Advanced Features Tutorial

This comprehensive tutorial covers DeepTalk’s most powerful capabilities, including AI service integration, semantic search, interactive chat, and workflow automation. You’ll transform from a basic user to a power user.

Tutorial Overview

What You’ll Accomplish

By the end of this tutorial, you’ll have:

✅ Set up and optimized external AI services (Speaches + Ollama)
✅ Mastered AI chat for interactive content exploration
✅ Implemented semantic search for powerful content discovery
✅ Customized analysis prompts and templates
✅ Created automated workflows and integrations
✅ Optimized performance for large-scale usage

Prerequisites

Completed Basic Workflow and Project Setup tutorials
Several transcripts and projects for testing advanced features
Willingness to install and configure external services
60-90 minutes for complete tutorial

What Makes This “Advanced”

This tutorial covers features that:

Require external service integration
Involve complex configuration and customization
Enable sophisticated analysis and automation
Support enterprise-scale usage patterns

Part 1: AI Service Integration

Step 1: Set Up Speaches for Enhanced Transcription

What is Speaches?

High-quality speech-to-text service using state-of-the-art Whisper models
Significantly better accuracy than basic transcription
Support for multiple languages and specialized models
Local installation for privacy and control

Installation Options:

Option A: Local Installation (Recommended)

# Install Speaches locally (example commands)
pip install speaches
speaches serve --port 8000

Option B: Docker Installation

# Run Speaches in Docker container
docker run -p 8000:8000 speaches/server

Option C: Remote Service

Use an existing Speaches instance on your network
Configure URL to point to remote service

Configure in DeepTalk:

Open Settings → Transcription tab
Set Service URL: http://localhost:8000 (or your Speaches instance)
Test connection: Click “Test” button to verify connectivity
Select model: Choose appropriate model for your content
- Small models: Fast, good for clear audio
- Medium models: Balanced speed/accuracy (recommended)
- Large models: Best accuracy, slower processing

Model Selection Guidelines:

Content Type           Recommended Model
─────────────────      ─────────────────
Clear meetings         faster-distil-whisper-medium.en
Technical discussions  whisper-large-v2
Multiple languages     whisper-large-multilingual
Noisy environments     whisper-large-v2
Real-time processing   faster-distil-whisper-small.en

✅ Checkpoint: Speaches is installed, configured, and successfully tested in DeepTalk.

Step 2: Set Up Ollama for AI Analysis

What is Ollama?

Local AI service for running large language models
Powers DeepTalk’s analysis, chat, and semantic search features
Privacy-focused with all processing on your machine
Support for various models optimized for different tasks

Installation Process:

1. Install Ollama:

# macOS/Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Windows: Download from ollama.ai

2. Download Models:

# Start with a capable general model
ollama pull llama2

# For better performance, consider:
ollama pull mistral
ollama pull codellama  # For technical content
ollama pull llama2:13b # Larger model for better analysis

3. Start Ollama Service:

ollama serve
# Service runs on http://localhost:11434 by default

Configure in DeepTalk:

Open Settings → Processing tab
Set AI Analysis URL: http://localhost:11434
Select model: Choose from downloaded models
Test connection: Verify Ollama is accessible
Configure analysis preferences: Set default analysis types and depth

Model Recommendations:

Use Case              Recommended Model    Rationale
─────────────────    ─────────────────    ─────────
General analysis     llama2               Good balance of speed/quality
Technical content    codellama            Better at technical terminology
Detailed analysis    llama2:13b           More thorough insights
Creative tasks       mistral              Better at creative interpretation
Multi-language       llama2               Good multilingual support

✅ Checkpoint: Ollama is running with appropriate models and successfully integrated with DeepTalk.

Part 2: Master AI Chat Conversations

Step 3: Interactive Content Exploration

Understanding Chat Modes:

Vector Search Mode:

Returns exact excerpts with relevance scores
Best for fact-finding and quote discovery
Fastest response time
No AI interpretation

RAG Mode (Recommended):

Retrieves relevant content and sends to AI for interpretation
Natural, conversational responses
Good balance of speed and comprehensiveness
Context-aware answers

Direct LLM Mode:

Sends full transcript to AI for comprehensive analysis
Most thorough understanding and context
Slower but most complete responses
Best for complex analysis

Advanced Chat Techniques

Progressive Questioning Strategy:

Start broad: "What were the main topics in this meeting?"
Focus: "Tell me more about the budget discussion"
Drill down: "What specific concerns were raised about the Q2 budget?"
Cross-reference: "How does this compare to previous budget discussions?"
Synthesize: "What patterns do you see in our budget planning process?"

Multi-Turn Analysis:

You: "Analyze the decision-making process in this transcript"
AI: [Provides initial analysis]
You: "What evidence supports the claim that Sarah was the primary decision-maker?"
AI: [Provides specific evidence and quotes]
You: "How could this decision-making process be improved?"
AI: [Provides recommendations based on analysis]

Cross-Project Exploration:

Start chat in project context to access multiple transcripts
Ask comparative questions: “How did the team’s concerns change between the March and April meetings?”
Track evolution: “Show me how the timeline discussion evolved across these meetings”
Identify patterns: “What themes appear in all of our project meetings?”

Chat Optimization Techniques

Context Configuration:

Chunk count: Start with 4-6 relevant chunks, adjust based on response quality
Chunk size: Use default settings initially, reduce if responses are too generic
Relevance threshold: Increase if getting too much irrelevant content

Query Optimization:

Be specific: “What did John say about the budget between minutes 15-20?” vs. “Tell me about budget”
Provide context: “In the context of Q2 planning, what were the main risks discussed?”
Use examples: “Find statements similar to ‘we need to accelerate the timeline’”

Response Quality Improvement:

Ask for citations: “Please include specific quotes and timestamps”
Request structure: “Organize your response as a numbered list”
Seek clarification: “Can you explain what you mean by ‘resource constraints’?”

✅ Checkpoint: You can effectively use AI chat to explore and analyze your transcripts with natural conversation.

Part 3: Advanced Search and Discovery

Step 4: Semantic Search Mastery

Beyond Keyword Search: Semantic search understands meaning, not just words:

Traditional Search Limitations:

Must use exact words that appear in transcripts
Can’t find related concepts expressed differently
Misses context and implied meanings

Semantic Search Capabilities:

Finds content by meaning and concept
Understands synonyms and related terms
Considers context and intent
Discovers implicit connections

Advanced Search Techniques

Concept-Based Discovery:

Instead of: "budget meeting"
Try: "financial planning discussion"
Find: Conversations about budgets, expenses, financial strategy, cost management

Instead of: "project delay"
Try: "timeline challenges"
Find: Discussions about delays, scheduling issues, resource constraints, missed deadlines

Natural Language Queries:

"What are the team's biggest concerns about the product launch?"
"Find all discussions about customer feedback and complaints"
"Show me conversations where people disagreed about the timeline"
"What did the engineering team say about technical feasibility?"

Cross-Transcript Pattern Discovery:

"Find similar discussions across all project meetings"
"What themes appear in both technical and marketing conversations?"
"Show me how the team's confidence changed over time"
"What topics generated the most debate or discussion?"

Search Workflow Integration

Search-to-Chat Pipeline:

Use search to find relevant content: “Find discussions about customer requirements”
Start chat with search results as context
Explore findings: “Analyze these customer requirement discussions for common themes”
Deep dive: “What specific features did customers request most often?”

Search-to-Analysis Workflow:

Search for pattern: Find all mentions of specific topics across projects
Aggregate results: Collect related content from multiple sources
Generate analysis: Use AI to analyze patterns across the search results
Export insights: Create reports based on search-driven discoveries

Search Optimization:

Use filters effectively: Date ranges, speakers, projects for focused discovery
Combine search types: Start semantic, refine with keyword search
Save useful searches: Create search templates for recurring analysis needs
Track search patterns: Learn what works for your content and use cases

✅ Checkpoint: You can effectively use semantic search to discover content and patterns that keyword search would miss.

Part 4: Custom Analysis and Automation

Step 5: Advanced Analysis Customization

Custom Analysis Prompts: Create specialized analysis templates for your specific needs:

Industry-Specific Analysis:

Template: Software Development Team Analysis
Focus Areas:
- Technical decisions and architecture discussions
- Sprint planning and velocity estimation
- Bug reports and quality concerns
- Team coordination and blockers
- Code review feedback and technical debt

Custom Prompt:
"Analyze this software development team conversation focusing on:
1. Technical decisions made and their rationale
2. Identified blockers and proposed solutions  
3. Quality concerns and testing strategies
4. Team coordination and communication patterns
5. Sprint progress and velocity indicators"

Role-Based Analysis Templates:

Executive Summary Template:
- High-level decisions and strategic implications
- Resource and budget considerations
- Timeline and milestone impacts
- Risk identification and mitigation
- Stakeholder alignment and communication needs

Technical Deep-Dive Template:
- Detailed technical decisions and trade-offs
- Implementation challenges and solutions
- Performance and scalability considerations
- Security and compliance requirements
- Integration and dependency analysis

Automated Workflow Creation

Trigger-Based Analysis:

Content triggers: Automatic analysis when transcripts match specific criteria
Schedule triggers: Regular analysis of project content
Quality triggers: Analysis when transcription confidence meets thresholds
Collaboration triggers: Analysis when team reviews are complete

Batch Processing Workflows:

Weekly Project Review Automation:
Identify all transcripts from past week
Generate comprehensive cross-transcript analysis
Extract action items and decisions
Create executive summary report
Email stakeholders with findings
Update project tracking systems

Integration Workflows:

Export to project management: Automatically sync action items to Jira, Asana, etc.
CRM integration: Connect customer feedback insights to CRM systems
Document management: Archive completed analysis in SharePoint, Confluence
Notification systems: Alert stakeholders to critical decisions or issues

Template Management

Create Reusable Templates:

Analysis templates: Standard analysis approaches for different content types
Export templates: Formatted reports for different audiences
Project templates: Complete project configurations with settings and workflows
Integration templates: Standard API calls and data export formats

Template Organization:

Template Library Structure:
├── Analysis Templates
│   ├── Executive Summary
│   ├── Technical Deep-Dive
│   ├── Action Item Extraction
│   └── Risk Assessment
├── Export Templates
│   ├── Board Report Format
│   ├── Team Update Format
│   ├── Client Deliverable Format
│   └── Compliance Documentation
└── Workflow Templates
    ├── Weekly Review Process
    ├── Project Milestone Analysis
    ├── Customer Feedback Processing
    └── Quality Assurance Review

✅ Checkpoint: You have created custom analysis templates and automated workflows tailored to your specific needs.

Part 5: Performance Optimization and Scale

Step 6: Optimize for Large-Scale Usage

System Resource Management:

Memory allocation: Configure RAM usage for large transcript processing
CPU utilization: Balance processing speed with system responsiveness
Storage optimization: Manage disk space for growing content libraries
Network resources: Optimize external service calls and API usage

Content Organization at Scale:

Large Library Organization:
├── Project Hierarchy
│   ├── Active Projects (current work)
│   ├── Completed Projects (archived but accessible)
│   └── Template Projects (reusable configurations)
├── Content Categories
│   ├── By Department (Engineering, Marketing, Leadership)
│   ├── By Content Type (Meetings, Interviews, Presentations)
│   └── By Stakeholder (Internal, Customer, Partner)
└── Archive Strategy
    ├── Recent (last 3 months, full access)
    ├── Historical (3-12 months, search only)
    └── Archived (12+ months, minimal access)

Batch Processing Optimization:

Group similar content: Process related transcripts together
Schedule heavy operations: Run analysis during off-peak hours
Parallel processing: Configure multiple concurrent operations
Queue management: Prioritize urgent content over batch operations

Advanced Configuration

Service Optimization:

Speaches Configuration:
- Model selection based on content type and quality requirements
- Batch size optimization for multiple file processing
- Quality thresholds for automatic vs. manual review
- Custom vocabulary for domain-specific terminology

Ollama Configuration:
- Model selection for different analysis types
- Context window optimization for different content lengths
- Temperature settings for consistent vs. creative analysis
- Resource allocation for optimal performance

Integration Architecture:

Enterprise Integration:
├── Authentication and Security
│   ├── SSO integration for team access
│   ├── API security for external integrations
│   └── Data encryption for sensitive content
├── Business System Integration
│   ├── Project management tool synchronization
│   ├── CRM and customer data integration
│   └── Document management system archival
└── Monitoring and Analytics
    ├── Usage tracking and optimization
    ├── Performance monitoring and alerts
    └── Content analytics and insights

✅ Checkpoint: Your DeepTalk installation is optimized for large-scale usage with proper resource management and integration.

Part 6: Advanced Use Cases and Applications

Step 7: Implement Sophisticated Workflows

Customer Research Pipeline:

Upload customer interview recordings
Apply customer research analysis template
Extract themes, pain points, and feature requests
Cross-reference findings across multiple interviews
Generate comprehensive research report
Export insights to product management tools
Track feature requests through development cycle

Competitive Intelligence Workflow:

Process sales call recordings and competitive discussions
Identify competitor mentions and market position discussions
Analyze competitive positioning and differentiation
Track competitive landscape changes over time
Generate competitive intelligence reports
Alert stakeholders to significant competitive developments

Compliance and Audit Support:

Systematically process all recorded meetings and calls
Apply compliance analysis templates for regulatory requirements
Identify potential compliance issues or risks
Generate audit-ready documentation with proper citations
Maintain searchable archive for regulatory inquiries
Track compliance training and policy discussions

Enterprise-Scale Implementation

Multi-Team Deployment:

Organizational Structure:
├── Executive Team
│   ├── Board meeting analysis and decision tracking
│   ├── Strategic planning documentation
│   └── Stakeholder communication management
├── Product Teams
│   ├── Customer feedback analysis and product insights
│   ├── Feature prioritization and roadmap planning
│   └── Technical discussion documentation
├── Sales Teams
│   ├── Customer call analysis and CRM integration
│   ├── Competitive intelligence gathering
│   └── Deal review and strategy optimization
└── Support Teams
    ├── Customer issue pattern analysis
    ├── Training effectiveness assessment
    └── Process improvement identification

Governance and Standards:

Content standards: Quality requirements and review processes
Privacy policies: Handling of sensitive and confidential information
Access controls: Role-based permissions and data security
Retention policies: Archive and deletion schedules for different content types

✅ Checkpoint: You understand how to implement DeepTalk at enterprise scale with sophisticated workflows and governance.

Advanced Troubleshooting and Optimization

Performance Tuning

Common Performance Issues:

Issue: Slow transcription processing
Solutions:
- Upgrade to more powerful Speaches models
- Optimize audio file quality and format
- Configure parallel processing for multiple files
- Use faster storage (SSD) for temporary files

Issue: AI analysis takes too long
Solutions:
- Use smaller, faster Ollama models for routine analysis
- Reduce context size for analysis prompts
- Implement caching for repeated analysis
- Schedule intensive analysis for off-peak hours

Issue: Search performance degradation
Solutions:
- Optimize search indexes with regular maintenance
- Implement search result caching
- Use search filters to narrow scope
- Archive old content to reduce search scope

Advanced Configuration:

Resource Allocation:
- Memory: 8GB+ recommended for heavy usage
- CPU: Multi-core processors for parallel processing
- Storage: SSD recommended, 100GB+ for large libraries
- Network: High-bandwidth for external service integration

Service Configuration:
- Speaches: Optimize model selection and batch size
- Ollama: Configure appropriate models and context windows
- DeepTalk: Tune analysis settings and resource limits
- Integration: Optimize API calls and data synchronization

Quality Assurance at Scale

Automated Quality Control:

Quality Metrics:
├── Transcription Quality
│   ├── Confidence scores and thresholds
│   ├── Speaker identification accuracy
│   └── Technical term recognition
├── Analysis Quality
│   ├── Insight relevance and accuracy
│   ├── Action item identification completeness
│   └── Cross-transcript consistency
└── Process Quality
    ├── Processing time and efficiency
    ├── Error rates and failure handling
    └── User satisfaction and adoption

Continuous Improvement:

Usage analytics: Track how features are used and optimize accordingly
Quality feedback: Systematic collection and incorporation of user feedback
Performance monitoring: Regular assessment of processing speed and accuracy
Process refinement: Ongoing optimization of workflows and configurations

Mastery Assessment and Next Steps

Skill Verification Checklist

Technical Setup Mastery:

✅ External services installed and optimized
✅ Custom analysis templates created and tested
✅ Advanced search techniques demonstrated
✅ Automated workflows implemented
✅ Performance optimization configured

Workflow Proficiency:

✅ Complex project management executed successfully
✅ Cross-transcript analysis performed effectively
✅ Team collaboration workflows established
✅ Integration with business systems implemented
✅ Scaling strategies planned and documented

Strategic Application:

✅ Business value clearly demonstrated and measured
✅ Use cases expanded beyond basic transcription
✅ Organizational adoption strategy developed
✅ Quality assurance processes established
✅ Continuous improvement plan implemented

Advanced User Graduation

You are now a DeepTalk power user if you can:

Configure and optimize all major DeepTalk components
Create custom workflows that solve specific business problems
Integrate DeepTalk with other business systems and processes
Lead implementation for teams and organizations
Troubleshoot complex issues and optimize performance
Develop best practices and train other users

Continuing Your Journey

Community Contribution:

Share your workflows and templates with the community
Contribute to documentation and tutorial improvements
Help other users in forums and discussion groups
Provide feedback for product development

Advanced Applications:

Explore API integration for custom applications
Develop specialized analysis templates for your industry
Create training materials for your organization
Implement enterprise-scale deployments

Stay Current:

Follow DeepTalk updates and new feature releases
Participate in user communities and feedback programs
Experiment with new AI models and capabilities
Share learnings and best practices with peers

Congratulations! You have completed the advanced features tutorial and are now a DeepTalk power user. You have the skills to leverage DeepTalk’s full potential for sophisticated content analysis and business workflow integration.

Continue exploring: Browse the complete documentation → or Help others get started →