Solidroad’s $25M bet on AI quality assurance for customer support

When customer support leaders talk about AI, they usually mean chatbots and virtual agents. The harder problem sits in the background: how to ensure quality when both humans and models handle millions of conversations across channels. Solidroad, an AI-native quality assurance and training platform, just raised a $25 million Series A round to make that problem its core business.

The San Francisco and Dublin–based startup automatically evaluates every interaction a company has with its customers, whether handled by human agents or AI systems, and turns those conversations into a continuous feedback loop for operations and training. The fresh funding, led by global investment firm Hedosophia with participation from First Round Capital, Y Combinator, and Sony Innovation Fund, brings Solidroad’s total capital raised to more than $30 million and signals how quickly quality assurance is becoming strategic in AI-powered customer operations.

Why quality assurance is the next AI battleground

Most contact centers still rely on manual sampling to monitor performance. A team lead listens to a small subset of calls or reads a handful of chat transcripts, then scores them against a rubric. Industry estimates suggest this often covers just 1–3% of total volume, which makes it difficult to spot systemic issues, coaching needs, or model failure modes with any statistical confidence.

As companies roll out AI agents alongside human teams, this gap becomes riskier. A bad answer from a chatbot can spread faster than a bad answer from a single agent, especially when models operate 24/7 at scale. At the same time, leadership teams are under pressure to improve customer satisfaction, reduce handle times, and prove that new AI investments actually move those metrics. That combination explains why investors are now backing infrastructure that can continuously score and explain what is happening in every customer interaction, not just a sample.

Solidroad positions itself as that layer. Instead of treating QA as a compliance checkbox, it aims to make quality measurement a real-time system that informs product decisions, operational changes, and training programs across human and AI support.

Inside Solidroad’s AI-native QA platform

Solidroad’s platform plugs into the channels that support teams already use—voice, chat, email, and increasingly AI agents—and automatically evaluates 100% of interactions against a company’s quality criteria. Under the hood, this means using AI models to understand context, intent, and outcomes, then translating that into scores and structured insights.

Instead of a spreadsheet with a handful of manually scored tickets, teams get dashboards that reveal patterns: where agents struggle with a particular type of query, which workflows generate repeat contacts, or where an AI agent consistently misses policy constraints. The system surfaces risk, identifies skill gaps, and can trigger personalized coaching in real time, closing the loop between measurement and improvement.

This approach also extends to AI oversight. As enterprises deploy generative AI into their support stack, they need evidence that responses are accurate, compliant, and aligned with brand tone. Solidroad evaluates AI-generated conversations alongside human ones, creating a single source of truth for quality across all support channels.

From QA to training: closing the loop

One of Solidroad’s core claims is that QA should not stop at scoring. By linking quality data directly to training content and workflows, the platform aims to make coaching more targeted and measurable. If multiple agents repeatedly mishandle a certain issue type, leaders can see that pattern and trigger specific training modules rather than generic refreshers.

Customers cited in public materials include Oura, Ryanair, ActiveCampaign, and Crypto.com, all high-volume, customer-centric brands with complex support needs. According to the company, one customer improved its go-live customer satisfaction score by three percentage points after deploying Solidroad, with overall scores now above 90%. At large outsourced contact centers such as PartnerHero and Tech Mahindra, onboarding times for new agents reportedly fell by half once performance insights fed directly into training.

For operators, this moves QA from an after-the-fact audit to a continuous learning loop. For AI teams, it offers a feedback channel to refine prompts, escalation rules, and model guardrails based on real-world performance rather than synthetic benchmarks.