About CHIAKOR

Financial reasoning shouldn't be a black box.

CHIAKOR is an explainable, knowledge-graph-based reasoning platform. It exists to make market cause-and-effect inspectable — so a recommendation is something you can follow, question and trust, powered by the frozen FinKG engine.

The problem

Opaque models, unaccountable calls

Most financial models output a number and hide the reasoning. When you can't see why, you can't audit, challenge or learn from it — and you certainly can't defend the decision.

Why we built it

The explanation should be the reasoning

CHIAKOR was built on a simple conviction: a recommendation is only trustworthy if its reasoning is explicit. So the explanation isn't generated after the fact — it is the computation itself.

Our vision

A world where every financial decision carries its evidence with it — transparent, traceable, and open to inspection by the people it affects.

What we stand for

Principles, not slogans

Explainability

Every output decomposes into signed, weighted paths. Nothing is asserted without its evidence.

Transparency

Provenance travels with every edge and every path — you always know where a number came from.

Trust

Point-in-time correctness, honest limits and a demonstrator posture: we never overclaim.

Architecture

A frozen engine behind a clean platform

CHIAKOR separates a deterministic reasoning engine from the platform that serves it. The engine is frozen and additive-only; the platform adds identity, APIs, observability and this interface.

  • The engine never depends on the platform — reasoning stays pure.
  • Language models are pluggable behind a vendor-neutral seam.
  • Every layer is independently testable and observable.
Product experienceNext.js
Platform & REST API/v1
FinKG reasoning enginefrozen
Knowledge graph & datapoint-in-time
Roadmap

Built in deliberate phases

The engine and platform were built additively — each phase frozen before the next began.

  1. Phase 1–2

    Knowledge graph & reasoning core

    The canonical graph, sign-algebra reasoning, RDF layer and evidence paths.

  2. Phase 3

    Temporal, causal & portfolio intelligence

    Point-in-time correctness, backtesting, calibration, portfolio and risk.

  3. Phase 4

    Platform, identity & serving

    REST API, JWT/RBAC, Postgres, observability, LLM seam and deployment.

  4. Phase 5

    Product interface

    The workspace, dashboards and this CHIAKOR experience.

  5. 6
    Phase 6

    Continuous learning in production

    Planned

    Live outcome feedback and adaptive calibration — not yet released.

Architectural signature
Created and Architected byMohammad Bius

The architectural authorship of CHIAKOR.

Explore explainable reasoning.