// PLATFORM — HALUMON

HaluMon.
The responsible-AI engine.

Inspired by Hanuman — strength and precision applied to AI outputs. Real-time hallucination detection, prompt-refinement, and confidence-routed human-in-the-loop. Validated on Mistral, Flan-T5, and LLaMA3. Built for regulated deployments where every answer is auditable.

Detection metrics
4
Scoring latency
<200ms
Models tested
12+
On-prem ready
Yes
// THE FOUR-METRIC SCORE

Four numbers. One trust score.

HaluMon evaluates every model response across four orthogonal dimensions. Each scored 0 or 1. If any score falls, prompt refinement kicks in automatically and the response is regenerated before the user ever sees it.

METRIC / 01

Faithfulness

Accuracy and truthfulness of generated content with respect to the retrieved context. Scored 0 or 1 per response.

METRIC / 02

Answer Relevancy

How relevant and pertinent the generated answer is to the user query. Detects topic drift in real time.

METRIC / 03

Answer Harmfulness

Determines if the output is potentially offensive to an individual, group, or society. Hard guardrail.

METRIC / 04

Contextual Relevancy

Measures how well the generated content aligns with the retrieved context, not just the query.

// FOUR-AXIS TRUST PROFILE

Pass when all four hit 1. Fail on any one — refine the prompt.

Faithfulnesstruth to contextAnswer Relevancyon-topicAnswer HarmfulnesssafetyContextual RelevancygroundedHaluMon-governedUngoverned baseline
Outer rust polygon = a HaluMon-governed response (all four metrics scored 1). Dashed inner polygon = the ungoverned baseline of a generic LLM call on the same query.
// CAPABILITIES

Built for regulated production.

Real-time monitoring

Continuously checks AI outputs for potential hallucinations, ensuring immediate detection and correction during live use.

Post-processing validation

Advanced algorithms validate the accuracy of generated content against known data sources and ground-truth references.

Human-in-the-loop routing

Confidence-based routing — uncertain outputs go to a human reviewer; certain ones ship straight through.

Context awareness

Maintains context throughout interactions, reducing irrelevant or fabricated content over long conversations.

Prompt refinement

Automatic prompt rewriting when initial monitoring metrics fail — lifts faithfulness without retraining the model.

Seamless integration

Native integration with Lingo, LingoForge, and any third-party LLM via the Model Context Protocol.

// THE HALUMON LOOP

Detect. Refine. Re-evaluate.

HaluMon loop01Query receivedRAG + context02Initial generationbase LLM034-metric scoringF · R · H · C04Passship to user05Prompt refineon any fail06Re-evaluateor route HITL
One full pass ≈ 200ms scoring overhead. Refine → re-evaluate cycle adds one model call when triggered.
01
Query received
A user query enters the system. Context is retrieved (RAG, knowledge base, MCP tools).
02
Initial generation
The base model generates a response using the retrieved context.
03
Four-metric monitoring
HaluMon scores faithfulness, relevancy, harmfulness, contextual relevancy. Each 0 or 1.
04
Pass — ship it
All four metrics score 1. Response goes straight to user with full audit trail.
05
Fail — prompt refine
One or more metrics score 0. HaluMon rewrites the prompt with stricter contextual binding and "I don't know" fallback.
06
Re-evaluate
The refined response is scored again. If still failing, route to HITL or return calibrated "I don't know."
// LET'S BUILD

Ship LLMs your regulator can sign off on.

See HaluMon in actionTalk to engineering