EADSS
Explainable AI for enterprise workflows

Turn human signals into explainable AI decisions.

EADSS helps teams detect risk, review AI outputs, and act on evidence-backed recommendations with human oversight and full auditability.

Launch WorkspaceExplore Demo DashboardView API
Explainable outputsHuman review built inAuditable decision trails
Signal Room
Decision latency
<300 ms
Review layer
Human-in-the-loop
Signal blend
Emotion + Risk + Topic
Current priorityExplainable decisions with evidence
Why it stands out

Not just model output. AI decisions people can understand and trust.

EADSS is designed for workflows where explainability matters as much as prediction quality, especially when teams need reviewer oversight, evidence-backed escalation paths, and accountable AI behavior.

Evidence-firstEvery recommendation can be traced back to source text, inferred signals, and reviewer action.
Human-in-the-loopTeams can approve, edit, or reject AI conclusions instead of letting the model decide alone.
Enterprise-ready governanceUse the same system for tickets, alerts, document Q&A, and internal review workflows.
Core surfaces

Product layers

Explainable Risk Signals

Trace emotional and operational patterns into transparent, reviewable outputs.

Review Queue

Create a clear approval lane between AI recommendations and business decisions.

Evidence Retrieval

Upload enterprise documents and return answers with cited supporting evidence.

Live interaction

Run a quick explainability demo

A simple front-door demo of how EADSS frames AI outputs for human review.
Decision outputExplainable AI summary
Run the demo to see a sample AI recommendation with risk framing.
Developer workflow

Designed to fit existing enterprise systems

REST APIs for ingestion, explainability, review workflows, alerts, and PDF retrieval.
const res = await fetch("https://api.eadss.com/api/v1/ingest/tickets", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "X-API-Key": process.env.EADSS_API_KEY,
  },
  body: JSON.stringify({
    enqueue_inference: true,
    items: [
      {
        org_id: "sample-org",
        source: "ticket",
        channel: "support",
        text: "Customer is frustrated. Payment failed again today."
      }
    ]
  })
});