TECKEL AI

Automatic real-time monitoring with human intervention to ensure quality output from your RAG trained models. Ask questions about our company and see in real-time our quality scoring from our LLM-as-a-judge system.

TECKEL AI KNOWLEDGE ASSISTANT

Ask questions about our company and products

Welcome to our company knowledge assistant! Ask me questions about our products, policies, or documentation.

Try asking:

AI responses may be inaccurate. Always verify important information.

TECKEL JUDGE

Scores will appear after asking a question

FAITHFULNESS

PRECISION

RELEVANCY

FRESHNESS

SEE TECKEL IN ACTION

Ask our chatbot a question and watch Teckel Judge score the response quality in real-time. See how we identify gaps in your documentation that lead to incomplete answers.

💡 For SMEs who want to improve AI output quality 💡

AWAITING RESPONSE

This demo shows standard RAG search on our company documents and implements our LLM-as-judge SDK to audit.

BUILDING THE FUTURE OF AI QUALITY CONTROL

Enterprise-grade quality control for RAG systems with human-in-the-loop oversight. We're empowering subject matter experts to take control of AI quality by identifying documentation gaps and improving knowledge bases—not just fixing responses, but fixing the source.

BEFORE VS AFTER TECKEL

Teckel identifies problematic documentation that leads to incomplete RAG responses, helping SMEs improve AI output.

Bad Response

DOCUMENTATION GAP

Query:

"Where can I run an investment proposal for a client?"

AI Response:

You can submit investment proposals through our standard review process. Please follow company guidelines for client investments.

Critical Issues:

✗Doesn't specify which internal system to use
✗No mention of required approval workflows
✗Missing compliance documentation requirements
✗Documentation gaps lead to incomplete guidance

Improved Response

COMPLETE GUIDANCE

Query:

"Where can I run an investment proposal for a client?"

Updated AI Response:

Submit investment proposals through the InvestmentPortal system (portal.company.com/investments). Required: client risk assessment, proposal form INV-001, and supervisor pre-approval in the Workflow system. See Policy IMS-2024-03 for full requirements.

Improvements:

✓Specific system URL provided
✓Clear required documents listed
✓Approval workflow specified
✓Exact policy reference included

✓

REAL-TIME AUDITING

Every LLM response gets scored automatically for accuracy, completeness, and reference document freshness. When quality drops below thresholds, your internal SMEs are notified.

✓

ACCURACY

Factual correctness

📋

COMPLETENESS

Full answer coverage

🕒

FRESHNESS

Source recency

👨‍💼

SME EXPERTISE WORKFLOW

Your domain experts don't just monitor output, they actively shape AI quality. When their docs are cited or responses need review, they provide the human insight that makes the difference.

❓

USER QUERY

Domain-specific question

📊

AUTOMATIC AUDIT

Quality scores trigger review

👨‍💼

EXPERT REVIEW

SME feedback improves output

🌐

WORKS WITH YOUR MODELS

Use your existing AI models and keep your data secure. Works with any provider- no vendor lock-in, no data sharing required.

🌐

ANY PROVIDER

LangChain, LlamaIndex, Custom RAG

🏠

YOUR MODELS

Internally trained on your documents

🔒

ENTERPRISE SECURITY

Privacy-focused architecture

INTERESTED IN EARLY ACCESS?

We're working with select partners to refine our AI audit technology. If you're dealing with AI quality issues in production, we'd love to hear from you.

SDK INTEGRATION

Drop-in integration with LangChain, LlamaIndex, and custom RAG solutions. Add one line of code to start monitoring AI quality.

INTEGRATION BENEFITS

⚡

5-MINUTE SETUP

Works with your LLM and RAG models

🌐

RAG FRAMEWORK READY

LangChain, LlamaIndex, custom solutions

🔌

NO INFRASTRUCTURE CHANGES

Works with existing setup

🛡️

FAIL-SAFE DESIGN

If Teckel is down, your app continues normally

📦

ZERO DEPENDENCIES

Lightweight SDK with no external dependencies

🔒

ENTERPRISE SECURITY

256-bit API keys with rate limiting

CODE EXAMPLE

npm install @teckel/tracer

import { TeckelTracer } from '@teckel/tracer';

const

tracer = new TeckelTracer('your-api-key');

const

response = await openai.chat.completions.create(...);

// Add Teckel monitoring (2 lines)

const

traceId = await tracer.trace(

userQuery, response.choices[0].message.content,

{ model: 'gpt-4o-mini', sources: retrievedDocs }

);

return response; // Business as usual