech use Voker to automate generating accurate, standardized post-call notes from vet-patient conversations, saving 15-20 minutes per consult.

Continuous Model Experimentation & Improvement: Voker’s platform supports A/B testing and iterative tuning of AI agent prompts to optimize accuracy and minimize errors, ensuring robust and reliable deployment at scale.

What you’ll learn:

How Tyler recognized early AI limitations and pragmatically pivoted from complex QA tooling to accessible AI building blocks for SMBs.
The importance of collaborative workflows that empower product and business teams while involving engineers only in final deployment.
Practical approaches to building AI products with clear ROI focus, balancing guardrails with rapid iteration and experimentation.
Insights on navigating early-stage startup challenges, from customer acquisition to pricing strategy in a nascent market.
Broader perspectives on evolving AI trends, such as model costs declining, rising multi-model support, and adopting “computer vision” style automation.

Some Takeaways:

Tactic: Prioritize building fast prototypes with clear guardrails and iterate rapidly rather than aiming for perfection initially.
Insight: Empowering domain experts to control AI tooling accelerates adoption and ROI without replacing engineers.
Milestone: Handling hundreds of millions of API calls monthly reflects Voker’s scalability and reliability.
Approach: Combining no-code agent design plus developer-friendly API deployments bridges technical gaps effectively.
Next Step: Founders should evaluate where manual work exists today ripe for AI-driven automation and start experimenting with accessible platforms like Voker.