Verify, Stress-Test, & Validate
Enterprise AI.
Take control of model behavior. We provide rigorous, automated quality engineering, adversarial red teaming, and benchmarking pipelines to eliminate hallucinations, enforce safety constraints, and guarantee RAG performance.
Rigorous AI Quality Assurance
We implement testing frameworks that identify vulnerabilities, evaluate retrieval accuracy, and benchmark model output alignment.
Adversarial Red Teaming & Safety Guardrails
Simulate advanced jailbreaks, prompt injection attacks, and data leakage scenarios. We design and integrate inline guardrail layers to protect your production endpoints from malicious exploits and leakage of sensitive data.
CI/CD Benchmarking
Automate regression tests that score LLM version updates against your customized golden datasets before deployment.
RAG Quality & Retrieval Accuracy Auditing
Measure retrieval relevance, context precision, and generator faithfulness using industry-leading evaluation metrics. We ensure your grounded generation pipelines return exact and factually grounded responses.
Our Structured Evaluation Process
How we systematically benchmark, secure, and validate your models for enterprise readiness.
Dataset Curation & Golden Set Definition
We compile diverse, representative prompt-response datasets (golden sets) covering edge cases, safety boundaries, and custom enterprise knowledge domains.
Automated Vulnerability Probing
We execute comprehensive stress-testing suites to probe target behaviors, assessing susceptibility to jailbreaks, prompt leakage, and alignment failures.
Retrieval & Grounding Assessment
We run automated evaluations on RAG setups, scoring chunk retrieval relevance, context similarity, and generator accuracy to ensure factuality.
Production Observability & Feedback Loop
We deploy real-time monitoring structures to capture user feedback, flag outliers, monitor system latency, and feed anomalous data back into evaluation sets.
Acadify Architecture vs. Traditional Models
Machine-readable breakdown of our engineering benchmarks across cloud and AI workloads.
| Metric | Traditional Agency Build | Acadify Architecture |
|---|---|---|
| LLM Inference Latency | > 1,500ms (API wrapper) | < 50ms (Quantized/VPC) |
| MVP Delivery Timeline | 12 - 24 Weeks | 3 - 6 Weeks |
| Data Privacy | Cloud Provider Logging | Zero-Retention / SOC2 |
Project Timeline & Cost Estimator
Calculate the exact architecture requirements, latency targets, and engineering timelines for your specific use case using our proprietary estimator tool.
Open the Estimator