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Prod Monitoring & Dataset Flywheel

Watch every AI request in production. Score it as it lands. Turn every failure into stronger test coverage.

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Observability & Evaluation

Runtime observability across every project

Track cost, token usage, latency, errors, and usage trends across every AI project. See what's spending what, where latency is creeping up, and which models or prompts are driving cost growth — at the project, model, and prompt-version level.

Online evaluation rules

Score every production trace as it arrives. Configure rules per project: hallucination, answer relevance, context recall, groundedness, or any custom evaluator. Sample all traffic or a percentage. Quality scores attach to the trace alongside cost, latency, and errors.

Custom runtime rules

Define your own rules in code. PII checks, domain-specific scoring, format validation, policy detection — anything you can express as a function. Results are captured on the trace, same as built-in evaluators.

Flywheel & Feedback

Dataset Flywheel

Promote any production trace directly into an evaluation dataset. Real user questions become your regression suite. Every surfaced failure — from an alert, annotation, or customer report — is one click from a permanent test case.

Trace annotation and human feedback

Annotate traces with labels, scores, or feedback. Use real failures to grow datasets, refine prompts, and target optimization at issues that actually matter.

Threshold alerting

Set thresholds on quality scores, cost, latency, and error rate. Alerts route into ServiceNow, email, or webhook — with the underlying trace ready to investigate.

Cross-project comparison

Compare behavior across projects, models, prompts, and versions. Spot cost creep, quality regressions, and model drift before they compound.

From first request to stronger test suite — automatically.

Connect

Production traces stream in over the same instrumentation used in development — no re-implementation between environments.

Score

Online evaluation rules run continuously against live traffic. Quality scores attach to every trace as it lands.

Alert

Thresholds on quality, cost, latency, and reliability trigger alerts routed into ServiceNow, email, or webhook. Responders land directly on the trace that produced it

Promote

Surface low-scoring traces, annotate them, and promote into evaluation datasets. Each release starts with stronger coverage than the last.

Built on open standards

No lock-in, no parallel systems. Works with the frameworks you already use, the observability stack you already run.

LangChain

LangGraph

LlamaIndex

OpenAI

Anthropic

CrewAI

AutoGen

Google ADK

Dominate with Data

40%

reduction in pipeline
downtime

30%

faster time-to-model
deployment

25%

lower cluster costs

99.9%

SLA adherence on
migrated workloads

Why Acceldata

One unified system across the entire AI development lifecycle. No stitched-together tools.

Pre-production and production in one system

Same metrics, datasets, and evaluators across both. A quality regression caught in production is one promotion away from a permanent regression test.

Datasets that grow from production

Failures get promoted directly into evaluation datasets — every incident strengthens the test suite that prevents the next one. Coverage compounds with every cycle.

Quality as an operational signal

Hallucination, relevance, and custom scores live on traces alongside cost and latency — alertable, searchable, and routable into your existing incident workflow.