Founded in 2025, Scalabl AI is sector-agnostic, supporting financial services, insurance, certification companies, ESG and sustainability, research, supply chain assessments, healthcare, logistics and other enterprise sectors where teams need to process large volumes of information accurately and quickly.

The common thread across these is not the sector itself, but the nature of the problem, claims Ramnath Iyer, co-founder and CEO of Scalabl AI.

He said, 'If a client has repetitive data-heavy workflows, manual review processes, complex reporting needs, document intelligence requirements or product ideas that need AIenabled execution, Scalabl AI can help design and run the workflow.'

Iyer stressed that the company's approach is always client specific, stating that the firm doesn't take a generic AI tool and force it into an industry. Instead, it understands the client's data, business rules, risk controls, users and desired outcome.

'Then we build the AI workflow around that context. This allows clients to adopt AI faster, support new products, reduce manual effort and keep their teams focused on client success rather than operational bottlenecks,' said Iyer.

Why enterprise AI strategies break

The world is currently in a stage where the AI race is standing front-and-center as a key dividing line between standing out and falling by the wayside.

One key question facing the industry is why enterprise AI strategies break before they ever break-even.

For Iyer, he comments how he is increasingly hearing stories of AI strategies failing because the business is not clearly defined.

He said, 'Many organisations do not redesign the workflow around measurable outcomes and instead start with a pain area or a proof of concept and federate the implementation to teams that have little or no incentive to show AI success. With limited focus on outcomes, slowly the AI implementation either result in shoddy outcomes or involve cost overruns with no outcomes. Some companies then pivot and hire AI experts who are not only super expensive but also are focussed on tools than business outcomes again jeopardising the strategy.'

Scalabl AI starts with the use case, the data, those involved, the current cost of the process and the value expected from AI implementation. Only then does the firm decide what architecture will support the outcomes. 'Our goal is to help clients move quickly from idea to production, with clear ROI, controlled cost and accountability from day one,' said Iyer.

Stopping AI automation becoming a cost centre

How can firms stop AI automation becoming a cost centre disguised as innovation? Iyer uses this chance to be succinct - stating that you can stop it by being very clear on what success means before the workflow is built.

He commented, 'AI can become expensive when companies automate without understanding the volume, accuracy requirement, exception rate and business value.

Our approach is to design AI workflows around unit economics. We look at what should be automated, where human review is needed, which model is most cost-effective, and how quality will be measured.'

Iyer added that Scalabl AI also avoids using expensive AI models for every task, with the business switching between models and combining automation with human-in-the-loop validation. This, he says, helps clients keep quality high while avoiding unnecessary AI spend.

What separates a production-grade AI deployment from a polished demo in the eyes of Iyer? Here, he states that whilst a demo shows what is possible, a production deployment shows what works every day, with real data, real accountability and real exceptions.

He continued, 'In production, the system must handle messy documents, incomplete information, changing client rules, compliance requirements and operational pressure. It also needs audit trails, data security, monitoring, escalation paths and clear ownership.'

This is where Scalabl AI focuses. The company builds workflows that fit into the client's operating environment, not just a standalone AI tool. 'Our work with enterprise teams across the EU, US and Asia has shown us that success depends on integration, governance and continuous support after go-live,' said Iyer.

Why human-in-the-loop becomes more valuable

In an age of increasing AI and automation, keeping the humanin-the-loop is progressively vital.

Iyer remarked, 'With AI, the role of humans changes from doing repetitive work to reviewing exceptions, prompt engineering, guiding the system and applying business judgement.'

This, the Scalabl CEO claims, is especially important in financial services, where small errors can create regulatory, financial or reputational risk. The AI provided answer is not always the right answer if the context is complex.

He added, 'At Scalabl AI, human-in-the-loop is a control layer. AI handles scale and speed, while domain experts review the right samples, edge cases and high-risk outputs. This improves trust, reduces rework and keeps costs under control.'

Building workflows you can trust

How do you build AI workflows that regulators, compliance teams and operators can all trust?

For Iyer, trust comes from transparency and control. 'Compliance teams need to know how data is handled, what decisions were made, who reviewed them and whether the process is repeatable and can be audited. Operators need the workflow to be simple, fast and reliable,' he said.

Scalabl builds these controls into the workflow from the beginning. This, he states, includes data privacy by design, client-specific environments, audit trails, access controls, clear reporting and human validation.

He added, 'We also spend time understanding the client's internal rules and risk appetite. Every client has different policies, data structures and approval processes. AI adoption becomes easier when the system respects those nuances rather than forcing everyone into a generic workflow.'

A challenging question for many in financial services is what explainable AI looks like inside a live environment. In such a space, it means for Iyer that the user can understand why an output was generated, what data was used, what confidence level was applied, if the rules and logic are transparent, if the inputs can be provenanced and whether a human reviewed and validated the output.

He gave an example, 'In document-heavy or data-heavy workflows, explainability means linking the output back to the source document, showing extraction logic, flagging lowconfidence fields, and keeping a review history.'

This for Iyer matters because financial institutions cannot rely on black-box outputs. They need systems that can be questioned, reviewed and improved. 'Scalabl AI is designed to make AI outputs usable by operations, compliance and management teams, not just data science teams,' he remarked.

Where competitive advantage arises

As AI adoption accelerates, will competitive advantage come from the model itself or from workflow integration, governance and proprietary data?

The model is important, but it won't be the main differentiator for most enterprises, states Iyer.

He remarked, 'Many companies will have access to similar models. The real advantage has always been as to how well AI is integrated into workflows, how securely proprietary data is used, and how effectively governance is built around it.'

This is why Scalabl AI is 'model-flexible', in the words of Iyer. 'We do not believe clients should be locked into one model or one way of working. The better approach is to use the right model for the right task, connect it to the client's data and workflow, and add human oversight where it creates value.'

He concluded, 'In the long run, companies that win with AI will be those that make it practical, secure and measurable across the business.'

The AIFinTech100, which this interview was a part of, can be downloaded here.