Case Studies

Three quiet failure modes in analytics platforms: the cost allocation whose residual is zero by construction, the identity join that merges two customers, and the fact table that changes months. Each one runs green.

Neutral, third-party proof of where DEX volume really originates, surfacing hidden, untagged orderflow plus execution quality and MEV across Ethereum, Base, Arbitrum, and Optimism. Live API + dashboard.

Predictive real-estate intelligence fusing fragmented municipal permit records with multimodal AI (Google Gemini) to score teardown probability, flippability, and off-market opportunity. Live B2B API.

A real-time anomaly-detection oracle that scores the reliability of decentralized GPU compute (Akash, Render, io.net) as a 0–100 Compute Health Score for autonomous AI agents.

A FastAPI rendering engine that aggregates Notion databases and streams dynamic, real-time SVG charts directly into Notion embeds.

A practical methodology for detecting MEV and sandwich attacks on-chain, separating real slippage from adversarial extraction, across Ethereum, Base, Arbitrum, and Optimism.

How AddressIntel unifies building permits from four municipal systems on the parcel APN, engineers a teardown label that is independent of the feature it validates, and backtests it point-in-time, with an honest read on what the model actually beats.

Nine structural bets about execution, MEV, and data verification on-chain that don't depend on any specific protocol, model, or chain staying the same.

How ClearTrace turns anonymous, high-volume "Unknown Proxy (0x…)" contracts into named entities with a cheapest-first resolution ladder across a dozen sources: on-chain registries, bytecode fingerprints, proxy slots, and paid APIs tried last.

Off-chain RFQ and private-market-maker fills revert under fork-simulation. ClearTrace detects them from the quote's own route and sends them to an on-chain read instead of discarding the sample, so the execution-quality leaderboard is not biased toward AMM-only flow.

Most predictive-ML backtests quietly leak the future and report a number that falls apart in production. Here is the point-in-time discipline that separates a defensible model from a flattering demo: the as-of-T rule, time-based folds, rare-event metrics, label independence, and a pre-registered go/no-go.

A blockchain has no sessions and no user IDs, only a flat log of swap legs. Window functions rebuild each wallet's ordered path between DEX entrypoints, but the work that decides whether the output means anything happens before the first LAG: collapsing legs to one transaction, excluding arbitrage bots, and keeping each chain's timeline separate.

There is no fee event on a swap. A frontend's cut is an ordinary ERC-20 transfer hiding among the swap's own token movements, so identifying it is a problem of exclusion rather than decoding: five categories to rule out, an asymmetry that makes a fee sink a signal in one direction only, and a confidence ceiling that stays at medium on purpose.