✅ The Problem BASEncrypt Solves
🔓 Web3 wallets today are vulnerable.
Most users blindly sign transactions, trusting that what they’re interacting with is safe — but:

Smart contracts are immutable — if malicious, they can't be changed.
Scams like rug pulls, token impersonation, and wallet drainers are rampant.
Wallet apps don’t offer real-time security or threat intelligence.
Users lack tools for deep forensic investigation after a suspicious transaction.
There’s no proactive firewall layer protecting wallets in the Base ecosystem.

Building BASEncrypt (Firewall of Base) wasn't just about assembling features — it was about engineering real-time protection in an environment where every millisecond and signature counts.

⚠️ 1. Latency in Threat Detection
Problem: Using server-side inference for transaction validation introduced delays, especially under network congestion or high-frequency interactions.

Solution:
We moved to a CPU-level inference engine using optimized attention-based models. This allowed us to run inference directly on-device, achieving sub-50ms validation without compromising accuracy.

⚠️ 2. Recursive Contract Analysis Bottlenecks
Problem: Deep research mode recursively traced token flows and smart contract relationships, but performance degraded with large transaction graphs.

Solution:
We introduced LangChain agents with caching and pruning logic — allowing us to cut off non-relevant branches early and prioritize risk-heavy paths. This made recursive tracing significantly faster and more insightful.

⚠️ 3. Wallet Monitoring Without Obstruction
Problem: We needed a non-blocking, async mechanism to constantly monitor wallet activity without interfering with other operations.

Solution:
Implemented a lightweight heartbeat daemon running in an async loop that checks for anomalies using event listeners and agent feedback — decoupled from the main transaction execution thread.

⚠️ 4. Lack of Reliable Threat Data
Problem: Existing blacklists and community-driven data were either outdated or insufficient for inference training.

Solution:
We built our own threat simulation suite, injecting known attack patterns and contract exploits into test environments. This synthetic data was used to train and fine-tune the AI agents on what “bad” actually looks like.