Curvestone AI secures £3m seed to preserve multistep accuracy in regulated workflows
This article covers Curvestone AI, an AI startup, which has raised £3m in seed funding to tackle the compound error problem that limits reliable automation in heavily regulated document workflows. The development aims to support regulated sectors including financial services, legal and insurance by preserving accuracy across multi-step document workflows and enabling more auditable, compliant automation.
Curvestone AI has raised £3 million in seed funding to tackle what it calls the “compound error problem” that limits reliable automation in heavily regulated document workflows. The London company, an AI startup led by brothers Dawid and Sebastian Kotur, says its platform preserves accuracy across multi-step processes used in financial services, legal and insurance work — a challenge that has slowed AI adoption in those sectors.
Why it matters
Regulated industries are cautious about automation because small errors in individual AI tasks can add up across a workflow. Curvestone highlights a common example: a component with 98 percent accuracy can see overall workflow accuracy fall into the 30 to 40 percent range after a dozen dependent steps. That erosion of reliability reduces trust, increases compliance risk and limits where firms will deploy AI.
Curvestone’s commercial claims matter because the company reports it reached profitability before taking outside capital, grew revenue sevenfold in 12 months and now processes billions of tokens quarterly across legal and financial services workflows. The result would be faster processing, more consistent compliance checks and auditable outputs where manual review has been the default.
Inside the product
Curvestone provides a workflow automation layer designed for document-heavy, regulated processes. Key product points:
- Focus on multi-step accuracy: the platform is built to maintain consistent performance across sequential validation and processing steps, addressing the so-called compound error problem.
- Model-agnostic: it integrates with a range of large language models rather than depending on a single provider.
- Systems integration: connects with CRMs, document management systems and loan origination software so teams can keep existing tools and data flows.
- No-code configuration: operations teams can adapt processes and compliance checks without engineering support, with human-in-the-loop review and audit-ready outputs.
- Compliance and security: Curvestone is ISO 27001-certified and says deployments include multistep compliance workflows such as those addressing DORA in the EU.
Customer examples give a sense of where the product is being used and the claimed impact. Walker Morris reduced a service agreement review task from four hours to around 15 minutes. Stephenson Harwood has deployed a multistep compliance workflow for DORA rules. Pivotal Growth is rolling out automated compliance checks across 20 specialist advice teams, moving from manual spot-checking of roughly 10 percent of mortgage applications to reviewing 100 percent, with automated ingestion of case files, validation against supporting documents and instant discrepancy flags.
Who are the investors?
Curvestone raised £3 million in a seed round led by MTech Capital, with participation from Boost Capital Partners, D2 Fund and Portfolio Ventures. Kevin McLoughlin, Partner at MTech Capital, will join Curvestone AI’s board.
Kevin McLoughlin, Partner at MTech Capital, who is joining Curvestone AI’s board, said:
We have recently seen a lot of AI startups focused on automating workflows. Curvestone is solving the hard technical problem of automating complex workflows while achieving high accuracy, and accuracy is paramount in regulated industries like financial services. While many AI startups are burning a lot of capital, we were impressed by the fact the founders bootstrapped to profitability before taking any outside funding. Their solution works across financial services, legal, and insurance, particularly for compliance workflows that desperately need automation. The early traction they’re seeing validates real market demand.
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Founder perspective
Dawid and Sebastian Kotur founded the business after running AI automation programmes at PwC, Metro Bank and GKN and organising local AI developer communities in London. They shut down their consultancy to build a productised automation platform aimed at professional services.
Dawid Kotur, Co-Founder and CEO of Curvestone AI, said:
In regulated industries, quality and scale have always been at odds. You can review everything and go broke, or cut corners and hope for the best. AI that actually works changes that equation by handling routine validation at scale while humans focus on the complex cases that need expert judgement.
The founders emphasise delivering config-driven workflows that operations teams can adapt as regulations change, rather than locking customers into a fixed system.
The bigger picture
Curvestone’s funding and customer traction sit within a broader trend: firms in financial services, legal and insurance are seeking automation that can be audited and defended for compliance purposes. Regulators in the UK and EU are increasingly focused on governance, explainability and audit trails for automated decision-making, which raises the bar for AI deployments in these sectors.
This deal also reflects selective investor interest in startups that combine technical solutions with early commercial validation — particularly those that can show measurable improvements in turnaround time and compliance coverage. For firms handling mortgages, wealth management and corporate legal work, the ability to move from sample-based checks to systematic, auditable reviews is a practical reason to invest in automation.
Curvestone’s move will be watched across the UK and Europe, where demand for reliable, auditable AI in regulated workflows is growing and regulators are tightening expectations around controls and traceability.
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