Production-ready AI that reduces costs, accelerates decisions, and scales safely across your organization.
Most companies treat generative AI like a science project—endless pilots that never reach production or create measurable business impact. Real enterprise value comes from AI that integrates into workflows, operates at scale, and delivers consistent outcomes your teams can trust. At Fornax, we build generative AI solutions that work in the real world: custom models that understand your business context, retrieval systems that ground responses in your data, and governance frameworks that keep everything compliant and auditable. We don't just deploy technology—we redesign processes around AI capabilities, ensuring adoption drives actual productivity gains and cost reductions.
Our approach combines technical depth with business pragmatism: models fine-tuned on your domain, RAG architectures that scale with your data growth, and monitoring systems that track both performance and business outcomes. The result is AI that becomes a competitive advantage, not just another tool.
How do we move beyond ChatGPT demos to production systems?
What's the ROI of building custom models versus using APIs?
What's the right architecture for our data and compliance requirements?
How do we ensure AI outputs are accurate and legally defensible?
How do we train our teams while maintaining security and control?
Domain-specific models fine-tuned on your data, optimized for your use cases, with clear performance benchmarks and improvement paths.
Retrieval-augmented generation systems that ground AI responses in your authoritative data sources, with version control and source attribution.
Seamless embedding of generative AI into existing business processes, from customer service to content creation to data analysis.
Built-in safety rails, audit trails, and approval workflows that keep AI outputs consistent with your brand and regulatory requirements.
Real-time tracking of model accuracy, user satisfaction, and business impact, with automated retraining and improvement cycles.
Practical workshops and documentation that get your teams productive with AI tools while maintaining quality and security standards.
How we build your solution
Map Use Cases & Value
Identify high-impact applications where GenAI can reduce costs or accelerate decisions, with clear success metrics.
Assess Data & Infrastructure
Evaluate your current data landscape, security requirements, and technical constraints to design the right architecture.
Design & Prototype
Build working prototypes that demonstrate value with your actual data and workflows, proving ROI before full deployment.
Implement & Scale
Deploy production systems with proper monitoring, governance, and user training to ensure adoption and sustained value.
Optimize & Evolve
Continuously improve model performance, expand successful use cases, and adapt to new AI capabilities and business needs.
Build vs. Buy Analysis
We model the total cost of ownership for custom development versus API usage, factoring in data sensitivity, usage patterns, and performance requirements. This prevents costly mistakes and ensures sustainable scaling.
Usage-Based Optimization
Smart caching, request batching, and model tiering reduce costs as volume grows. We design systems where increased adoption improves unit economics, not the reverse.
Measurable Business Impact
Every AI implementation maps to specific KPIs—customer response time, content production speed, or analysis accuracy. This makes budget conversations straightforward and renewal decisions obvious.
Content Quality Controls
Automated checks for accuracy, brand consistency, and regulatory compliance before any AI-generated content reaches customers or stakeholders.
Audit-Ready Documentation
Complete lineage tracking from data sources through model training to final outputs, with version control and approval workflows that satisfy enterprise requirements.
Risk-Appropriate Guardrails
High-stakes use cases get human review; routine tasks stay automated. This balance maintains both safety and productivity.
Evolving Best Practices
Your governance framework adapts to new model capabilities, industry regulations, and internal learning—keeping you compliant as AI advances.
Explore All Capabilities
Strategy and Transformation
We help leaders build strategies that don’t sit in decks, but those that scale, adapt, and deliver measurable value.
Data Foundation
A modern data foundation gives you one source of truth for analytics, AI, and decision-making - engineered for reliability, speed, and scale.
Advanced Analytics & Insights
We build analytics platforms and production models so leaders make faster, confident decisions at scale.
AI / ML Innovation
From robust AI engineering to production-grade LLM solutions and ML platforms, Fornax turns experimentation into scalable impact.
The break-even typically happens when you're processing 100,000+ requests monthly, have specific domain knowledge requirements, or need guaranteed data privacy. Custom models also give you control over costs, performance optimization, and fine-tuning for your exact use cases. We analyze your usage patterns and requirements to determine the most cost-effective approach.
We implement multi-layer validation: custom models trained on your approved content, RAG systems that pull from authoritative sources, automated quality checks, and human review workflows for high-stakes outputs. Every system includes source attribution and confidence scoring so users know when to trust or verify AI recommendations.
It depends on your risk tolerance and regulatory requirements. Options range from on-premises deployments and private cloud instances to carefully designed API integrations with data anonymization. We assess your specific compliance needs (GDPR, HIPAA, SOX) and design architectures that maximize AI value while maintaining security.
Success requires both technical integration and change management. We embed AI into existing workflows rather than creating separate tools, provide hands-on training with real work scenarios, and establish clear guidelines on when and how to use AI. Most importantly, we track adoption metrics and iterate based on actual usage patterns.
We design systems that can incorporate new models without rebuilding everything. Your custom fine-tuning, RAG infrastructure, and governance frameworks remain valuable regardless of the underlying foundation model. We help you evaluate new capabilities and integrate them strategically rather than chasing every release.
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