Digital asset products and specialized AI

Build it. Then make it better.

We build DeFi products, smart contracts, and execution infrastructure, then train and evaluate specialized models that make them faster and more reliable. Each improvement ships with the measurement behind it.

Improve an existing system
About Us

Kaizen means continuous improvement.We take it literally.

We build digital asset products end to end, from smart contracts and backend services to infrastructure and interfaces. Then we keep improving them after launch, against a baseline, on held-out data, with clear limits on what the numbers show.

Solutions

Better systems. Clearer evidence.

Start with the problem. Define what must hold. Measure what improves.

Correctness

Make requirements and assumptions explicit. Test critical behavior, edge cases, and failures throughout development.

Performance

Measure latency, computation, and resource use against a clear baseline. Check that improvements preserve the behavior that matters.

Reproducibility

Keep experiments, evaluation criteria, and results inspectable so others can reproduce the work and judge its limits.

Integration

Connect new ideas to existing code, data, and operations, with attention to the practical requirements of running the system.

Services

Three ways to work with us

Bring us a product to build, a system to improve, or a roadmap that needs senior engineers.

Build

For teams launching or extending a digital asset product.

  • Products

    DeFi applications from first prototype to production, with the interface, backend, and contracts connected into one system.

  • Smart contracts

    Protocols, upgrades, and integrations, with written specs, invariant tests, and fuzzing before anything holds funds.

  • Execution infrastructure

    Simulation, historical replay, indexing, and the data pipelines that keep production systems honest.

Improve

For teams that need a system to be faster, more reliable, or better measured.

  • Performance

    Find bottlenecks and test changes on replayed production workloads, measuring speed and correctness together.

  • Specialized models

    Task-specific models for problems like trade allocation and routing, compared against a simple baseline before they reach production.

  • Evaluations

    Reproducible tests and simulations for protocols, models, and trading agents, including the edge cases that cost money.

Embed

For teams that need senior engineers working inside their own roadmap.

your team · our engineers

  • Senior engineers

    Smart contract, backend, and data engineers with production DeFi experience, working in your codebase, your reviews, and your standups.

  • Dedicated team

    A small team that owns one workstream from spec to production and reports to your engineering leads.

  • Based in Latin America

    Engineers in Brazil and across LatAm, with working hours that overlap US and European teams.

Smart contracts

Break it in testing,not in production.

We write invariants before code holds funds, fuzz them with thousands of call sequences, and fix what breaks. Here's what that loop looks like.

src/Vault.sol
function previewDeposit(uint256 assets)    public view returns (uint256){    uint256 supply = totalSupply();    return assets.mulDiv(supply + 1e6, totalAssets() + 1);}
test/VaultInvariants.t.sol
/// a nonzero deposit never mints zero sharesfunction invariant_noZeroShareDeposits() public view {    assertEq(handler.ghost_zeroShareDeposits(), 0);}

Illustrative example, not client code.

forge
$ forge test --mc VaultInvariantsfuzzing 2 invariants[FAIL] invariant_noZeroShareDeposits() (runs: 41, calls: 20480)shrinking counterexample:3 callsdeposit(1, attacker)donate(1e18)deposit(1e18, victim) → 0 shares# add virtual shares so a donation cannot inflate the share price$ forge test --mc VaultInvariantsfuzzing 2 invariants[PASS] invariant_noZeroShareDeposits() (runs: 256, calls: 128000, reverts: 0)[PASS] invariant_solvency() (runs: 256, calls: 128000, reverts: 0)2 passed · 0 failed
Research

Open questions we're working on

Where protected order flow leaks. Whether a model can find a better starting point for a trade. How machine-checked proofs in Lean 4 can raise the bar for protocol code. We publish the method and its limits with the result.

Field notes

Settlement.lean
theorem settle_preserves_supply (s : State) (b : Batch) :
    supply (settle s b) = supply s := by
  induction b with
  | nil => rfl
  | cons t ts ih => simp [settle, apply_preserves, ih]
Goal state
No goals. Proof checked.
Illustrative snippet, not a published result.

You built it.Let's make it better.Tell us what you're shipping and where it needs to improve.

DM us on X, or email pedro@kaizenlabs.cc