Question 1

How can enterprises safely use generative AI like ChatGPT?

Accepted Answer

Enterprise-safe generative AI requires: private model deployments (not public APIs for sensitive data), prompt guardrails to prevent harmful outputs, data loss prevention controls, audit logging for compliance, and human-in-the-loop workflows for critical decisions. We implement all these through our secure GenAI platform.

Question 2

What is RAG (Retrieval-Augmented Generation) and why do enterprises need it?

Accepted Answer

RAG combines large language models with your enterprise knowledge base, so AI responses are grounded in your actual data rather than general training. This eliminates hallucinations, ensures accuracy, and makes GenAI actually useful for enterprise use cases like customer support, documentation search, and expert systems.

Question 3

Should we fine-tune an LLM or use RAG for our enterprise AI?

Accepted Answer

RAG is typically better for most enterprise use cases -it's faster to implement, easier to update, and keeps your data secure. Fine-tuning is reserved for specialized use cases requiring domain-specific language or behaviors. We often recommend a hybrid approach: RAG for knowledge retrieval plus light fine-tuning for tone and format.

Question 4

How do you prevent AI hallucinations in enterprise applications?

Accepted Answer

We implement multiple safeguards: RAG architecture to ground responses in factual data, confidence scoring to flag uncertain outputs, citation requirements so users can verify sources, human review workflows for critical decisions, and continuous monitoring to catch and correct errors. This reduces hallucination rates to under 2%.

Question 5

What's the cost of implementing enterprise generative AI?

Accepted Answer

Costs vary significantly based on scale and approach. A departmental chatbot might cost $50-100K to implement. Enterprise-wide GenAI platforms range from $500K-2M. However, ROI is substantial -we typically see 40-60% reduction in knowledge worker time on routine tasks, with most implementations paying back within 12-18 months.

Generative AI Products Built for Enterprise Use

The Enterprise GenAI Challenge

Uncontrolled Risk

Governance Gaps

Integration Friction

Product-Led GenAI Approach

Secure by Design

Domain-Specific Intelligence

Human-in-the-Loop

What We Deliver

Enterprise Ready by Design

Sovereign Infrastructure

Governance & Compliance

Strategy-Led Enablement

Discover & Define

Configure & Deploy

Adopt & Optimize

Generative AI FAQs

Move GenAI from Experimentation to Production