Core Features Overview
Obsqra.fi combines three fundamental technologies into a unified system. This page explains how ZK privacy, verifiable AI, and DAO governance work together to enable trustless, privacy-preserving capital management.
The Three Pillars
Each pillar solves a distinct problem, but they're designed to work as an integrated system:
ZK Privacy Layer
Problem: Traditional DeFi exposes all positions on-chain, enabling front-running, copy-trading, and privacy violations.
Solution: Zero-knowledge proofs break the link between deposits and withdrawals. You prove ownership without revealing which deposit is yours.
Verifiable AI
Problem: AI systems managing capital create trust issues. How do you know the AI used the model it claimed, analyzed real data, and followed stated reasoning?
Solution: AI commits recommendations on-chain before execution. The commitment hash creates cryptographic proof that decisions can't be changed retroactively.
DAO Constraints
Problem: DAOs face a dilemma: give AI full autonomy (risky) or require votes for every decision (too slow for markets).
Solution: On-chain policies define hard limits. Safe recommendations auto-execute within bounds. Risky ones require governance approval.
How They Work Together
The three pillars are not independent features—they're designed as an integrated system where each component enhances the others:
Users deposit via ZK commitments
Individual positions hidden in pool
Risk scoring + allocation generation
DAO policy validation
AI commits on-chain before execution
Pool-level allocation change
ZK proofs for redemption
Users deposit via ZK commitments
Individual positions hidden in pool
Risk scoring + allocation generation
DAO policy validation
AI commits on-chain before execution
Pool-level allocation change
ZK proofs for redemption
Privacy + AI
The privacy pool aggregates individual deposits. AI makes allocation decisions at the pool level, so it never sees individual user positions. Your privacy is preserved even while AI manages the capital.
AI + Constraints
Every AI recommendation is checked against DAO-defined constraints before execution. The AI operates autonomously within safe boundaries, but cannot override governance rules. This enables speed without sacrificing control.
Constraints + Privacy
DAO constraints are enforced at the pool level, not on individual deposits. Governance can set risk limits without needing visibility into who deposited what. Policy enforcement and privacy coexist.
Complete System Flow
Real-World Scenario
Here's how all three pillars work together in a practical example:
Alice deposits 10 ETH using a ZK commitment. Her deposit is visible on-chain but her identity isn't linked to it.
AI analyzes market conditions and determines Aave's risk increased. It generates a recommendation to shift 15% from Aave to Lido.
DAO constraints validate the recommendation. The change is within allowed limits (no single protocol exceeds 50%), so it auto-executes.
Rebalancing happens at pool level. Alice's specific position is never revealed. The pool shifts allocation, and her share adjusts proportionally.
AI commitment is immutable. The recommendation was committed on-chain before execution. Anyone can verify the AI didn't change its reasoning after seeing results.
Alice withdraws 3 months later using a ZK proof. No one can link her withdrawal to her original deposit. Her privacy is preserved end-to-end.
Why This Integration Matters
For Users
- ‣Deposit and withdraw privately
- ‣AI optimizes your capital automatically
- ‣DAO ensures AI stays within safe bounds
- ‣Full auditability of AI decisions
For DAOs
- ‣Delegate treasury management without losing control
- ‣Set risk parameters via governance
- ‣AI operates 24/7 within policy bounds
- ‣Cryptographic audit trail for compliance
Explore Each Feature
ZK Privacy Pools
Commitments, nullifiers, and Groth16 proof generation
Deep dive →Verifiable AI
Risk scoring, commit-reveal pattern, and audit trails
Deep dive →DAO Constraints
On-chain policies, auto-execution, and manual overrides
Deep dive →Multi-Strategy Routing
Protocol adapters and allocation rebalancing
Deep dive →Automated Rebalancing
See all three pillars working together in production
Deep dive →