H
Hurley Express AI
Enterprise AI Engineering

Architecting Deterministic AI Infrastructure & Workflows

We transition businesses from basic LLM prompts to production-ready multi-agent orchestration, automated eval gates, versioned PostgreSQL prompt management, and enterprise-grade Python SDKs.

99.9%
Schema Reliability
<100ms
Cached Context Latency
CI/CD
Automated Eval Gates
pipeline_eval.py
from deepeval import evaluate
from hurley_ai.sdk import PromptRegistry

# Pull active version from PostgreSQL
registry = PromptRegistry(db_url=CONFIG.DB)
prompt = registry.get_active("agent_router")

# Execute DeepEval Quality Gate
results = evaluate(
test_cases=dataset,
metrics=[GEval(), HallucinationMetric()],
threshold=0.92
)

if results.passed:
registry.promote_to_production(prompt.id)

Solutions & Capabilities

Full-Stack AI Systems Architecture

We replace fragile, unmonitored prompts with resilient, deterministic software architecture.

Prompt Registries & Versioning

Design and deployment of version-controlled PostgreSQL schemas for dynamic prompt injection, lifecycle status management, and complete execution logging.

Automated CI/CD Eval Pipelines

Integration of GitHub Actions workflows powered by DeepEval, GEval, and hallucination metrics to block low-performing prompts from hitting production.

Multi-Agent State Orchestration

Architecting stateful multi-agent state machines with reflection loops, autonomous self-correction, and error fallbacks using LangGraph.

Custom Asynchronous Python SDKs

Building light, asynchronous client wrapper libraries with Pydantic V2 schema enforcement to guarantee 100% structured outputs.

Hybrid RAG & Semantic Caching

Implementing vector search with pgvector and GraphRAG to reduce latency, lower API token consumption, and retain context precision.

SLM Fine-Tuning & Distillation

Distilling complex multi-turn prompt logic into lightweight, fine-tuned open-weights models (LoRA/QLoRA) for private local deployment.

Methodology

The Engineering Engagement Lifecycle

01

Prompt & System Audit

Diagnostic evaluation of prompt density, token overhead, parsing failure rates, and safety edge cases.

02

Schema & SDK Design

Engineering PostgreSQL schemas, OpenAPI specs, and Pydantic models tailored to your business domain.

03

Eval & CI/CD Integration

Building automated GitHub Actions quality gates and golden datasets to test regression before promotion.

04

Scale & Monitoring

Deploying semantic vector caching, cost-reduction pipelines, and stateful agent telemetry monitoring.

Initiate Technical Consultation

Let's audit your current AI setup and design a production-grade infrastructure plan.