AI Engineering Review & Optimization · Diagnose · Optimize · Validate

When your AI application costs more than expected, runs slowly, or behaves inconsistently, ANAGANI helps you investigate why.

ANAGANI Diagnose reviews an exported run trace and highlights evidence-backed issues worth investigating.

Who it’s for

AI Engineering teams building LLM, RAG and agent applications—including applications using proprietary or open-weight models—provided their traces use a supported format. It is designed for LLM applications, not traditional ML workflows.

How it works

Observability toolLangSmith, Langfuse, Datadog,
OpenInference or
OpenTelemetry GenAIYour application run trace

ANAGANIReviews the exported trace

Analysis and recommendations

Cost, latency and token use
Repeated model/tool calls and retries
Tool failures
Retrieval and context patterns
Evidence-backed recommendations

Executive + Engineering ReportsPrioritized findings linked to trace evidence.

Requires a supported trace format. Insights depend on the data available in the trace.

The pilot uses trace uploads; ANAGANI is not currently a one-line terminal integration. Start with the Quick Check, ask about a metadata-only review, or request a guided pilot.

Have privacy concerns? Ask about a metadata-only review before sharing trace content.

Choose your next step

Start with the five-minute Quick Check. Use approximate, non-sensitive information only. Do not enter prompts, customer data, secrets or API keys.

Download Quick Check Ask about a metadata-only review Request a guided pilot

The Quick Check provides discussion signals. It is not a measured audit or savings claim.

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20 diagnostic rulesEvidence-driven analysis with transparent rule coverage

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Evidence-backed recommendationsActionable engineering priorities

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Executive + Engineering reportsBusiness context and technical depth

Next step: a guided pilot

After the Quick Check or metadata-only conversation, we agree the review scope and confirm what can be shared safely.

Use the "Request a guided pilot" button above to email sanaganiai@gmail.com.

What the reports cover

Cost, latency, token use, model and tool calls, repeated calls, retries, failures, and retrieval or context patterns—when those details are present in the trace. Findings are prioritized, linked to trace evidence, and paired with recommendations for what to investigate.

▤ Executive Report

High-level assessment, key performance metrics and prioritized next steps for decision-makers.

🔧 Engineering Report

Detailed recommendations, supporting evidence, quality-risk considerations, validation steps and a privacy-safe Trace Timeline.

How to read the review

Finding = an evidence-supported issue. Observation = measured trace behaviour. Hypothesis = something to investigate or validate.

ANAGANI AI output is engineering decision support, not a guarantee of savings or correctness. Validate recommendations against representative tests and quality requirements before production changes.

Example review: GPT Researcher

See the findings and Executive and Engineering reports from one traced GPT Researcher workflow.

Explore GPT Researcher review

One run; no measured savings claim. No signup or upload is required to view this example.

How ANAGANI fits with tracing tools

Tracing tools capture and display application runs. ANAGANI reviews a supported exported trace and produces focused Executive and Engineering Reports to help teams investigate it. It complements existing tracing tools; it does not replace them.

What ANAGANI doesn’t do

It does not continuously monitor live traffic, manage inventory or IAM, enforce security or compliance controls, inspect source code, automatically fix or deploy changes, or certify that an answer is correct. It can flag quality risks visible in the trace.