Simple AI raises $14M to make voice agents the default for B2C calls

Simple AI Raises $14M to Put Voice Agents on the Front Lines of B2C Sales and Support

Simple AI has closed a $14 million seed round led by First Harmonic, with participation from Y Combinator, Massive Tech Ventures, and True Ventures. The San Francisco startup builds voice AI agents for direct-to-consumer sales and support, and says its agents convert and upsell up to 30% more than trained human reps. The funding goes toward its end-to-end voice stack, custom generative models, and deeper analytics so brands of all sizes can track intent, conversion, and call outcomes. The team's bet is clear: voice AI will become the default for inbound and outbound B2C calls-and beat human teams on efficiency at scale.

What Simple AI Actually Does

The core product is a voice agent that answers questions, personalizes offers, and completes transactions over the phone. It ingests a brand's full catalog-SKUs, metadata, pricing-and blends that with real-time customer data to route, recommend, and take action (place an order, update info, issue credits). Every call creates transcripts, summaries, and what the company calls "actionable insights" for operators. The team cites sub-850ms end-to-end latency on a proprietary voice stack, aiming for natural, low-friction conversations.

Why This Matters for Sales and Support

Phone channels are still expensive and hard to staff 24/7. If a voice agent can understand intent, navigate complex catalogs, and actually close-your cost per order drops while coverage expands. That's especially useful for DTC brands with tight margins, nights-and-weekends demand, and high inquiry volume. Expect closer regulatory attention, too; for example, the FCC has moved against AI-generated voice misuse in robocalls, raising the bar on consent, disclosure, and data handling.

• FCC action on AI-generated voice robocalls

Competitive Snapshot

• Simple AI: $14M seed; proprietary voice stack; sub-850ms latency; focus on B2C sales and support with deep catalog ingestion and real-time personalization.
• Voice Genie and Sela: Early-stage players focused on automated sales calls and lead conversion rather than full contact-center suites.
• Retell AI: $5.1M seed (Aug 2024); API-first for developers; known for production monitoring/analytics. Reported strong revenue efficiency at seed stage.
• VoiceRun: $5.5M seed (Jan 2026); code-first orchestration layer; positions for enterprises needing governance and developer ownership.

Takeaway: Simple AI leads on capital raised and published latency, signaling investor conviction in a full-stack approach for consumer brands. Retell AI and VoiceRun lean developer-first, while Voice Genie and Sela emphasize sales automation outcomes.

Signals From This Round

• Investor conviction: This size of seed often precedes a market turn. The syndicate suggests a bet on specialized, vertical agents over generic LLM wrappers.
• Full-stack strategy: Owning telephony, models, and analytics usually yields better control of latency, uptime, and outcomes-key in sales.
• Sales-first posture: The promise isn't just call deflection; it's revenue. Expect more focus on conversion lift, AOV, and upsell precision.

What This Means for Your Team: A Practical Rollout Plan

• Start with one high-intent use case: Cart recovery, back-in-stock, or inbound product questions for top SKUs. Define success: conversion rate, AOV, handle time, transfer rate.
• Data plumbing: Sync your product catalog (SKUs, pricing, promos), CRM/CDP profiles, order management, and payment rails. Set up warm-transfer rules to human reps.
• Compliance and trust: Add clear AI disclosure, consent for recording, DNC/TCPA controls, and opt-outs. Review scripts for truth-in-offer and refunds/returns policy alignment.
• Experience design: Set guardrails, objection handling, and escalation paths. Target sub-1s end-to-end latency. A/B test prompts and offer sequencing.
• QA loop: Audit transcripts weekly, tag failure reasons (price, shipping, sizing, stock), and push fixes back into prompts, catalog data, or policies.
• Team ops: Retrain reps for complex consultative calls and escalations. Align comp plans so AI-assisted sales still reward human contribution.

KPIs to Watch

• Revenue: Conversion rate uplift, AOV/upsell rate, recovery of abandoned intents.
• Efficiency: Cost per call/order vs human, average handle time, containment rate, transfer rate.
• Experience: CSAT, NPS, first contact resolution, repeat contacts within 7 days.
• Performance: Latency, interruption/overlap rate, ASR accuracy, escalation reasons.
• Time-to-value: Days to first closed sale, days to target containment, model iteration velocity.

Risks and How to Mitigate Them

• Data quality: Bad SKUs or pricing = bad offers. Automate catalog freshness checks and promo expirations.
• Hallucinations or off-brand tone: Use strict retrieval, supervised prompts, and style constraints. Penalize guesswork; favor graceful escalation.
• Edge cases: Warranty claims, partial shipments, multi-address orders-pre-build flows and test with real transcripts.
• Vendor lock-in: Keep integration layers (webhooks, events) well-documented. Export transcripts and analytics regularly.
• Regulatory pressure: Maintain disclosures, consent logs, and suppression lists. Periodically review with legal and QA.

Bottom Line

Voice remains one of the least-optimized channels in consumer sales. With fresh capital and a full-stack posture, Simple AI is betting that specialized voice agents will drive measurable conversion lift while cutting staffing costs. If you own revenue or support, pilot a tightly scoped use case, instrument it rigorously, and scale on proof-not promise.

Want more practical examples and playbooks for sales teams? Explore AI for Sales.