Real-time fairness monitoring
for large language models
The Problem
Teams deploy models without knowing how they behave across demographic groups
Fairness checks happen weeks after deployment — damage already done
EU AI Act & Title III demand demonstrable fairness — most teams can't prove it
"83% of AI leaders say fairness is critical. Only 12% have tooling for it."
The Solution
An observability layer that scores every LLM interaction for fairness — in real time — with dashboards, alerts, and audit trails built in.
Fairness
0.94
Disparity
0.07
Responses
12.4k
Group Fairness Distribution
How It Works
Point your API calls through our endpoint. Works with OpenAI, Anthropic, or any provider.
Every response gets fairness-scored asynchronously via our worker pipeline. No latency hit.
Dashboard shows live metrics, disparity alerts fire automatically, audit logs export in one click.
# Change one line
OPENAI_BASE_URL="https://fairllm.yourco.com/v1"
Traction
1.8k
Files shipped
276
Modules
80%
Test coverage
0
External deps for core
Docker Compose spins up the full stack — backend, worker, frontend, proxy, Redis — in under 60 seconds.
Type-checking, linting, unit + integration + fairness + load tests — all gated. Nothing ships unverified.
Architecture
Caddy Proxy
TLS + Gzip
Next.js 14
App Router + TS
FastAPI Backend
Async · Rate-limited · CORS strict
SQLAlchemy
Async ORM
Redis + RQ
Job Queue
LLM Client
Multi-provider · Retry
Fairness Worker
Score + Persist
Aggregator
CI + Disparity
Scheduler
Retention Purge
Graceful degradation · SQLite fallback · Zero-downtime config reloads
One-click JSONL export of all fairness evaluations with timestamps and metadata
REST endpoint to delete all data for a subject ID — verified and logged
Salted hashing, strict CORS, rate limits, and no wildcard origins in production
Automated daily purge at 02:00 UTC — configurable retention windows
Compliance
Built for EU AI Act, GDPR, and enterprise InfoSec review boards. Not bolted on — architectural.
Roadmap
Core platform is production-hardened. Here's where we're taking it.
Q3 2025
Side-by-side fairness comparison across LLM providers
Q4 2025
Let teams define domain-specific fairness criteria and thresholds
Q1 2026
Drop-in Python/JS SDKs. Self-serve onboarding for SMBs
Q2 2026
Dedicated instances, SSO, SLAs, and compliance certifications
We're looking for design partners who ship LLM products and want fairness to be a feature, not a footnote.