Osta's diagnostic AI achieves high accuracy rates through trade-specific training and continuous improvement mechanisms.
Accuracy performance:
The diagnostic system demonstrates strong reliability:
Routine repairs - 85-90% accuracy for common residential issues
Standard installations - 90-95% accuracy for typical installation requirements
Complex problems - 70-80% accuracy for multi-system or intermittent issues
Emergency diagnostics - 80-85% accuracy for urgent safety concerns
Accuracy rates are based on completed job comparisons where initial AI diagnosis is verified against actual work performed.
Factors contributing to accuracy
Training data quality
Historical database of thousands of completed service jobs
Professional verification of diagnoses and solutions
Regional variation in building codes and requirements
Seasonal patterns and common failure modes
Trade-specific expertise
Separate diagnostic engines for electrical, plumbing, HVAC, and handyman
Input from licensed professionals in each trade
Industry-standard troubleshooting methodologies
Current code requirements and best practices
Adaptive questioning
Question sequences adjust based on answers
Follow-up questions target specific diagnostic branches
Visual verification through photo/video analysis
Contextual factors (building age, system type, location)
Real-time validation
Material pricing verified against major supplier databases
Labor estimates calibrated to regional market rates
Permit requirements checked against local regulations
Safety considerations flagged automatically
Accuracy limitations
The AI diagnostic system has inherent constraints:
Information quality dependency
Accuracy depends on complete, accurate information from users
Vague or incomplete answers reduce diagnostic precision
Inability to perform physical inspections limits certainty
Hidden damage or inaccessible areas cannot be evaluated remotely
Complex scenario challenges
Multiple interacting problems may require sequential diagnosis
Intermittent issues are harder to diagnose without observation
Rare or unusual failures may not match training data patterns
Building modifications or non-standard installations add complexity
Visual inspection gaps
Cannot assess internal damage without seeing inside walls/systems
Difficult to evaluate structural integrity remotely
May miss secondary issues not mentioned by user
Cannot test electrical voltage, water pressure, or HVAC performance
When on-site assessment recommended
The AI recognizes situations requiring professional inspection:
Complex diagnostic scenarios
Multiple potential causes with similar symptoms
Intermittent problems that aren't currently occurring
Safety concerns requiring immediate professional evaluation
Issues involving multiple interacting systems
Hidden or inaccessible areas
Problems inside walls, ceilings, or underground
Attic or crawlspace issues requiring physical access
Electrical panel internals requiring meter testing
Plumbing behind finished surfaces
Code compliance verification
Major installations requiring permit inspection
Older systems that may not meet current code
Commercial properties with specific requirements
Situations where liability requires professional certification
Continuous improvement process
Osta enhances diagnostic accuracy through:
Job completion feedback - Professionals report actual findings vs. AI diagnosis
Pattern analysis - Identify common diagnostic errors and adjust algorithms
Training data expansion - Add new scenarios and solutions to knowledge base
Professional review - Licensed technicians evaluate and improve diagnostic trees
Regional calibration - Adjust for location-specific patterns and requirements
Pricing accuracy
Material and labor estimates achieve separate accuracy levels:
Material pricing
95%+ accuracy for standard materials (updated daily from supplier databases)
Higher variance for specialty items or market shortages
Regional availability affects precision
Rush orders or unusual specifications may vary
Labor estimation
85-90% accuracy for standard jobs in typical conditions
Property-specific factors (access, age, modifications) may affect actual time
Complexity revealed during work may require adjustment
Regional labor market variations impact precision
User impact of diagnostic accuracy
When diagnosis is accurate
Booking with confidence in scope and pricing
Minimal surprises during service delivery
Efficient professional execution
Costs align with expectations
When diagnosis needs refinement
Professional assesses on-site and provides updated information
You approve any scope or price changes before work proceeds
Additional findings documented and explained
Updated invoice reflects actual work performed
Transparency about uncertainty
The AI communicates confidence levels
High confidence when symptoms clearly indicate specific problem
Medium confidence when multiple likely causes exist
Low confidence when on-site inspection recommended
Explicit disclaimers when diagnosis has significant uncertainty