Technical Preview

The AI Execution Engine for Versioned Knowledge

Capturing, validating, and versioning AI intelligence for production-grade infrastructure.

Bridging the gap between probabilistic outputs and deterministic infrastructure.

From Disposable AI to Durable Knowledge

The Problem

  • Silent Hallucinations

    Models fabricate facts with high confidence and no mechanism exists to catch it before production.

  • Zero Provenance

    No record of which model version, prompt, or context produced an output. Audits are impossible.

  • Inference Entropy

    Repeated runs produce different results. Nothing is pinned, versioned, or deterministically reproducible.

The Evida Solution

  • Recursive Validation

    An automated Judge scores every output. Anything below threshold is rejected and redriven — automatically.

  • Immutable Versioning

    Every validated output is content-addressed with a SHA-256 hash. The full lineage chain is sealed on commit.

  • Durable Execution

    State-checkpointed pipelines survive infrastructure failures. Long-running tasks never lose progress.

Execution Engine Simulator

Watch an AI inference cycle execute in real-time

Que
QueryUser Input
RAG
RAGContext Retrieval
Gen
GenInference
Jud
JudgeAutomated Validation
Re-
Re-genRecursive Loop
Pro
ProvenanceImmutable Versioning

Technical Specs

Under the Hood

versioned_knowledge.json
{
  "schema": "evida/knowledge/v1",
  "engine_version": "v1.0.4",
  "timestamp": "2026-04-03T09:14:52Z",

  "judge_validation": true,
  "confidence_score": 0.974,
  "recursive_depth": 2,

  "provenance_hash": "sha256:e3b0c44298fc1c14…",
  "parent_hash": "sha256:9f86d081884c7d65…",

  "model": {
    "id": "evida-gen-turbo-01",
    "quantization": "int8"
  }
}

System Orchestration

01

Queueing

Data ingested via high-throughput NVIDIA-optimized workers. Tasks are partitioned, deduplicated, and enqueued with priority weighting before dispatch.

02

Recursive Loop

Real-time Judge–Gen feedback loop executes until output confidence exceeds 0.96. Each cycle increments recursive_depth and re-hashes the candidate artifact.

03

Commit

Final validated state is written to the immutable version-tree. A SHA-256 provenance hash is sealed against the full lineage record for auditability.

Engineering Proof

Core Engine Architecture

Three interlocking subsystems that turn raw inference into production-grade, auditable intelligence.

Durable Execution

State-Managed Reliability

  • Persistent execution state survives process restarts and infrastructure failures without data loss.
  • Every pipeline step is checkpointed — partial runs resume from the last verified node, not from scratch.
  • Deterministic replay ensures identical outputs for audited re-runs, eliminating non-reproducible failures.

Recursive Validation

Judge-Led Hallucination Filter

  • An automated Judge layer scores every model output against a configurable factual-accuracy rubric.
  • Outputs below threshold are rejected and routed back to Re-gen — creating a closed correction loop.
  • Loop depth, retry budget, and acceptance thresholds are all runtime-configurable without redeployment.

Immutable Provenance

Version-Hashing & Auditability

  • Every validated output is content-addressed and assigned a cryptographic version hash on write.
  • The full lineage chain — model, prompt, context, score, timestamp — is stored alongside the artifact.
  • Rollback to any prior verified version is O(1). No output can be silently mutated after commitment.
View Deep Technical Specs

Built for the Modern Intelligence Stack

NVIDIA H100 / A100

Compute

Temporal

Orchestration

Pinecone / Weaviate

Vector Memory

Anthropic / OpenAI

Inference

Redis / Kafka

Message Queue