> For the complete documentation index, see [llms.txt](https://dataroom.mercle.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dataroom.mercle.ai/funding.md).
# Funding 💲
Learn about previous round, current round, use of funds.
Mercle ‌has ‌stayed ‌lean by mixing a few different sources of capital, outside funding, support from the surrounding web3 network, revenue it earned along the way, and money from the founder. That mix let the team keep shipping, cycling through several versions of the product, and arrive at today’s proof-of-human platform, all before opening this seed round.
#### Prior Funding
In July 2023, Mercle raised a $1.1M pre-seed round at a $20M valuation cap. The round was led by Protocol Labs, with participation from [Graph Paper Capital](https://www.graphpapercapital.xyz/) and angel investors including [Sandeep Nailwal](https://x.com/sandeepnailwal) (Polygon), [Swaroop Hegde](https://x.com/SwaroopH) (Powerloom), and [Aniket Jindal](https://x.com/aniket_jindal08) (Biconomy). The broader capital base around the round also included support from Aave, NEAR, Filecoin, GMX, and Mercle’s community NFT sale.
In addition to external financing, Mercle also generated approximately $500k in revenue from its first product iteration, which helped pay for the build before the company narrowed in on proof-of-human infrastructure. Pash has also personally invested $500k into the business.
This matters because Mercle has reached its current stage with relatively efficient capital deployment: a live product, early SDK integrations, and real-world verification data, before the current seed round.
#### Current Round
Mercle is currently raising a $4M Seed Round to scale the core infrastructure behind proof-of-human verification.
This is mainly an infrastructure raise. The funds are meant to deepen the technical edge over time, by improving uniqueness detection, widening the real-world training dataset, and getting the system ready for bigger platform rollouts.
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### Use of Funds
**60% Uniqueness Model R\&D**\
Advance Mercle’s uniqueness and anti-spoofing models, with a focus on biometric deduplication, robustness across real-world environments, and accuracy at scale. This includes model development across CNN- and transformer-based approaches, stronger evaluation pipelines, and improvements to production performance.\
[Current progress](https://docs.mercle.xyz/whitepaper-bluerock/ai-ml/facial-anti-spoofing) | [Current benchmarks](https://0xmercle.notion.site/MercleV1-Model-Comparison-30882d1676178072a36ddb5d7604de3e?pvs=74)
**40% mDAI Hardware Deployment**\
Deploy Mercle’s multimodal data acquisition infrastructure (mDAI) to collect higher-quality real-world verification data across diverse devices and environments. This improves training data quality, strengthens model performance, and compounds Mercle’s long-term data advantage.\
[Read about hardware device here.](https://docs.mercle.xyz/whitepaper-bluerock/network-components/interactive-blocks)
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