Finance technology company Tracelight, specializing in AI for Excel, secures $3.6 million in funding to enhance efficiency in financial tasks.
UK-Based AI Company Tracelight Raises Over $3.6m in Seed Funding to Simplify Complex Financial Models in Excel
Tracelight, a UK-based AI company, has announced the successful completion of a seed funding round, raising over $3.6m. The funding round was led by Chalfen Ventures, with participation from Acequia Capital, Inovo, and EF. Angel investors included Charlie Songhurst and Suhit Gupta.
Tracelight's mission is to simplify the complex financial models in Excel that have remained largely unchanged for decades. The company's AI engine acts as a translator, converting spreadsheet logic into a format understandable for large language models. This enables financial analysts to build complex, accurate models faster, reporting up to 90% time savings on repetitive tasks in investment banking and professional services.
The AI engine also checks formulas for logical errors and prevents inaccurate data from being produced. It can carry out repeat analysis, such as updating models with new financial results. Tracelight's technology has gained early traction among analysts, investment banks, and leading professional service firms.
According to Tracelight's CEO, Peter Fuller, Excel financial models are crucial for the corporate world's most important decisions. Tracelight's co-founder and CTO, Aleksander Misztal, adds that the financial models used are large, logically complex, and formatted to strict rules.
The funds raised will be used to build Tracelight's AI product, expand the team, and increase distribution to finance and strategy professionals. The call for the use of AI in financial models has increased in recent years, with many tech startups, including Tracelight, preparing to challenge traditional methods.
Other companies focusing on simplifying complex financial models in Excel include Endex AI, Fundamental Research Labs' Shortcut, and FinModelBuilder. These companies aim to automate, accelerate, and improve the accuracy of financial modeling workflows by embedding AI agents and generative models directly into Excel or spreadsheet templates.
By doing so, they target the pain points of time-consuming manual work, complex formulas, and error-prone spreadsheets, enabling faster, more accurate, and easier financial modeling workflows for analysts, consultants, and companies across finance sectors.
[1] Tracelight. (2022). Press Release: Tracelight raises $3.6M seed funding to transform financial modeling in Excel. Retrieved from https://www.tracelight.ai/press-release-tracelight-raises-3-6m-seed-funding-to-transform-financial-modeling-in-excel
[2] FinModelBuilder. (n.d.). AI-generated financial model templates. Retrieved from https://finmodelbuilder.com/
[3] Endex AI. (n.d.). Excel-native AI agent. Retrieved from https://endex.ai/
[4] Fundamental Research Labs. (n.d.). Shortcut. Retrieved from https://fundamentalresearchlabs.com/shortcut
[5] Microsoft Excel World Championship. (n.d.). Cases. Retrieved from https://excelchamp.com/challenge/cases/
- The UK-based AI company, Tracelight, aims to use the funds raised in their seed funding round, exceeding $3.6m, to develop their AI product, expand their team, and increase distribution to finance and strategy professionals.
- Tracelight's technology, designed to simplify complex financial models in Excel, has garnered early traction among analysts, investment banks, and leading professional service firms, potentially challenging traditional methods in the finance sector.
- Other tech startups, such as Endex AI, Fundamental Research Labs' Shortcut, and FinModelBuilder, share Tracelight's goal of automating, accelerating, and improving the accuracy of financial modeling workflows by integrating AI agents and generative models into Excel or spreadsheet templates.
- AI technology has become increasingly relevant in the finance industry, with corporations relying heavily on Excel financial models for their most critical decision-making processes, making these startups' efforts crucial in addressing the challenges of time-consuming manual work, complex formulas, and error-prone spreadsheets.
