Stop hallucinations with
uncertainty-aware workflows.
Combine LLMs with ML classifiers and programmatic validators.
Add thresholds, voting, retries, and fallbacks. Ship AI that actually works in production.
Click on the nodes to understand more about how they work.
Model Library
Templates
The reliability problem with LLMs
LLMs are probabilistic. They guess. Sometimes they're 95% confident, sometimes 20%. Without knowing which is which, you can't trust the output.
The solution: Specialized model workflows. Use multiple models, check confidence scores, validate with rules, retry on failures. Just like Netflix and Uber do.
Reliability comes from orchestration, not bigger models.
The Journey to Intelligence
Build reliable AI in three steps
1. Find Quality Data
Search datasets with uncertainty scores. Know what's reliable before training starts.
- • Uncertainty scoring
- • Auto-normalization
- • Synthetic generation
2. Train Specialists
Describe behavior, we create data and train. Each model becomes an expert at one specific task.
- • Vibe training (no dataset)
- • 3x cheaper than GPT
- • Export anywhere
3. Add Validators
Combine LLMs with validators. Set confidence thresholds, add voting, implement fallbacks.
- • Uncertainty thresholds
- • Majority voting
- • Automatic retries
Workflow Patterns
Common patterns that eliminate hallucinations
Sequential with Validation
Each model output gets validated before the next step. If confidence is low, retry or use fallback. Perfect for high-stakes processes.
Example:
Contract Review Pipeline
- 1. Extract key terms (NER Model)
- 2. Classify risk level (Classification)
- 3. Generate summary (LLM)
- 4. Route for approval (Logic)
Example:
Customer Service Hub
- • Sentiment analysis in parallel
- • Intent classification in parallel
- • Knowledge retrieval in parallel
- • Combine results for response
Multi-Model Voting
Multiple specialized models vote on the answer. Majority wins. Disagreement triggers human review. Reduces hallucinations by 80%.
Uncertainty Routing
Route based on confidence scores. High confidence: automate. Low confidence: human review. Never let uncertain outputs through.
Example:
Fraud Detection System
- 1. Score transaction risk
- 2. If high: Block + Manual review
- 3. If medium: Additional checks
- 4. If low: Auto-approve
In Production Today
Real workflows in production
E-commerce Order Processing
7 models working together: inventory check, fraud detection, address validation, shipping optimization, customer notification, invoice generation, and feedback collection.
Healthcare Intake Automation
Extract symptoms from forms, classify urgency, check insurance, schedule appointments, send reminders, update records - all in one workflow.
Legal Document Analysis
Parse contracts, extract obligations, identify risks, compare to templates, suggest revisions, track changes - replacing entire paralegal workflows.
Social Media Management
Monitor mentions, analyze sentiment, generate responses, check brand compliance, schedule posts, track engagement - fully automated brand presence.
Why this eliminates hallucinations
Single LLMs hallucinate because they're probabilistic. They guess and can't verify their own outputs. That's why ChatGPT makes things up.
Specialized model workflows fix this. Different models check each other. Validators catch errors. Confidence thresholds prevent bad outputs. This is how production AI actually works.
Identify Risks
Where could hallucinations hurt your business?
Add Validators
Insert checks, thresholds, and voting
Deploy Safely
Ship with confidence - no hallucinations
Ship AI without the hallucination risk
Engineers get reliability. Builders get simplicity. Everyone gets AI that works.
Drag, drop, add validators, deploy. No more "it worked in the demo."