AI Reliability Infrastructure

Know whetheryour AI actually works.

Evaluate RAG systems and AI agents with evidence-backed testing that exposes failures before your users do.

The problem

Production AI can sound right and still be wrong.

A polished response does not prove retrieval worked, an agent completed its task, or the answer stayed grounded in evidence. Teams need verdicts they can inspect, not chat logs they have to trust.

Looks correct

Polished answer, wrong underlying retrieval

Unsupported claim

Confident response with no source support

Incomplete retrieval

Relevant documents never surfaced

Hallucinated detail

Specific fact not present in knowledge base

Wrong source

Answer cites irrelevant retrieved document

Inconsistent

Same question, different answers across runs

Why basic testing fails

Passing a few test questions isn't the same as knowing your system works.

Basic pass/fail testing doesn't explain why failures happen. ASHE evaluates correctness, retrieval, hallucination resistance, and case-level diagnosis.

01
A test can pass.
Hand-picked questions often look fine in isolation.
02
But retrieval can still fail.
The right documents may never surface under real query patterns.
03
The answer can sound convincing.
Fluent language hides unsupported claims and missing evidence.
04
The system can hallucinate.
Specific facts appear that were never in the knowledge base.

AI reliability infrastructure

How ASHE evaluates production AI.

One pipeline for RAG reliability and agent reliability: scenario, execution, evidence, judgment, and verdict.

Your AI system

The production endpoint or agent ASHE evaluates under real query and task patterns.

Query or task

ASHE generates scenarios from your knowledge base or task set and sends them to the target.

Context capture

Retrieved documents, tool calls, and execution traces are recorded when the system returns them.

Response

The system output is judged for correctness, grounding, and task completion.

Evaluation

Frontier intelligence scores quality dimensions and flags cases that need deeper review.

Evidence

Case-level evidence chains expose retrieved sources, traces, and judge reasoning.

Verdict

Structured pass/fail outcomes with deterministic failure classification.

Introducing ASHE

The evaluation layer for production AI.

ASHE connects to your knowledge base or agent task sets, runs evaluations against live RAG and agent systems, and returns structured reports with evidence you can act on.

ASHE evaluates · ASHE explains

Your Knowledge Base

Generate evaluation questions

Execute against your system

Evaluate responses

Analyze evidence

Classify failures

Generate recommendations

Actionable evaluation report

What ASHE measures

ASHE doesn't just say “your system scored 78%.” It explains why.

RAG scoring weights correctness, retrieval, and hallucination resistance. Agent scoring adds task completion, tool use, and trajectory quality, with case-level evidence behind each dimension.

answer_correctness

45%

Correctness

Answer accuracy against expected knowledge from generated questions.

retrieval_relevance

30%

Retrieval

Relevance of returned documents when retrieval is available.

hallucination_resistance

25%

Hallucination resistance

Groundedness of the response against retrieved evidence.

Evaluation lifecycle

From knowledge base to diagnosis.

Scroll to traverse the full evaluation pipeline. Each stage stays pinned until the next locks in.

Ingest

ASHE reads the supplied knowledge base and builds the evaluation context.

01 / 08

Generate

Evaluation scenarios are generated from your knowledge base, not hand-picked demos.

02 / 08

Query

Scenarios are sent to your production system under realistic query and task patterns.

03 / 08

Judge

Frontier intelligence evaluates answer and retrieval quality with structured verdicts.

04 / 08

Escalate

Low-confidence or ambiguous cases escalate to Advanced Reasoning for deeper review.

05 / 08

Diagnose

Failures are classified deterministically instead of buried in aggregate scores.

06 / 08

Explain

Evidence chains show why each verdict was reached, case by case.

07 / 08

Recommend

Actionable recommendations surface the next engineering step.

08 / 08

Product workflow

Move through the ASHE product without leaving the page.

Scroll to walk from project setup through evaluation, cases, evidence, and report.

Project

Create a project to organize targets and knowledge bases.

Target

Knowledge base or task set

Upload ground-truth content for RAG questions, or define agent tasks with optional tools and policy.

Evaluation run

Start an async run. ASHE returns 202 and executes in the background.

Cases

Inspect individual failures with verdict and failure classification.

Evidence

See retrieved documents, judge reasoning, and ground-truth comparison.

Verdict

Structured outcome with deterministic failure categories.

Report

Overall quality, component breakdown, and trends across runs.

project.config

name: customer-support-ai

targets: 2

knowledge_bases: 1

target.endpoint

type: assistant | agent

url: /api/query

auth: bearer

evaluation.config

rag: refund-policy.md

agent: support_tasks.json

status: ready

evaluation.run

status: running

cases: 48 / 128

concurrency: 4

case

verdict: hallucination

failure: unsupported_claim

confidence: low

evidence_chain

retrieved: refund-policy.md

score: 0.91

ground_truth: match

verdict

result: failed

category: hallucination

review: required

evaluation_report

overall_quality: 82

correctness: 88

retrieval: 79

Project

Create a project to organize targets and knowledge bases.

project.config

name: customer-support-ai

targets: 2

knowledge_bases: 1

Target

target.endpoint

type: assistant | agent

url: /api/query

auth: bearer

Knowledge base or task set

Upload ground-truth content for RAG questions, or define agent tasks with optional tools and policy.

evaluation.config

rag: refund-policy.md

agent: support_tasks.json

status: ready

Evaluation run

Start an async run. ASHE returns 202 and executes in the background.

evaluation.run

status: running

cases: 48 / 128

concurrency: 4

Cases

Inspect individual failures with verdict and failure classification.

case

verdict: hallucination

failure: unsupported_claim

confidence: low

Evidence

See retrieved documents, judge reasoning, and ground-truth comparison.

evidence_chain

retrieved: refund-policy.md

score: 0.91

ground_truth: match

Verdict

Structured outcome with deterministic failure categories.

verdict

result: failed

category: hallucination

review: required

Report

Overall quality, component breakdown, and trends across runs.

evaluation_report

overall_quality: 82

correctness: 88

retrieval: 79

Product preview

Investigation, not just scoring.

evaluation_report

Overall quality

Correctness

Retrieval

Hallucination

high_confidence: 24review_required: 3

case_diagnosis

What is the refund policy for enterprise customers?

system_answer

Enterprise customers receive a 60-day refund window.

verdict: hallucination

failure: unsupported claim in retrieved evidence

recommendation: verify enterprise policy in knowledge base

Evidence

Don't just get a score. See the evidence behind it.

Every case exposes retrieved documents, judge reasoning, ground-truth comparison, and the evidence chain that led to the verdict.

Question → system response → evidence and trace

→ Ground truth → Judge assessment → Verdict

“Customers may request a refund within 30 days of purchase.”

source: refund-policy.md · score: 0.91

Failure diagnosis

Know what failed. Not just that something failed.

hallucinationretrieval failureretrieval irrelevanceincorrect answerincomplete answertool failuretrajectory deviationground truth mismatchtarget api error

FAILED

↓ why?

↓ retrieval_failure

↓ what evidence?

↓ what should I investigate?

Continuous evaluation

Built for teams who iterate.

Evaluate→

Understand→

Fix→

Evaluate again→

Track improvement

Who ASHE is for

AI Engineers

Validate system quality before every release.

ML Engineers

Investigate evaluation failures with structured evidence.

Product & Engineering Teams

Track reliability as the product evolves.

Teams shipping AI assistants

Know whether production behavior can be trusted.

Built for teams shipping AI

Reliability for the systems developers are actually building.

ASHE is early-stage infrastructure focused on honest evaluation — no inflated claims, no fake social proof. Real capabilities you can verify in the product.

RAG + Agent evaluation

Structured reliability testing for retrieval systems and single AI agents.

Evidence-backed scoring

Scores, verdicts, and supporting evidence — not opaque pass/fail labels.

OpenAI-powered evaluation

Frontier Intelligence, Advanced Reasoning, and Premium Reasoning routed by ASHE.

Production-oriented diagnostics

Failure categories, step-level analysis, and trace inspection for agents.

Evaluation infrastructure

OpenAI frontier models
Structured evaluation pipeline
Evidence chains
RAG correctness + retrieval + hallucination
Agent task + tool + trajectory scoring

Inside the product

Everything you need to evaluate and investigate.

One active workspace area at a time, from projects through reviews, synchronized with the preview panel as you scroll.

Projects

Organize targets and knowledge bases.

Evaluation Runs

Start and monitor evaluations.

Reports

Understand overall quality and trends.

Cases

Inspect individual failures.

Evidence

See why a case received its verdict.

Reviews

Mark cases requiring human attention.

project_workspace

Organize targets, knowledge bases, and task sets in one evaluation workspace.

acme-support

2 targets · 1 knowledge base

run_monitor

Track async execution progress, concurrency, and completion status.

run_8f2a · running

64 / 128 cases complete

quality_report

Weighted scores with correctness, retrieval, and hallucination breakdown.

overall_quality: 82

high_confidence: 24

case_inspection

Open any failed case to see the question, answer, and classification.

verdict: retrieval_failure

failure: missing evidence

evidence_chain

Retrieved documents, judge assessment, and ground-truth comparison.

refund-policy.md · 0.91

unsupported claim detected

human_review

Escalated or low-confidence cases flagged for manual review.

review_required: 3

terra_escalated: 1

About ASHE

AI systems should be evaluated by evidence, not vibes.

The problem we care about

Production AI quality cannot be inferred from demo conversations. A polished answer does not prove retrieval worked, a task was completed, or that claims are supported.

What we believe

AI systems should be evaluated by evidence, not vibes. Systematic evaluation with transparent scoring is how teams ship with confidence.

Why evaluation needs more than pass/fail

Aggregate green checks do not expose failure categories, evidence chains, execution traces, or the reasons a case failed. Teams need structured insight, not a single score.

What ASHE is designed to do

ASHE generates evaluation scenarios, executes them against your system, judges outcomes with frontier model capabilities, and produces reports with weighted scoring and case-level evidence.

Where we're going

Repeatable AI reliability infrastructure for engineers and ML teams: track quality across iterations, diagnose failures, and improve with evidence.

Start evaluating production AI.

Connect a RAG system or agent, define scenarios from a knowledge base or task set, and see where reliability actually stands.

Start evaluating

See how it works