Generative AI Development Services Built on Your Data, Engineered for Accuracy

Fine-tuning changes how a model behaves: tone, structure, terminology. RAG changes what it knows by retrieving live information at query time. Fine-tuning wins on style and precision; RAG wins on accuracy, freshness, and traceability. Most production systems use both. Choosing one without evaluating the use case is one of the most common architectural mistakes enterprises make.

Evaluating GenAI isn't like testing traditional software. Quality requires multiple layers: automated benchmarks for factual accuracy, retrieval precision tests for RAG, human expert review, and adversarial testing to probe the model's limits. The priority metric depends on the use case: precision for legal drafting, hallucination rate for customer-facing systems. Any system deployed without a domain-specific evaluation framework is flying blind.

Yes, but only when compliance is built into the architecture from the start, not retrofitted afterward. The key decisions are where data is processed and stored, whether outputs are logged and auditable, whether sensitive information ever reaches an external model, and whether human review gates exist before outputs are acted upon. We design for HIPAA, GDPR, and EU AI Act requirements by default.

Build and run costs are two different budgets. Build covers architecture, development, and testing. Run costs depend on token volume, model tier, infrastructure, and maintenance. At frontier model pricing, 10,000 daily queries can cost tens of thousands per month. Right-sizing model selection; smaller models for simple tasks, larger only for complex reasoning, keeps production costs predictable.

Preventing misuse requires guardrails at multiple levels. Prompt injection protection stops users from crafting inputs designed to override system instructions. Role-based access ensures users only query data they're authorized to see. Output filtering catches PII or confidential data before it reaches the user. Scope guardrails define what the system can and cannot discuss. Full audit logging creates accountability.

ROI from generative AI is often measured incorrectly. Vanity metrics: output volume, query counts, and adoption rates tell you the system is used, not whether it delivers value. Metrics that matter: time recovered per process, error rate reduction, throughput increase, and cycle time compression. Establish a pre-deployment baseline for each; without it, you're measuring improvement against guesswork.

Enterprise subscriptions are good tools for general productivity. We'll tell you honestly if that's all you need. Custom generative AI is right when your use case depends on proprietary data, compliance rules out shared infrastructure, or your domain demands accuracy that a general-purpose model can't deliver. The mistake is using a subscription for a production-system problem, or building custom when a subscription would do the job.