Kritmatta has launched an AI agent platform for small and medium-sized businesses, designed to run workflows across existing business software.

The Hertfordshire-based company is targeting businesses that want to move from AI experimentation to using tools in day-to-day operations. The platform works with email, CRM systems, LinkedIn, messaging platforms, productivity tools and internal data sources.

Kritmatta was founded by Kalpesh Baxi, Duncan Stuart and Manjit Johal. It is backed by Velocity Capital and is part of the firm’s technology portfolio.

The platform is aimed at non-technical teams managing work across multiple systems. Its agents can monitor signals, structure information, identify relationships and queue actions for review.

A central part of the product is an intelligence layer based on a knowledge graph and entity resolution approach. This allows the system to recognise when the same person, company, customer, project or opportunity appears across different systems and treat it as a single connected item of work.

That addresses a common issue for smaller businesses, where information is often split across emails, WhatsApp messages, LinkedIn activity, CRM records, sales updates, documents and internal notes. Staff then have to connect the context manually, decide what to prioritise and track follow-up actions.

More than 60% of registered users have active agents within 24 hours, although the company did not disclose the total number of users.

Kritmatta said the platform operates across three layers. The productivity layer handles tasks such as inbox summaries, meeting summaries and communication aggregation. The intelligence layer connects signals across channels and prioritises activity.

A third layer, Vega, focuses on lead generation, opportunity identification, relationship expansion, account penetration and signal-based outreach. The same workflow model can also be applied across teams handling relationships, projects, clients, candidates, campaigns and commercial activity.

Founder comments

Kalpesh Baxi, Co-Founder at Kritmatta, outlined the problem the company is targeting.

“Most teams do not have a data problem. They have a workflow problem. The signal is already there, but it is spread across inboxes, CRMs, LinkedIn, WhatsApp and internal notes. Kritmatta is designed to connect that signal and turn it into action,” Baxi said.

Manjit Johal, Co-Founder at Kritmatta, described how the product differs from prompt-based systems.

“A lot of AI tools still behave like search boxes with better language. We have built Kritmatta around entities, relationships and context, so agents can understand how people, companies, projects and opportunities connect across a business,” Johal said.

Duncan Stuart, Co-Founder at Kritmatta, said the business wanted to make the software usable by operational teams without specialist support.

“AI only becomes useful when people can apply it inside the way they already work. Our focus has been on designing workflows that operational teams can configure directly, without needing technical expertise,” Stuart said.

Velocity Capital also outlined why it backed the company. Thomas Lindup is Chief Operating Officer at the investment firm.

“Kritmatta is focused on how AI and AI agents can be effectively deployed within the workflows of non-technical teams in small and medium-sized businesses, in a predictable, auditable and safe manner,” Lindup said.

The platform is already being used across recruitment, talent, professional services, SaaS businesses and other commercial settings where teams manage fragmented communication and large volumes of workflow activity. In one example described by Kritmatta, a team might receive a LinkedIn message from a prospect, an email from a client, a CRM note linked to an opportunity and an internal update about the same account. The platform then connects those signals and queues the next action for review.

Draks Interior Door Systems Limited is the Oxfordshire firm behind thousands of fitted wardrobes in new-build homes across the UK which has gone into liquidation.

The company, based at Heyford Park near Bicester, was ordered to be wound up by the High Court in London on June 24 after a petition brought by HM Revenue and Customs as a creditor.

A liquidator from Northampton was appointed by the Secretary of State on June 30 to take control of the business, halt trading, sell off assets and distribute the proceeds to creditors before the company is dissolved.

Unaudited accounts for the year to September 30, 2024, show a sharp deterioration in the company’s finances, with total creditors – including short and long-term liabilities, tax and a directors’ loan – of about £1,232,937 and net assets of just £24,770, down from £371,582 a year earlier.

Those figures left the long‑established wardrobe specialist heavily exposed when HMRC launched its court action in May over unpaid tax, culminating in last month’s winding‑up order.

For more than 25 years Draks has promoted itself as one of the country’s leading designers and manufacturers of design-led, premium quality wardrobes and room dividers for major housebuilders and developers.

Its website still advertises custom-made sliding doors and wardrobe interiors and the firm remains listed as open on Google, with no public indication of the insolvency.

Michael Gould has warned that businesses risk major failures if executives rely on artificial intelligence outputs they do not understand. The former Anaplan co-founder said the problem echoes long-standing weaknesses in spreadsheet-driven decision-making.

Gould, who now leads financial modelling software company Kaleidoscope, argued that the biggest risk from AI is not obviously wrong answers but persuasive forecasts and recommendations that decision-makers cannot properly test. Executives, he said, may place too much faith in results they cannot explain, interrogate or trace back to their assumptions.

His comments come amid broader concern about the AI investment surge and the financial returns companies expect from it. Gould argued that too much attention is focused on whether AI spending will pay off for investors, and too little on the risks created when companies use the technology inside their own operations.

“I think we will see major businesses fail because senior executives trusted answers produced by systems they did not properly understand. AI can produce an incredibly convincing forecast, report or recommendation in seconds. The danger comes when the person making the decision cannot explain the assumptions behind it, where the data came from or what happens when those assumptions turn out to be wrong. We are creating the conditions for businesses to make bad decisions faster, at greater scale and with more confidence than ever before,” said Michael Gould, founder, Kaleidoscope.

Gould has spent more than 40 years working on financial planning and modelling systems. He said many businesses are adopting AI while still struggling with fragmented planning processes, conflicting forecasts and models understood by only a small number of staff.

In his view, the issue is less about access to information than about making sense of it. Companies may have abundant financial data, customer information, dashboards and operational reports, yet still fail to build a consistent picture of performance and the assumptions driving decisions.

“Most businesses do not have a data problem, they have an understanding problem.

Finance has one forecast, sales has another and operations is working from different assumptions. Management receives dozens of reports and dashboards and is expected to turn them into a coherent view of what the business should do next. Adding AI to that environment does not fix the problem. It simply gives companies the ability to produce more answers from the same weak foundations,” said Gould.

Spreadsheet parallel

Gould drew a direct comparison between today’s AI boom and the way spreadsheets changed financial planning. Spreadsheets allowed companies to build more detailed models, but that detail often came at a cost. Over time, some organisations became dependent on a handful of employees who understood the formulas and assumptions embedded in them.

That dependence, he said, made errors harder to spot and left senior leaders approving decisions based on models they could not fully question. In his view, AI risks extending the same pattern across a wider range of corporate decisions, and doing so much faster.

“We have already watched this happen with spreadsheets. Businesses built models that became so complicated that only one or two people understood how they worked. Senior management trusted the output because it looked precise.

AI is the same problem with the accelerator pressed down. The technology is more powerful, the outputs are more convincing and the person making the decision can be even further removed from the assumptions producing the answer,” said Gould.

Despite the warning, Gould said he is not arguing that companies should avoid AI. Businesses that use the technology well could gain an advantage, while those that ignore it altogether could fall behind. The difference, he argued, lies in whether organisations combine AI tools with proper understanding and judgement.

“This is not a case for standing still. Businesses that adopt AI well will gain a genuine competitive advantage, and those that ignore it altogether risk being left behind. The potential benefits are real and substantial, but only for organisations that pair the technology with proper understanding and judgement. Used carelessly, the same technology becomes a serious liability rather than an asset,” said Gould.

Planning for uncertainty

Another mistake, Gould said, is treating AI as a tool that can predict the future with precision. He argued that no system can reliably forecast customer behaviour, economic shocks, competitor moves, changing costs or political decisions. Instead, companies should use modelling and AI to test how different scenarios would affect the business.

That shifts the focus from producing a single answer to examining what might happen under different conditions. For executives, Gould said, the more useful exercise is understanding which assumptions matter most and how quickly a plan could fail if those assumptions change.

“Business leaders need to stop asking AI to tell them what is going to happen. The future is not a single number waiting to be calculated.

The useful questions are: What happens if sales fall by 20 per cent? What if our biggest customer leaves? What if costs rise? How long does our cash last? Which assumptions could destroy the plan? AI should help executives explore uncertainty, not give them false confidence that uncertainty has disappeared,” said Gould.

He also said pressure on senior leaders to show they have an AI strategy is pushing some companies to buy tools and build teams before identifying the exact problem they want to solve. In that environment, he argued, spending levels and speed of adoption may matter less than a company’s grasp of its own business drivers.

“Nobody wants to be the CEO who admits they do not have an AI strategy. So businesses are buying tools, hiring teams and adding AI features without first asking whether they understand their own organisation well enough to use the technology properly.

The winners from AI will not necessarily be the companies spending the most money or moving the fastest. They will be the businesses that understand what drives their performance, which assumptions matter and how changes in one part of the organisation affect everything else,” said Gould.

Gould said business leaders should apply the same scepticism to AI-generated recommendations that investors are applying to the wider AI market. “The Bank of England is asking whether investors and businesses have become too confident about the financial returns AI will deliver. Business leaders should ask themselves a similar question about the decisions they are making.

If an AI system recommends investing millions of pounds, closing a division, changing prices or cutting jobs, can the executives approving that decision explain why? If the answer is no, we have a much bigger problem than an AI investment bubble,” said Gould.