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🔬 Acadify AI Labs

Acadify AI Labs is the model testing, training, and evaluation division of Acadify Solution. We specialize in LLM behavioral analysis, bias diagnostics, adversarial safety auditing, and Supervised Fine-Tuning (SFT) dataset validation. We design evaluation metrics to help engineering teams verify model accuracy, consistency, and compliance before promoting systems to production.

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⚙️ The AI System Review (ASR) Methodology

We evaluate production readiness through a structured 3-phase review process:

1. Context Engineering: Mapping target user journeys and systems architecture to define high-impact evaluation datasets that mirror live production usage.
2. Pressure Simulation: Simulating sustained interaction sequences (long sessions and complex prompt sequences) to analyze model reasoning preservation and logic drift over time.
3. Failure Mode Analysis: Exposing latent vulnerabilities, hallucinations, and safety compliance failures that standard unit testing and static benchmarks do not capture.

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💻 Code Sandbox: Automated Semantic Faithfulness Evaluation

The following Python snippet demonstrates how we run programmatic semantic checks on RAG pipeline outputs using a custom evaluation class integrated into CI test runners:

import pytest
from ai_evals import Evaluator, metrics

# Evaluates that candidate outputs remain faithful to the retrieved context
def test_rag_faithfulness_metric():
    evaluator = Evaluator(model="claude-3-5-sonnet-20241022")
    
    query = "What is the recorded API gateway latency?"
    context = "The API gateway logs report an average processing latency of 24ms."
    candidate_output = "The gateway processes requests with an average latency of 24ms."
    
    # Calculates a semantic overlap and faithfulness score between 0.0 and 1.0
    score = evaluator.score(
        query=query,
        context=context,
        output=candidate_output,
        metric=metrics.FAITHFULNESS
    )
    
    # Assert output matches the context above a semantic threshold of 0.85
    assert score >= 0.85

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🛡️ Active Testing & Diagnostics Repositories

We maintain reference architectures and libraries for evaluating model performance and securing LLM integrations:

• ai-evals-framework — Automated quality assurance frameworks and semantic similarity checks.
• llm-security-proxy — Real-time PII redaction and prompt injection classification gateways.
• fine-tuning-pipeline — Modular training configurations for LoRA/QLoRA model adaptation.
• asr-feedback — Structured frameworks to capture model performance drift and diagnostics feedback.

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📬 Technical Consultations

For questions regarding evaluation protocols, VPC deployment architectures, or custom dataset generation models, schedule a call with one of our engineers:

Schedule Technical Call  •  contact@acadifysolution.com

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