Saber says bootstrapped AI growth beats UK funding frenzy

Saber has grown into a bootstrapped business with £13 million in annual recurring revenue. The Manchester AI workflow automation company traces that growth back to work first developed for a law firm audit.

It began with a 200,000-claim book that had been due to be audited by hand. Saber said the review was expected to take six weeks to assess 10% of the claims, but an automated workflow processed the entire book in under three days.

That project became the basis for the business co-founded by Jakub Lenski, who said the experience shaped how the company built its product. Rather than raising venture capital at the outset, Saber expanded without external funding and focused on customer problems in claims, insurance and pensions.

Lenski argues that the wider UK market has a gap between investment in AI and its use in live business processes. He pointed to figures in the company's article showing that UK AI startups raised a record £9.4 billion in the first half of 2026, while businesses often face longer and less certain returns from AI projects than from older forms of software.

Implementation focus

His central argument is that the sector's focus on fundraising can push software companies towards growth targets before they have proved their products can solve operational problems. That matters particularly in regulated sectors, where audit trails, scrutiny and high claims volumes can quickly expose weaknesses.

"The UK is experiencing an AI implementation problem," said Jakub Lenski, Co-founder, Saber.

He said the issue is not investment itself but the incentives that can come with it. In his view, those pressures can lead developers to build for speed and market attention rather than for use in real working environments.

"Founders are racing to capitalise on the AI goldrush and using funding to build products at speed that can compete in an increasingly competitive market," Lenski said.

The company's account draws a contrast between investor interest in AI and the experience of buyers in areas such as claims handling. For those teams, software purchases are judged heavily on payback periods and on whether systems move beyond trials into routine use.

Lenski said sectors such as insurance and pensions are especially difficult environments for untested tools. Workflows in those markets are often shaped by compliance demands and high document volumes, leaving little room for products that perform well in demonstrations but struggle in production.

Bootstrapped growth

Saber said its decision to remain bootstrapped forced it to stay close to customer requirements. The company argues that solving a clearly defined claims problem before trying to scale helped it develop software better suited to operational teams.

That position reflects a broader debate in the UK technology sector about whether strong funding totals are translating into business adoption. The company cited the number of venture-backed AI companies in the UK as evidence of a crowded market, while questioning whether all of them are solving problems customers urgently need addressed.

Lenski framed that question directly in the article: "If Saber had raised before finding and solving that first problem, would the product have been as effective?"

He also argued that success in AI should be measured less by the amount of capital raised and more by the value delivered to customers. That view aligns with the company's history, which ties its commercial growth to one initial audit assignment rather than outside finance.

For Saber, the law firm project remains the defining example of that approach. The company said a task expected to take weeks on a sample basis became a full-book audit completed in days, a result that "eventually led to the £13m ARR bootstrapped business that followed," Lenski said.