01 · Reasoning
Explainable AI
Every recommendation exposes factors, weights and overrides. No black box outputs. Traceable, auditable, defensible.
An API-first architecture that blends explainable AI, expert systems, knowledge graphs and machine learning — orchestrated under a human-in-the-loop supervisor.
Every recommendation exposes factors, weights and overrides. No black box outputs. Traceable, auditable, defensible.
Encoded domain rules from vetted specialists — audited, versioned and updatable across every vertical.
Structured relationships between people, factors and outcomes — the fabric of context.
Statistical patterns tuned to domain-specific outcomes — never a single-model monoculture.
Traceable branching logic that any human — engineer, expert or auditor — can inspect and challenge.
Symbolic reasoning + statistical learning combined at every layer. The best of both worlds.
Instrumented, versioned LLM interactions with citations, guardrails and evals — production-grade.
Domain experts review, override and improve every recommendation surface — a compounding advantage.
Every capability is exposed as a callable endpoint — for partners, enterprises and future applications.
Formal frameworks for structuring, scoring and auditing decisions — with lineage back to Herbert Simon, Kahneman and modern DI research.
Applied cognitive psychology: bias mitigation, decision-under-uncertainty, prospect theory, dual-process reasoning.
Structured translation of yogic sciences, dharmic psychology and Vedic frameworks into decision-relevant signals.
Clinical and organisational psychology models for personality, compatibility, leadership and coping strategies.
Modern ML, LLMs, symbolic AI and systems thinking — combined into a hybrid architecture that is greater than any single model.