● Enterprise AI Diagnostics

Your AI assistant isn't broken. Your retrieval is.

We connect to your existing search index — Azure AI Search, Amazon Bedrock Knowledge Bases, or Vertex AI Search — run a controlled experiment matrix, and deliver a ranked report showing which retrieval configuration produces the best answers on your actual data.

No implementation. No infrastructure changes. Just a measurement.

Run Free Audit ▶ View Sample Report
BlindspotLabs AI Retrieval Optimization — Baseline vs Optimized

Why Your Chatbot Fails Silently

Your chatbot looks fine in demos. Production conversations fail. We identify the root cause.

🔍

Retrieval Failures

Your documents exist, but the bot can't find them. Wrong chunking, poor indexing, or weak recall.

📄

Poor Documentation Quality

Fragmented, unstructured, or outdated docs confuse both retrieval and generation.

💬

Prompting Limitations

The right context is retrieved, but the prompt doesn't guide the LLM to use it correctly.

🤖

Model Mismatch

Maybe your LLM isn't the right fit for your use case. We test and recommend.

These are common. We diagnose which one is YOUR problem.

Ideal for Teams Running Enterprise AI

If your team builds or operates any of the following, this service was built for you.

✓ Azure AI Search
✓ Azure OpenAI
✓ Internal enterprise AI assistants
✓ Knowledge retrieval systems
✓ Support AI platforms

How We Diagnose Your AI

📥

Ingest

🧪

Test

✓

Diagnose

💡

Recommend

We ingest your knowledge base, run automated evaluation, diagnose the root cause (retrieval, documentation, prompting, or model), and recommend a specific Azure, AWS, or Google managed solution to fix it.

The Output: Ranked Experiment Results

Each configuration is tested against the same 100 questions. Results are ranked by answer correctness and severe fail rate — not by vendor preference.

B

acme-corp-helpdesk

Optimization report • 3 of 10 experiments shown • May 2026

Production candidate: Keyword k=3

Experiment

Faithfulness

Context recall

Answer correct.

Severe fail

Root cause

★ Production candidate

Keyword k=3 Hard-grounded

Azure keyword gpt-4.1-mini

0.7728

0.9247

96.8%

0.0%

retrieval
100%

baseline

Vector k=5 Hard-grounded

Azure vector gpt-4.1-mini

0.7612

0.7581

96.8%

3.2%

reasoning
100%

Hybrid k=5 Hard-grounded

Azure hybrid gpt-4.1-mini

0.7579

0.7473

93.5%

3.2%

retrieval
50%
reasoning
50%
Optimization report only

Keyword k=3 + Semantic Reranker

Azure keyword reranker gpt-4.1-mini

0.8104

0.9512

98.4%

0.0%

retrieval
100%

Showing 4 of 10 experiments • Full report includes additional LLM variants, top-k comparisons, grounding strategy, and context assembly results

Download sample audit report (PDF)

Works With Your Existing Platform

Tell us where your RAG solution runs. We connect directly, ingest your indexes and documents, and identify which LLM performs best — within your ecosystem.

Azure AI / OpenAI

Azure AI Search + Azure OpenAI

Already running on Azure? We connect.

  • ✓ We ingest your existing AI Search indexes
  • ✓ We evaluate all models available in your Azure subscription
This is my platform →

AWS Bedrock / Kendra

SOON

S3 + Kendra + Bedrock

Already running on AWS? We connect.

  • ✓ We pull your Kendra indexes and S3 knowledge base
  • ✓ We evaluate Claude, Titan, and other Bedrock models available to you
This is my platform →

Google Cloud Vertex AI

SOON

Vertex AI Search + Gemini

Already running on Google Cloud? We connect.

  • ✓ We ingest your existing Vertex AI data stores
  • ✓ We test Gemini variants available in your project
This is my platform →

No platform switch required. We optimize within your existing setup — and flag new model options available on your plan, but only after discussing it with you.

Enterprise-Safe by Design

BlindspotLabs does not require production admin access.

What we use

→ Azure AI Search query keys (read-only)
→ Isolated test indexes
→ Read-only retrieval access

Guarantees

✓ No infrastructure modifications required
✓ No document migration required
✓ No production write access required

From baseline audit to measurable AI optimization

Start with one free experiment on your current production setup. If the baseline reveals improvement potential, run a controlled optimization matrix across retrieval, index and LLM configurations.

Free AI Retrieval Audit

€0

1 experiment • no commitment

Get a measurable baseline of your current AI assistant. We run one controlled experiment on your existing production setup and show where answer quality breaks: retrieval, prompting, grounding, or context assembly.

Setup

Connect your corpus, search index, and LLM to our platform — we handle the rest. Optionally, you can restrict corpus access: we'll work from document names only, with reduced diagnostic precision.

✓ 1 production baseline experiment
✓ 100 auto-generated questions (editable)
✓ Answer correctness & severe fail rate
✓ Context recall & retrieval quality
✓ Failure root causes listed
✓ Baseline report ready for comparison

Not included

✕ 10 experiment matrix
✕ Retrieval strategy comparison
✕ Root cause diagnostics
✕ Production recommendation
Run free audit

This is not a demo. Both packages run real experiments on your production setup using your existing index and LLM.

What Happens After Contacting Us

A structured, low-friction process from first contact to delivered report.

01

Intro call

02

Retrieval backend connection

03

Retrieval experiments

04

Answer quality evaluation

05

Diagnostics & recommendations

06

Review session

⏱ Typical turnaround: 24–72 hours for the free audit

Case Study

Enterprise Support Knowledge Base

Problem

The assistant frequently returned incomplete policy answers.

Finding

Hybrid retrieval exposed ranking inconsistencies caused by weak document structure.

Impact

64% → 83%

Answer correctness improved after retrieval strategy adjustments.

Built for Enterprise

🔓

Transparent Methodology

Open RAGAS evaluation framework. No proprietary black boxes.

🏢

Enterprise Background

Built by a Senior Product Manager who developed AI features and advised enterprise clients in a solution architect and consultant role across banking, insurance, and telecom.

📊

Data-Driven Insights

Every recommendation backed by real audit experiments, not generic advice.

Common Questions

Everything you need to know.

Do you require production Azure access? ▼
No. We work with read-only query keys only. We never request admin access, write permissions, or access to your production infrastructure configuration. Your production environment is not affected.
What access is required? ▼
For Azure AI Search: a read-only query key and the index endpoint. For the LLM: an API key with inference permissions only. That is the full access requirement. No admin credentials, no write access, no infrastructure changes.
Can this run on isolated test indexes? ▼
Yes, and we recommend it. You can replicate your production index into an isolated test environment. We connect to that, run all experiments there, and your production system is never touched.
Do you store our documents? ▼
No. We do not persist your documents or knowledge base content. Data is processed in-memory during the evaluation run and discarded afterward. We sign an NDA before any data exchange.
Do you support Azure AI Search only? ▼
Azure AI Search is our primary supported platform. AWS Bedrock and Google Cloud Vertex AI are on the roadmap. If you are on a different platform, contact us — we assess each case individually.
Do you support Bedrock? ▼
AWS Bedrock support is coming soon. If Bedrock is your primary platform, let us know — we are prioritizing platform support based on demand.
How long does an audit take? ▼
Free audit: 24–72 hours after connection setup. Optimization report: typically delivered within 5–7 business days, depending on index complexity and the number of experiment configurations.
Can this work with existing chatbots or AI assistants? ▼
Yes. We do not require access to your chatbot frontend or application layer. We connect directly to your retrieval backend — search index and LLM endpoint — and run experiments at that level. The interface layer is irrelevant to the evaluation.
Can you compare retrieval strategies? ▼
Yes. That is the core of the Optimization Report. We run experiments across vector, keyword (BM25), hybrid, and reranker-augmented retrieval using your actual index, and rank all configurations by answer correctness and severe fail rate.

Diagnose Your AI Today

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Request Free Snapshot Audit