# KORA – Inference-First Execution Layer

## Core Thesis

AI applications are inference-reflexive.
They call LLMs by default.

KORA restructures inference invocation.
Structure first. Infer only when necessary.

We are not optimizing GPUs.
We are reducing how often GPUs need to run.

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## Problem

Modern AI apps:
- Call LLMs for every request
- Scale cost unpredictably
- Create latency volatility
- Centralize compute dependency

a16z insight:
“AI startups spend 80% of what they raised on compute.”

The bottleneck is not model quality.
It is invocation discipline.

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## Solution: KORA Runtime Governance

KORA enforces:

1. Deterministic-first execution
2. Explicit DAG task decomposition
3. Budget governance (max_tokens, max_time_ms, max_retries)
4. Strict schema validation
5. Structured failure contracts
6. Telemetry + cost observability

Inference becomes escalation, not reflex.

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## Measured Results (Feb 17, 2026)

Long Request Benchmark (gpt-4o-mini):

Direct:
- tokens_in: 187
- tokens_out: 200
- cost: $0.00014805
- latency: 4003ms

KORA:
- tokens_in: 188
- tokens_out: 172
- cost: $0.00013140
- latency: 4577ms

Savings:
- $0.00001665 per request
- 11.25% cost reduction

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## Stress Test (1000 Sequential Runs)

- total_runs: 1000
- ok_runs: 950
- failed_runs: 50 (intentional exhaustion cohort)
- skipped_llm_runs: 748
- total_llm_calls: 202
- p95 latency: 1869ms
- p99 latency: 3112ms

Budget mode:
- budget_breach_count > 0
- error_type: BUDGET_BREACH
- stage: BUDGET

This validates runtime governance under stress.

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## Strategic Positioning

KORA is not:
- A model
- A chatbot wrapper
- A marketplace

KORA is:
An execution layer for intelligence.

We distribute the decision of whether the model is needed.
