13M ARR without a term sheet: why it made us build a better product
Artificial Intelligence (AI) has proven to be one of the most transformative modern technological developments enabled by its ability to streamline workflows. The rate of growth in the global AI landscape is projected to continue its rapid upward trajectory, with the market estimated to exceed $826 billion by 2030.
As AI capabilities mature, the economic advantages have motivated significant funding from investors, and founders across the UK are racing to harness the benefits. UK AI startups raised a record £9.4 billion in the first half of 2026, accounting for nearly three quarters of all venture capital investment in the UK, however, subsidisation does not automatically translate to effective deployment. In 2025, Deloitte published a report in which respondents claimed to achieve satisfactory Return on Investment (ROI) on AI in two to four years against a typical payback of seven to twelve months for legacy technology. In sectors where operational savings and ROI are crucial data points – such as claims, insurance and pensions – integrated software needs to deliver tangible results.
Pilot Purgatory “Pilot purgatory” is a contemporary term coined by the AI tech industry to describe the sustained state of AI experimentation phases with no functional mobilisation of software for a multitude of reasons, echoed by the UK Government in their 2026 AI Adoption Research. This government-led research affirms only one third of businesses who plan to integrate AI into operations feel confident enough to do so.
As the AI tech space expands, the bottleneck in AI implementation within UK businesses becomes obvious, the problem is not with technological accessibility but whether that technology is fit-for-purpose.
The defining question is deviating from rate of deployment and towards true product utility, specifically, can emerging software solve real-world operational challenges and encourage AI software deployment? Observing this shift was at the centre of Saber’s core development from day one.
Built for Business
Insurance and similar high-volume sectors require operational relief. Building software that enhances workforce capacity means understanding what slows front-line teams down and eradicating those pain points. It’s an obvious mandate, yet one that early funded startups regularly fail to execute. Scaling targets and investor pressures can dictate product development meaning solving real customer problems takes a backseat to chasing growth.
Initial venture capital investment frequently results in artificial incentives to scale before establishing a product-market fit. Our AI workflow automation platform originated as a consulting practice, translating to six months of deep sector immersion and hands-on experience. We gained an unbiased understanding of the clients’ greatest inefficiencies by spending time embedded on the front lines of their business.
When assigned with auditing a 200,000-claim book – a manual task expected to take six weeks to process a fraction – we proved our approach by automating the entire task in less than three days. By tackling the problem manually first and commercialising second, we built a tool focused on real-world application rather than venture-funded assumptions. The Economics of Customer-Led Growth As of April 2025, the UK is home to over 2,300 Venture Capital (VC) funded AI companies. External investment in a startup’s infancy is a valuable tool for scaling and rapid product development however if secured prior to understanding the target demographic’s operational challenges, it can expedite an unvalidated direction rather than an impactful solution.
Operating as a bootstrapped startup reinforced the question – does our solution solve a real-world problem that a customer will genuinely benefit from? Our roots as a hand-on consultancy meant we solved pain points with solutions we had already stress-tested with real clients. The platform was born as a natural evolution of those proven solutions as opposed to starting as a product concept, ensuring customer feedback remained the central cog of our roadmap.
Without ever signing a term sheet, we scaled in tandem with genuine client demand, compounding to £13M ARR versus relying on capital to dictate deployment cadence. It serves as a master class in value-led growth, where the pace of expansion is determined by market pull over capital push.
The Sectors Where AI Has Nowhere to Hide
The configurable framework that underpins Saber’s solution means the platform stays plaint and can be adapted to fit a diverse catalogue of customers operating in different sectors. This versatility is particularly crucial in the insurance and group litigation sectors.
McKinsey & Company estimates that convoluted claims can have lengthy lead times of up to 90 days – impacting customer satisfaction and operational expenditure (OpEx) and consequently negatively affecting profitability. There is a justified call within insurance and adjacent industries to upgrade internal digital infrastructure to relieve operational strain on workforces as legacy and manual practices fight to process increased data volume and intricacy.
Organisations that have successfully deployed appropriate AI platforms internally report clear ROI, with underwriting and claims functions already seeing productivity improvements of 30–40% from generative AI.
Despite marketing itself as a turnkey solution to modernise the sector, audit trails, regulatory scrutiny and claims volumes expose demoware quickly and adoption remains low, with abandonment rates estimated to be 80%, underpinned by deficits in governance and human–system interaction design.
The Case for Customer-Led AI
The full integration of AI workflow software in large-volume, process-intensive industries is undeniable and, in theory, welcomed by service professionals, however, successful mobilisation within UK businesses continues to prove problematic.
Maintaining a customer-centric approach and developing Saber through cultivating a deep awareness of customer pain points to direct agile product refinement meant Saber became a fully configurable platform, anchored in organisational utility.
When it comes to VC, there is no right or wrong answer. That said, diverse capital sources dictate execution roadmaps and strategic incentives. Pursuing self-funded, organic growth meant the elimination of investor influence and accommodated for client ROI.
The UK has a strong position in AI investment, research, start-ups and government attention. The real challenge to keep pace with the global rate of successful AI deployment, and therefore economic prosperity, is moulding existing technology to combat pragmatic operational bottlenecks effectively and to deliver real value to end-users.
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