Insights from SlatorPod: The Future of Frontline Data
We recently had a fantastic conversation with SlatorPod, where our Co-Founder and President, Amir Haramaty, shared aiOla’s unique approach to solving the most persistent problem in enterprise technology: uncaptured data.
Amir introduced himself not as a serial entrepreneur, but as a serial problem solver, focused on one core challenge: most critical enterprise data remains uncaptured, unstructured, and unused, severely limiting the impact and ROI of modern AI initiatives.
Watch the Full SlatorPod Interview
The Enterprise Challenge: Why Traditional Speech Tech Fails
For large organizations, reliable data capture is hampered by the chaos of the real world. Traditional Speech-to-Text (ASR) technology simply fails when the stakes are high.
Amir emphasizes that general models break down in true enterprise workflows due to three key factors:
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Acoustic Chaos: Background noise in manufacturing floors, airport tarmacs, or field sites.
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Multilingual Complexity: Accents, regional dialects, and code-switching between languages.
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Hyper-Specific Jargon: The unique terminology of any given industry (e.g., aviation, pharmaceuticals, automotive).
aiOla tackles this challenge by building workflow-specific language models that don’t attempt general conversation but focus on narrow processes, ensuring the highest level of precision in niche environments.
From Speech-to-Text to Workflow-Ready Structured Data
The fundamental difference in aiOla’s approach is the outcome. We don’t produce a messy text transcript; we create actionable, structured intelligence.
Amir explains that aiOla converts natural, multilingual speech not into text, but directly into structured, schema-ready data. This process allows organizations to:
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Automate Workflows: Trigger actions and updates across core enterprise systems instantly.
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Improve Compliance: Ensure data is complete, auditable, and accurate at the point of capture.
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Identify Trends: Surface insights and anomalies long before manual processes could catch them.
This shift dramatically drives efficiency across frontline roles, cutting multi-hour procedures down to minutes and creating previously unavailable datasets that feed enterprise intelligence. We routinely demonstrate significant ROI examples in industries like supermarkets, airlines, manufacturing, and automotive.
Scaling Transformation: The Power of Strategic Partnerships
After proving the value of our technology, the fastest path to market dominance was through strategic partnerships.
aiOla actively collaborates with major firms already embedded in global enterprise digital transformation. We proudly partner with leaders like UST, Accenture, Salesforce, and Nvidia.
Amir calls this channel strategy a force multiplier that achieves several strategic goals:
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Shortens Sales Cycles: Embeds aiOla directly inside broader modernization initiatives.
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Replicates Success: Allows us to quickly replicate proven wins across thousands of clients globally.
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Integrates Domain Expertise: These partners bring deep industry and domain expertise that aiOla deliberately chose not to build in-house.
The Future is Agentic AI, Powered by Voice
Looking ahead, Amir outlines aiOla’s future priorities, including product-led growth and the development of speech-prompted AI agents.
He predicts that Agentic systems will rely heavily on high-quality spoken data. When AI agents are tasked with autonomous execution, the quality and structure of the input data are paramount. By solving the challenges of capturing precise, workflow-ready data from the frontline, aiOla’s role becomes even more central to the future of Agentic AI.
The future of voice relies on the ability to convert natural, multilingual speech not into text, but directly into workflow-ready, structured data that fuels automation, delivers fast ROI, and eliminates slow and costly manual data entry.
