AI for money must earn trust.
The financial harness for testing financial agents before capital moves.
Paradyze
AI agents can now move money.
But trusting them is still guesswork.
Finance has no shared standard for separating convincing answers from sound financial decisions.
Confidence looks like competence
Agents can sound certain while relying on stale data, weak evidence, or false assumptions.
Small errors become real losses
A mistake in timing, sizing, leverage, or execution compounds the moment capital moves.
Trust is based on demos, not evidence
Users and builders lack a durable record of how an agent behaves across market regimes and real constraints.
Paradyze makes financial agents earn trust before they touch capital.
Test before trust
Every financial idea is challenged against evidence, constraints, and market history.
Review before action
Users see what changes, why it changes, and what could go wrong before approving.
Verify after execution
Every outcome becomes evidence that improves future decisions and agent selection.
Test before trust
Every financial idea is challenged against evidence, constraints, and market history.
Review before action
Users see what changes, why it changes, and what could go wrong before approving.
Paradyze makes financial agents earn trust before they touch capital.
Verify after execution
Every outcome becomes evidence that improves future decisions and agent selection.
Paradyze Workspace lets users test an agent before trusting it with capital.
Start with an idea
Drop a sentence, post, wallet, chart, or strategy. Paradyze turns it into a structured financial objective.
Idea → objective
Watch it build and test
Follow the thesis, evidence, assumptions, possible implementations, and replay results as inspectable artifacts.
Thesis → evidence → worlds
Review the Capital Diff
See the exact change to capital, exposure, fees, downside, and invalidation before approving.
Proposal → capital and risk diff
Explore and simulate without funding. Autonomy increases only as trust is earned.
Trust is not a confidence score.
It is a chain of evidence.
Objective, context, evidence, and constraints
Replays, live shadows, and alternative Worlds
Capital Diff, risks, and approval
Intent versus execution and outcome
The complete record enters Trading IQ
Frame
Objective, context, evidence, and constraints
Test
Replays, live shadows, and alternative Worlds
Review
Capital Diff, risks, and approval
Verify
Intent versus execution and outcome
Learn
The complete record enters Trading IQ
Trading IQ builds an evidence graph connecting intent, context, decisions, market regimes, execution, and outcomes.
The trust harness we built for Zya becomes Paradyze Proof.
Catch failures before users do
Test agents across controlled historical regimes, live shadows, and unsafe-action scenarios.
Historical → shadow → unsafe action
Compare what actually works
Evaluate agents, models, prompts, skills, and data sources against the same tasks and constraints.
Agent → model → skill → data
Produce auditable proof
Inspect evidence and tool traces, score execution and constraint adherence, and generate verification reports.
Evidence → score → report
Evaluation infrastructure for agentic trading products, wallets, and fintechs—without building it in-house.
Financial agents are arriving.
Trading infrastructure was built for humans.
Execution without evaluation
Systems can route an order, but they cannot determine whether the agent used sound evidence or respected its constraints.
Backtests without behavior
Existing tests measure returns—not instruction-following, evidence quality, safe refusal, or confidence calibration.
Automation without accountability
Intent, data, decisions, execution, and outcomes live in different systems. Agent behavior is difficult to explain, audit, or improve.
We are not building the financial harness in a lab.
Paradyze already sits inside real trading behavior.
Private testers making real financial decisions
Trading volume generated in private testing
Trades executed across real market conditions
30-day repeat retention
AI interactions inside real trading journeys
Private testing launched Oct 31st
Private testing launched October 31st
Private testers making real financial decisions
Volume generated across 12,260 trades
30-day repeat trading retention; 97 of 293 eligible traders returned
AI interactions inside real trading journeys
This gives Paradyze the environment to turn decisions, approvals, execution and outcomes into measurable evidence of agent trust.
Dennis Egenrieder
Technical Founder & CEO9 years in crypto. Every cycle. Solo founder who shipped from idea to live mainnet in 12 months.
Finance at Rheinmetall. Self-taught developer. Winner Injective AI Hackathon 2025. Bootstrapped Paradyze from idea to live mainnet in 12 months.
Onboard:
CMO
pre-funding
Key hires:
CTO
Head of Growth
“I've watched traders lose everything — not because they're bad, but because no one built the system to make them better. So I did.”
Backed by:
Cointelegraph Accelerator · Injective Co-Founders
The market is not one exchange.
It is millions of traders across a fragmented financial stack.
initial serviceable organizations across exchanges, wallets, broker-dealers, and investment advisers.
One trust harness for the agents serving both sides of the market.
Bottom-up, cross-venue deduplicated model · consumer sensitivity: 3M–12M qualified traders · sources: company disclosures, Coinbase, Kraken, CoinGecko, FINRA, IAA
The market is building agents that act.
Paradyze builds the proof layer they need.
Financial agents
HeyAnon · Wayfinder · Almanak
Research, strategies, and execution
Agent infrastructure
Coinbase AgentKit
Wallets, tools, and onchain actions
Generic evaluation
LangSmith · Braintrust · Patronus AI
Tracing, observability, and general-purpose evals
The financial trust harness
PARADYZE
Finance-native evidence connecting an agent's intent to its impact on capital.
Status quo: in-house backtests · paper trading · manual QA
Generic tools ask whether an agent completed a task. Paradyze asks whether it should be trusted with capital.
Sources: official product materials from Almanak, HeyAnon, Wayfinder, Coinbase, LangSmith, Braintrust, and Patronus AI
Two products generate revenue.
Every customer strengthens the same trust engine.
Consumer · Paradyze Workspace
Workspace
Inspect, simulate, and approve agent-driven financial work.
Workspace Pro
Higher run capacity, advanced evaluation, and autonomy within defined limits.
Free exploration and simulation create the acquisition funnel.
B2B · Paradyze Proof
Paid pilots→platform + usage
Financial-agent teams pay to test, compare, and verify agents without building the evaluation infrastructure internally.
Shared foundation
Trading IQ
More runs → stronger evaluations → better agents → greater retention and revenue
Trading IQ is not a third SKU. It is the compounding evidence moat beneath both revenue streams.
Every product strengthens the same evidence graph.
Every loop makes the financial harness harder to copy.
Consumer products
Workspace turns financial ideas into repeated agent runs.
Structured evidence
Every run captures intent, context, constraints, execution, and outcome.
Paradyze Proof
External agents and financial products are tested against the same harness.
Better agents
Evidence improves agent selection, skills, safeguards, and evaluation.
The moat is not one model. It is the evidence graph connecting intent, context, action, market regime, execution, and outcome.