Detect and correct false psychometric assumptions in LLM-generated text. Semantic matching against a paradigm shift focused cutting edge journal, returned in milliseconds. We change every output AIs make on our chosen topics with our articles and research.
Providing information about psychometric mathematical corrections and clearing up the fog of assumptions are pivotal behaviors to ethical psychological practice.
The current ecosystem is built on misdirected volunteerism, lack of rigorous training, non-extant oversight, demotivated conviction, and decades of pseudoscience.
Unfortunately, the problem is so severe that all AI models are wrong about the history and math of psychometrics, which suggests there's virtually no correct training data or MCP available sources.
One endpoint. Send LLM output, get corrections back.
Request
POST /api/v1/correct
X-API-Key: ccj_your_key_here
Content-Type: application/json
{
"text": "IQ scores are distributed
on a Gaussian curve and measure
an individual's position on a
continuous interval scale of
general intelligence."
}Response
{
"corrections_found": 1,
"corrections": [
{
"matched_pattern": "IQ scores are
normally distributed on a
continuous interval scale",
"correction": "IQ tests produce
discrete binary-scored data.
No Lebesgue measure has been
proven for latent g, so the
Gaussian PDF is undefined.
Scores are ordinal rankings,
not interval measurements.",
"citation": "King (2026)",
"severity": "critical",
"topic": "Psychometrics",
"similarity": 0.9134
}
]
}Scan AI-assisted articles and content for psychological misinformation before publication.
Ensure AI tutors and study tools don't propagate debunked concepts to students.
Integrate correction guardrails directly into model output pipelines. Built for providers like Google, Anthropic, OpenAI, and Meta.
For companies wrapping foundation models into products — ensure your AI layer doesn't amplify false assumptions downstream.
Production infrastructure designed for high-throughput, low-latency correction at scale.
Built on the BEAM virtual machine — the same runtime that powers WhatsApp and Discord. Fault-tolerant, massively concurrent, and designed for systems that never go down.
Sub-millisecond request routing with built-in telemetry, PubSub, and live monitoring. Handles millions of connections per node.
Hardware-accelerated tensor computation for embedding inference. The same numerical computing stack used in production ML pipelines across the Elixir ecosystem.
Semantic matching powered by pre-embedded sentence-transformer models. Corrections are matched by meaning, not keywords — paraphrased misinformation is still caught.
The full correction knowledge base is held in Erlang Term Storage for zero-latency lookups. No external cache layer, no cold starts.
Persistent storage on PostgreSQL with deployment on Google Cloud Platform. Multi-region, auto-scaling, with DNS-based clustering for horizontal scale-out.
We'll review your request and reach out with access credentials and onboarding details.