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Operational
Infrastructure for RLHF.

Sorvex Labs builds the operational layer for human feedback and AI evaluation workflows.
Reviewer analytics, drift detection, queue management, and workflow intelligence — built for frontier AI teams.

Get Early Access

Linear/Jira

for eval pipelines

Real-time

reviewer analytics

MVP

shipping soon

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The operational layer
RLHF pipelines have been missing.

Stop managing eval workflows with spreadsheets. Sorvex Labs gives you the operational intelligence to run human feedback at scale.

Swipe to explore services
Task Queue

Task Queue System

A centralized operational queue displaying pending tasks, completed reviews, retries, and escalations — giving you full visibility into every item flowing through your evaluation pipeline.

100%

workflow visibility

⏳

Contract Review #1,204

Annotator: sarah_k · Latency: 4.2s · Confidence: 0.87

Pending
✅

Safety Audit #1,203

Reviewer: james_r · 2 retries · Escalated

Done
🔄

Preference Pair #1,202

Retry 3/3 · Queue bottleneck detected

Retry
Analytics

Reviewer Analytics

Operational metrics for every evaluator in your pipeline. Track disagreement rates, approval strictness, retry frequency, average latency, and escalation patterns to maintain quality at scale.

94%

inter-rater agreement target

Disagreement
12.3%
↑ 2.1%
Avg Latency
8.4s
↓ 0.6s
Escalations
3 today
↓ trend
Drift Detection

Drift Detection

Track changes in reviewer behavior over time. Detect consistency shifts, disagreement spikes, and anomalous reviewer patterns before they silently degrade your evaluation quality.

< 48h

drift detection window

AI generating roadmap…

“Reviewer consistency drop detected for evaluator 'alex_m' — disagreement rate increased 18% over 72 hours. Recommend calibration session before next batch assignment.”

View Full Roadmap ↗
RLHF Interface

RLHF Preference Interface

A simple, focused interface for comparing response A vs response B and selecting preferred outputs. Every selection is stored with timestamps and confidence metadata to build your preference signal infrastructure.

1M+

preference pairs storable

Governance score over time↑ 57pts
JanFebMarAprMayJunJul
Queue Health

Queue Health Dashboard

Operational visibility into bottlenecks, overloaded reviewers, stuck task categories, turnaround times, and retry loops — so you can optimize throughput and reduce operational delays before they compound.

3×

faster bottleneck resolution

Safety Audit Queue

47 tasks · 3 overloaded reviewers

Warning

Preference Pairs

1,204 completed · 99.2% on-time

Healthy

Retry Loop #112

Stuck 6h → auto-escalated

Resolved

From messy pipelines to operational clarity in three steps.

Sorvex Labs handles the operational layer — so your team can focus on building better models.

Quick integration

Step 01

Connect Your Eval Pipeline

Integrate Sorvex Labs with your existing RLHF or evaluation workflow in minutes. We support annotation platforms, custom pipelines, and manual review setups — no major migrations required.

Live in minutes

Step 02

Configure Reviewer Analytics & Alerts

Set up your reviewer roster, define calibration thresholds, and configure drift detection alerts. The dashboard populates with real operational data from your first task batch onward.

Continuous intelligence

Step 03

Monitor, Optimize & Scale

Get real-time operational visibility into your entire evaluation system. Identify bottlenecks, catch reviewer drift early, resolve retry loops, and continuously improve quality at scale.

What eval teams are saying

Evaluation engineers and RLHF operators are already telling us the operational gap is real.

RLHF

operational focus

Frontier

AI lab interest

MVP

shipping soon

“The fragmentation in our RLHF stack was killing us — spreadsheets for reviewer tracking, Slack for escalations, and a dozen dashboards with no unified view. Sorvex Labs is exactly the operational layer we've been looking for.”
60%reduction in ops overhead

Evaluation Engineer, Frontier AI Lab

“Reviewer calibration drift is a silent killer in large eval pipelines. We only noticed it when model performance inexplicably degraded after weeks of data collection. Having a system that catches this in real-time is critical.”
< 48hdrift detection window

RLHF Operator, AI Infrastructure Company

“The annotation tools are great for labelers. But nobody has built the operational intelligence layer on top — the reviewer analytics, the queue health monitoring, the calibration systems. That's the real gap Sorvex Labs is filling.”
3×faster bottleneck resolution

AI Infrastructure Founder

Teams across the AI ecosystem

Built for the evaluation layer

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The operational layer
RLHF needs.
Now.

Stop managing eval workflows with spreadsheets. Get early access to Sorvex Labs and bring operational intelligence to your RLHF pipeline.

or contact us directly at aisorvex@gmail.com

No credit card required
RLHF & eval ops focus
MVP shipping soon

Building in public — join us early