SoundHound's Sales Assist Puts Agentic AI on the Shop Floor

Sales Assist retail AI signals a new in-store playbook

Sales Assist retail AI moved from concept to a named product today. SoundHound AI announced Sales Assist, an agentic AI solution designed to help store associates answer questions, explain differences between products, and guide shoppers confidently. The announcement matters because it pushes AI assistance out of the back office and onto the shop floor, where speed, accuracy, and confidence directly affect conversion.

This is the most practical version of “AI in retail” we’ve seen in months: it focuses on helping staff do the job better rather than replacing them. For e-commerce teams, that’s a reminder that in-store and online experiences are converging. If store associates are powered by AI that knows the catalog, your product data strategy can’t stop at the website.

What SoundHound announced (and why it’s relevant)

Sales Assist sits inside SoundHound’s broader agentic AI platform and is positioned as a ready-to-deploy retail solution. The product is designed for fast, conversational access to product knowledge, policies, and store-specific guidance. It’s being introduced publicly at Mobile World Congress 2026 in Barcelona, where SoundHound is highlighting agentic retail use cases.

Why should e-commerce leaders care? Because this kind of assistant blurs the boundary between digital and physical commerce. The same product attributes that power your PDPs and feeds are now expected to answer a shopper’s spoken question in seconds. If your data is incomplete or inconsistent, the in-store AI will be too.

It also signals a shift in how retail AI is packaged. Instead of building a custom chatbot from scratch, teams are being offered an off-the-shelf agent that can plug into existing knowledge bases and product systems. That changes the rollout timeline: faster pilots, clearer ROI conversations, and less tolerance for messy source data. In practice, the success of an in-store assistant won’t hinge on the model alone — it will hinge on how well your catalog and policy data can be unified, normalized, and kept fresh.

Key takeaways for e-commerce and retail teams:

• AI is moving closer to the customer: not just chatbots on the site, but assistance at the point of sale.
• Product detail accuracy becomes a frontline KPI: wrong specs in-store erode trust faster than a typo online.
• Retail associates become AI-augmented sellers: better answers, faster comparisons, and fewer “let me check” moments.

The data readiness gap (and how to close it)

Agentic assistants are only as good as the product data they’re built on. For most retailers, the real challenge isn’t deploying an AI model — it’s standardizing thousands of SKUs that were never meant to answer real-time questions.

If you’re preparing for in-store AI support, prioritize these foundations:

1. Attribute completeness: ensure every SKU has structured specs (dimensions, materials, compatibility, energy ratings, etc.).
2. Variant logic: size and color relationships must be clear so the assistant can compare and recommend.
3. Policy and service data: warranties, returns, assembly options, and installation details need to be searchable.
4. Local availability: inventory and delivery timelines should map to specific store locations.
5. Language consistency: unify naming conventions and units to avoid contradictory answers.

Teams that already invest in product data quality can move faster. If you’re still cleaning feeds manually, tools like Lasso can automate the heavy lifting — importing messy supplier files, standardizing attributes, and filling gaps with AI. See how this works in the Lasso features overview, or explore use cases to see where teams start.

What this means for omnichannel conversion

Sales Assist retail AI isn’t just about in-store convenience; it’s an omnichannel signal. When the assistant learns from product content, you need that same content aligned across channels:

• Search relevance: clean attributes improve on-site search, marketplace feeds, and now in-store answers.
• Consistency across touchpoints: the associate, the PDP, and the chatbot should tell the same story.
• Faster new product launches: if your data pipeline is solid, you can roll new products into in-store AI without a separate project.

If you’re already using AI for product content generation, compare your approach with our breakdown of AI shopping assistants and catalog readiness. The core requirement is the same: structured, reliable product data that can be reused anywhere.

Practical checklist for retail AI rollouts

To make agentic retail assistants successful, focus on what your data needs to answer:

• “Which model is best for me?”: requires clear feature differentiation and intent mapping.
• “Does it work with my existing device?”: needs compatibility data and cross-sell rules.
• “What’s the difference between these two?”: relies on normalized specs and side-by-side comparability.
• “Can I return it if it doesn’t fit?”: must include policy details and store-specific rules.

Two extra considerations often get overlooked in pilots:

• Change control: when pricing or promotions update weekly, the assistant needs a clear source of truth so answers don’t drift.
• Escalation paths: when the assistant is unsure, it should route to a human expert or present a short checklist rather than guessing.

If your answers today are inconsistent, fix the foundation first. Our product data quality checklist is a good starting point for auditing gaps before you deploy AI assistance at scale.

Getting started: make AI assistants trustworthy

Sales Assist retail AI shows the direction the market is heading — agentic assistants built for real commerce workflows. The fastest path to adoption is data quality: clean product specs, structured variants, and aligned policies that an AI can trust.

If you want to move faster, Lasso can help you consolidate supplier data, standardize attributes, and generate consistent product content across channels. Start with pricing to see what fits your team, or book a quick walkthrough on the contact page.