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Most Web3 tools only track basic balances and approximate wallet data. Raster brings TradFi accounting rigor to DeFi through a unified Truth, Risk, and Decision Intelligence framework. Our proprietary attribution engine reconstructs a fully auditable state from raw blockchain activity to deliver deterministic PnL (Truth). We calculate deep, real-time portfolio risk signals (Risk) to power our governed AI Decision Intelligence, built specifically for deep analysis and institutional workflows.
Hey Everyone!
Jacob here, CEO at Raster.
We built Raster because onchain finance is facing a massive structural block: digital asset portfolios aren't truly operational.
If you talk to anyone handling capital in DeFi, they'll tell you the exact same things: PnL cannot be fully trusted, risk metrics can't be cleanly measured, and critical portfolio decisions inevitably break across fragmented venues and wallets.
Most tools out there only aggregate basic balances or show raw event data. They expect teams to do the hard work of internal modelling. Coming from backgrounds of TradFi, building enterprise infrastructure, and scaling systems for millions of users, our team knew we needed to bring TradFi accounting rigor to Web3 data.
How we solve it: We engineered Raster around three pillars: Truth, Risk, and Decision Intelligence.
- Truth: Instead of approximating metrics, Raster uses a proprietary attribution engine that looks at raw blockchain activity and accurately reconstructs the full portfolio state from the ground up. This gives you an absolute, auditable source of truth for cost basis, exposure, and net asset value (NAV).
- Risk: We measure real-time risk signals, including VaR, expected shortfall, and concentration analysis, directly on top of that reconstructed state.
- Decisions Intelligence: On top of that foundation, we are incredibly close to deploying our Decision Intelligence layer. This is a governed AI agentic system which converts deterministic portfolio truth into actionable market recommendations and automated financial workflows that interact directly with you.
We are actively scaling our platform through embedded institutional workflows and APIs. You can dive into the live platform right now at raster.finance.
We’d love to hear your thoughts, answer any engineering questions about our data reconstruction logic, or get your feedback on our upcoming decision intelligence architecture! 🚀
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About Raster on Product Hunt
“The AI Quant Desk for Onchain Finance”
Raster was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #16 on the daily leaderboard. Most Web3 tools only track basic balances and approximate wallet data. Raster brings TradFi accounting rigor to DeFi through a unified Truth, Risk, and Decision Intelligence framework. Our proprietary attribution engine reconstructs a fully auditable state from raw blockchain activity to deliver deterministic PnL (Truth). We calculate deep, real-time portfolio risk signals (Risk) to power our governed AI Decision Intelligence, built specifically for deep analysis and institutional workflows.
Raster was featured in Fintech (47k followers), Artificial Intelligence (469.1k followers) and DeFi (1.4k followers) on Product Hunt. Together, these topics include over 111.1k products, making this a competitive space to launch in.
Who hunted Raster?
Raster was hunted by Iwan Spillebeen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Raster stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Iwan Spillebeen