Beyond ChatGPT: Enterprise-Grade GenAI
Consumer AI tools leave value on the table. We build custom GenAI systems trained on your data, integrated with your workflows, and secured to your compliance requirements.
Faster content creation, document analysis, and knowledge synthesis
Data privacy—your information never trains public models or leaks to competitors
Accuracy on domain-specific tasks with custom fine-tuning and RAG
Why Generic AI Falls Short
Generic ChatGPT/Claude
- • No knowledge of your business, products, or customers
- • Data shared potentially trains public models
- • Generic responses lacking domain expertise
- • No integration with your systems or workflows
- • Can't access internal documents or databases
- • Compliance and security gaps
Custom GenAI Integration
- • Deep knowledge of your domain and context
- • Private deployment—your data stays yours
- • Expert-level responses for your industry
- • Seamless integration with existing tools
- • Access to your knowledge bases and systems
- • Enterprise security and compliance built-in
High-Impact GenAI Applications
Where GenAI delivers measurable ROI across enterprise functions
Content Creation & Marketing
Generate personalized marketing copy, product descriptions, email campaigns, and social content—maintaining brand voice at scale.
Knowledge Management & Search
Transform document chaos into instant answers. Employees ask questions in plain English and get precise responses synthesized from your entire knowledge base.
Customer Experience & Support
Intelligent chatbots that understand context, resolve issues, and escalate thoughtfully— delivering exceptional experiences while reducing support costs.
Data Analysis & Insights
Natural language interfaces to complex data. Ask business questions in plain English and get instant analysis, visualizations, and recommendations.
Code Generation & Documentation
Custom coding assistants trained on your codebase, style guides, and architecture— accelerating development while maintaining consistency.
Contract & Legal Analysis
Automated review of contracts, RFPs, and legal documents—extracting key terms, identifying risks, and flagging deviations from standards.
Our GenAI Integration Approach
Use Case Discovery & ROI Modeling
Identify highest-value GenAI applications across your organization. Map workflows, estimate productivity gains, and prioritize based on business impact.
Model Selection & Architecture Design
Choose optimal models (GPT-4, Claude, Llama, domain-specific) and deployment approach (API, on-premise, hybrid). Design RAG architecture for knowledge integration.
Data Preparation & Knowledge Base Design
Curate, clean, and structure your domain-specific data. Build vector databases, design chunking strategies, and implement semantic search.
Prompt Engineering & Fine-Tuning
Develop optimized prompts for your use cases. Fine-tune models on your data when needed for maximum accuracy and domain expertise.
Integration & User Experience
Embed GenAI capabilities into existing tools and workflows. Design intuitive interfaces that feel native, not bolted-on.
Security, Compliance & Governance
Implement data privacy controls, output validation, bias monitoring, and audit trails. Ensure compliance with industry regulations.
Monitoring & Continuous Improvement
Track usage, quality, and ROI. Implement feedback loops for continuous refinement. Retrain and optimize as your business evolves.
What You'll Achieve
Exponential Productivity
Tasks taking hours now take minutes—content creation, analysis, summarization at machine speed
Personalization at Scale
Deliver customized experiences to every customer without scaling headcount
Institutional Knowledge Accessible
Decades of documents instantly searchable and synthesizable by anyone
Better Decision-Making
Instant analysis of complex data enabling faster, more informed choices
Competitive Differentiation
Custom AI capabilities become proprietary advantages
Cost Optimization
Right-sized models and efficient architectures minimize AI spending while maximizing value
Frequently Asked Questions
How do you keep our data private and secure?
We deploy private instances or use enterprise API tiers with data isolation guarantees. Your data never trains public models or leaves your control. We implement encryption, access controls, and audit trails meeting enterprise security standards.
What's the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) connects models to your knowledge base in real-time— best for frequently changing information. Fine-tuning retrains models on your data—best for specialized styles or technical domains. Often we use both together.
How do you prevent hallucinations and ensure accuracy?
Multi-layer approach: RAG grounds responses in source documents, confidence scoring flags uncertain outputs, fact-checking validates against trusted sources, and human review for high-stakes applications. We measure and optimize accuracy continuously.
What's the typical timeline from start to production?
Simple integrations (chatbot, document Q&A) can be production-ready in 4-8 weeks. Complex applications with custom fine-tuning and multi-system integration typically take 3-4 months. We deliver value incrementally with pilots before full rollout.
How much does GenAI cost to run at scale?
Costs vary by volume and model choice. We optimize by selecting appropriately-sized models (not always the largest), caching frequent queries, and using hybrid approaches. Most clients see costs of $0.01-0.10 per user interaction, far less than human labor costs replaced.