AI Implementation Guide

Most companies approach AI backwards - they start with cool technology and hope to find business value. We start with revenue opportunities and build AI solutions that directly impact your bottom line.

The Heveli AI Framework

1. Revenue Impact Assessment: Identify highest-value AI opportunities
2. Proof of Concept: Build small, measurable pilots
3. Production Deployment: Scale successful pilots
4. Continuous Optimization: Improve performance over time

Success Metric: Our AI implementations deliver measurable ROI within 90 days. Average ROI: 400% in the first year.

Sales & Marketing

• Lead Scoring: AI predicts which leads are most likely to convert
• Content Personalization: Dynamic website/email content based on user behavior
• Predictive Analytics: Forecast sales pipeline and identify at-risk deals
• Chatbots: Qualify leads and schedule demos 24/7

Customer Success

• Churn Prediction: Identify customers at risk of canceling
• Upsell Identification: Find expansion opportunities automatically
• Support Automation: AI-powered ticket routing and responses
• Health Scoring: Continuous customer health monitoring

Operations

• Process Automation: Automate repetitive manual tasks
• Demand Forecasting: Predict resource needs and capacity
• Document Processing: Extract data from invoices, contracts, etc.
• Quality Control: Automated quality checks and validation

Beyond traditional automation, AI agents and knowledge systems are transforming how teams work and serve customers.

Internal Knowledge Base Chatbots

• Writing Assistant: AI helps create blog posts, emails, proposals using company tone and data
• Research Agent: Instantly pulls relevant information from internal docs, past projects, case studies
• Onboarding Assistant: New employees get instant answers about processes, tools, company policies
• Technical Documentation: Developers query code docs, API references, deployment procedures

Customer-Facing AI Agents

• Technical Troubleshooting: Walk customers through complex setup procedures step-by-step
• Product Recommendations: Suggest products based on customer needs and usage patterns
• Intelligent Lead Scoring: Qualify prospects through conversational discovery
• Demo Scheduling: Smart calendar integration with context about prospect needs

Implementation ROI: Internal knowledge chatbots typically save 2-3 hours per employee per week by eliminating time spent searching for information.

Rather than big, risky AI projects, we build small pilots that prove value quickly.

POC Success Criteria

• Clear Metrics: Define exactly what success looks like
• Limited Scope: Focus on one specific use case
• Quick Timeline: 2-4 weeks maximum for POC
• Real Data: Use actual business data, not samples

Common POC Examples

Lead Scoring POC

• Data Required: 6 months of lead data + conversion outcomes
• Timeline: 3 weeks
• Success Metric: 20%+ improvement in conversion rate prediction
• Tech Stack: Python + scikit-learn + your CRM API

Churn Prediction POC

• Data Required: Customer usage data + churn history
• Timeline: 4 weeks
• Success Metric: Identify 70%+ of churning customers 30 days early
• Tech Stack: Python + TensorFlow + data warehouse

POC Best Practice: Always run POCs in parallel with current processes. This lets you compare AI performance against your existing methods.

Once POCs prove value, we build production-ready systems that scale.

Production Requirements

• Reliability: 99.9% uptime with proper error handling
• Scalability: Handle 10x current data volume
• Security: Data encryption, access controls, audit logs
• Monitoring: Real-time performance and accuracy tracking
• Integration: Seamless connection to existing tools

Technology Stack

For Small/Medium Companies

• Cloud Platform: AWS or Google Cloud
• Data Storage: PostgreSQL + S3/Cloud Storage
• ML Framework: Python + scikit-learn or TensorFlow
• API: FastAPI or Flask
• Monitoring: CloudWatch or Google Cloud Monitoring

Continuous Optimization

• Weekly: Review performance metrics and alerts
• Monthly: Analyze prediction accuracy and business impact
• Quarterly: Retrain models with new data
• Annually: Evaluate new AI opportunities and technologies

Implementation Timeline: 90 Days

• Week 1-2: Assessment & Planning
• Week 3-6: Proof of Concept
• Week 7-12: Production Development
• Week 13+: Optimization & Scaling

Ready to Implement AI? We've implemented AI solutions for 100+ companies, with an average ROI of 400% in the first year.