Operate AI with confidence.
EDRAS Nexus gives engineering, AI, platform, and security teams one control plane to observe, evaluate, govern, and optimize AI applications in production.
Live Request Spans & Trace Inspector
Stream: 124 req/sec| Trace ID | Timestamp | Application | Model Target | Latency | Tokens (In/Out) | Policy Check | Status |
|---|---|---|---|---|---|---|---|
| tr_9a81f3 | 14:08:42.102 | customer-support-copilot | gpt-4o | 218 ms | 412 / 184 | PII Redacted | 200 OK |
| tr_7b12e9 | 14:08:40.891 | financial-statement-analyzer | claude-3-5-sonnet | 340 ms | 1890 / 512 | Passed | 200 OK |
| tr_4c33d1 | 14:08:38.411 | ecommerce-recommendation-agent | gpt-4o-mini (Cached) | 12 ms | 250 / 95 | Passed | 200 OK |
| tr_1d88f6 | 14:08:35.002 | claims-processing-bot | gpt-4o | 410 ms | 1240 / 0 | Blocked | Flagged |
| tr_8e99a4 | 14:08:31.750 | internal-knowledge-search | text-embedding-3-small | 45 ms | 180 / 0 | Passed | 200 OK |
AI is easy to launch. Production AI is hard to operate.
As AI applications scale from sandbox experiments to core enterprise workflows, four major operational friction points emerge.
Telemetry Blindspots
Unseen prompt paths, un-monitored agent loops, and black-box provider failures leave teams debugging blind when errors occur.
Unpredictable Quality
Silent hallucinations, model output drift, and broken JSON schemas degrade user experience without triggering traditional APM alerts.
Governance & PII Leaks
Unsanitized customer PII, prompt injection exploits, and non-compliant model responses introduce catastrophic regulatory risks.
Uncontrolled Cloud Costs
Runaway token consumption, redundant system prompt context, and un-cached LLM requests lead to unpredictable monthly invoices.
How EDRAS Nexus Intercepts & Governs Production AI
Click any architecture node below to inspect its operational role, data flow, and control capabilities.
EDRAS Nexus Control Plane
High-throughput proxy intercepting AI requests with <5ms latency to observe, evaluate, govern, and optimize calls.
Complete Control Across the AI Lifecycle
OBSERVE: Distributed Tracing & Telemetry
Trace every AI request, agent loop, RAG retrieval span, and LLM call in real time with high-precision latency breakdown.
- Full Span Tree Visualization for multi-step agent chains
- Error rate alerts & model p99 latency tracking
EVALUATE: Response Quality & Hallucination Evals
Measure model response correctness, hallucination probability, RAG relevance, and safety at scale without blocking user responses.
- Continuous production quality scoring & drift detection
- Prompt version regression benchmark test suites
GOVERN: Policy Engine & Risk Guardrails
Enforce real-time regex/NER PII redaction, rate limits, and safety policies before prompts touch external LLMs.
- Inline regex & Named Entity Recognition (NER) PII masking
- Immutable encrypted audit logging for compliance
OPTIMIZE: AI Spend & Performance Optimization
Control AI model spend with intelligent fallback routing, semantic caching, and token context optimization.
- Up to 40% cost reduction via semantic response caching
- Dynamic cost-based routing between GPT-4o & lighter models
Integration takes 2 lines of code.
EDRAS Nexus is built for developers. Rather than rewriting application code, simply update your API client Base URL to point to the EDRAS Gateway proxy.
# Change 2 lines of code to route through EDRAS Nexus AI Gateway
import os
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.edrastech.com/v1",
api_key=os.environ.get("EDRAS_NEXUS_API_KEY")
)
# EDRAS automatically traces, evaluates, enforces policy & optimizes spend
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Process customer insurance claim..."}],
extra_headers={"X-Edras-App-Id": "claims-bot-prod"}
)
print(response.choices[0].message.content)Integrate with the tools your AI stack already uses
EDRAS Nexus is architected to seamlessly interface with primary LLM providers, cloud infrastructure, and observability ecosystems.
Intercept, trace, evaluate, and govern GPT-4o, GPT-4o-mini, and o1 model endpoints.
Route and monitor Claude 3.5 Sonnet, Claude 3 Opus, and Haiku models through EDRAS Gateway.
Unified proxy routing for AWS Bedrock foundational models with IAM & KMS isolation.
Enterprise Azure deployment proxy integration with VNet data isolation controls.
Observe and evaluate Gemini 1.5 Pro and Flash model invocations with structured evaluation metrics.
Track RAG vector retrieval latency, context relevancy, and embedding vector queries.
Native SDK middleware wrapper for tracing multi-step agent chains and state graphs.
Export EDRAS Nexus AI spans, errors, and cost metrics directly to your Datadog dashboards.
Frequently Asked Questions
Technical clarity regarding gateway latency, security controls, and proxy architecture.
Ready to operate production AI with confidence?
Get started in 3 minutes with our developer tier or schedule an infrastructure architecture review with our team.