Solidroad Secures $25M Series A to Automate Quality Assurance for Customer Support with AI

Solidroad Raises $25M Series A Funding to Transform Customer Support Quality Assurance

Solidroad, a Dublin and San Francisco-based startup, has secured $25 million in Series A funding led by UK investment firm Hedosophia to scale its AI-powered platform that automates quality assurance for customer support teams1

1

2

2

. The round follows a $6.5 million seed led by First Round Capital with participation from Y Combinator, bringing the company's total funding to $31.5 million1

1

. Founded in 2023 by Mark Hughes and Patrick Finlay, both former Intercom employees, Solidroad currently employs 20 staff and counts Ryanair, Crypto.com, and Oura among its growing customer base1

1

3

3

.

AI Tackles the 1-3% Problem in Enterprise Customer Service

Most customer support operations review between 1% and 3% of their interactions for quality assurance, a process that is slow, inconsistent, and statistically meaningless1

1

. Traditional manual approaches involve team leads listening to a handful of calls or reading chat transcripts and scoring them against a rubric, providing almost no insight into the remaining 98% of customer support interactions1

1

. Solidroad's platform addresses this gap by applying AI to evaluate 100% of customer interactions across voice, chat, and email, scoring every conversation against company-defined quality criteria1

1

2

2

. The system identifies patterns in agent performance, flags coaching opportunities, and generates comprehensive visibility that manual review cannot provide at any practical scale1

1

.

How the Platform Supports Human Agents Rather Than Replacing Them

Unlike AI customer support companies pursuing full automation, Solidroad positions itself as a tool to enhance human agents through better training, feedback, and quality oversight1

1

. Mark Hughes emphasized that traditional people-based quality assurance teams are inherently subjective, with different reviewers interpreting standards differently2

2

. The platform uses AI that adheres to a customized rubric, evaluating everything at the same standard with consistent guidelines2

2

. Critically, the system doesn't apply a generic rubric across all companies—each organization defines its own quality standards, including tone, policy compliance, and interaction standards that fit their brand2

2

. One customer is running more than 800,000 conversations monthly through Solidroad, with each scored against company-defined standards—a level of uniformity impossible with manual reviews2

2

.

Training Simulations Close the Loop Between Evaluation and Improvement

A key differentiator in Solidroad's technology is its ability to translate evaluation data directly into training interventions2

2

. Most tools stop at the scorecard, putting the burden on companies to figure out what to do about identified issues2

2

. Solidroad integrates evaluation and training into a single workflow, automatically generating realistic training simulations tied to specific skill gaps when the platform identifies a problem2

2

3

3

. Instead of simply flagging that an agent needs work on billing disputes, the agent receives a targeted exercise mirroring the exact type of conversation they struggled with, enabling them to practice before engaging with customers2

2

. This approach delivers personalized coaching at scale, addressing the rising complexity of escalated conversations that reach human agents as AI handles routine requests3

3

.

Early Results Show Measurable Impact Across Contact Centres

Customers have reported significant improvements after deploying the platform. Crypto.com improved its go-live CSAT score by three percentage points, with customer satisfaction now above 90%1

1

. At PartnerHero and Tech Mahindra, two of the world's largest outsourced contact centres, onboarding times for new agents dropped by 50%1

1

. Lead management vendor Podium started resolving customer issues 33% faster after going through Solidroad's training simulations, with new hires reaching performance benchmarks in half the time2

2

. Across all customers, Solidroad typically sees a 20% increase in quality assurance coverage and 90% reduction in manual review time3

3

. These metrics matter to contact centres operating on tight margins and high turnover, where efficiency gains translate directly to cost savings1

1

.

Enterprise Readiness and Market Positioning

The company holds SOC 2 and ISO 27001 certifications, which are table stakes for selling into enterprise contact centres but represent a meaningful barrier for early-stage startups1

1

. Achieving these certifications at 20 employees suggests the founders prioritized enterprise readiness from the beginning, trading speed for credibility with large BPO operators who represent the biggest potential contracts1

1

. The founders' background at Intercom, one of the companies that defined the modern customer support software category, provides both domain credibility and a network of potential customers who understand the pain points Solidroad addresses1

1

. Solidroad joined Y Combinator's Winter 2025 cohort, which gave the company the Silicon Valley distribution channel that Irish startups often struggle to access from Dublin1

1

3

3

.

Expansion Plans as Demand Accelerates

Solidroad plans to use the Series A funding to expand its product capabilities and workforce across its Dublin and San Francisco offices2

2

3

3

. Hiring has already begun and will continue over the coming months, according to Mark Hughes3

3

. In 2025, the then two-year-old startup had around 50 customers, a number that has since grown significantly3

3

. As enterprises seek better visibility and control over both human and AI-led customer interactions, expanding the team is critical to meeting that need and raising the quality bar across the industry, Hughes noted3

3

. The platform creates a single, consistent view of quality across human and AI interactions, allowing teams to identify issues early and close gaps before they reach customers2

2

. This unified approach matters as many organizations currently monitor human agents with one process and AI agents with another, creating gaps in visibility across the system2

2

.