Custom AI Solutions

Custom AI delivers documented ROI: AI-powered data integration delivers 3.7x average ROI (Integrate.io 2025). 62% of organizations expect over 100% ROI. McKinsey reports AI forecasting improves accuracy by ~10% and reduces costs up to 15%. Amazon generates 35% of purchases from AI recommendations. Predictive maintenance reduces costs 25-40%. AI churn prediction increases retention by 25% (Gartner 2025). Most clients see positive ROI within 6-12 months.

Custom AI costs in 2026: Proof-of-concept models $15,000-40,000 (4-6 weeks). Production-ready predictive models $40,000-80,000 (8-12 weeks). Enterprise personalization and NLP $80,000-150,000 (3-4 months). Full-scale platforms $150,000-500,000+ (4-6 months). We provide detailed ROI projections before any engagement.

Our 5-phase methodology: Discovery (1-2 weeks), Data Preparation (2-3 weeks), Model Development (3-6 weeks), Testing & Validation (2-3 weeks), Deployment (2-4 weeks). Simple predictive models take 6-10 weeks, mid-complexity solutions 10-16 weeks, enterprise platforms 4-6 months. We use agile methodology with bi-weekly deliverables.

LLM Fine-tuning adapts pre-trained models (GPT-4, Claude, Llama) to your domain, achieving 20-40% better accuracy. The enterprise LLM market will reach $32.82 billion by 2032 (31.4% CAGR). RAG (Retrieval-Augmented Generation) combines LLMs with your knowledge base–51% of enterprises use RAG (AICerts 2025). RAG reduces hallucinations and provides source-cited responses.

AI predictive maintenance analyzes sensor data to predict equipment failures before they occur. Results: 50% reduction in unplanned downtime, 25-40% maintenance cost savings, equipment life extended 20-40%. 78% of production facilities using AI reported positive results (Tech-Stack 2025). IoT Analytics found 95% of adopters report positive ROI with 5-10x returns within 2-3 years.

Yes, custom AI solutions are trained on your proprietary data for relevant, accurate insights. We help identify, collect, and prepare the right data. All data handling follows strict security protocols (HIPAA, SOC 2, GDPR, PCI-DSS). Note: 73% of AI failures stem from data quality issues–we help ensure your data is AI-ready.

AI churn prediction uses machine learning to identify customers at risk of leaving before they actually churn. Our models analyze:

• Usage patterns: Declining engagement, feature abandonment, login frequency drops
• Support interactions: Ticket volume, sentiment analysis, resolution times
• Customer health scores: Multi-factor scoring combining behavior, satisfaction, and lifecycle stage
• External signals: Market conditions, competitor activity, industry benchmarks

Results: Gartner reports AI churn prediction increases retention by 25%. Companies using predictive retention see 15-20% lower churn rates. Early intervention saves 40-60% of at-risk customers when triggered 30+ days before likely churn. The average cost of acquiring a new customer is 5-7x more than retaining an existing one.