Generative AI.
Intelligence That Creates. Systems That Think. Outcomes That Scale.
Entiovi's EnGen practice - Generative AI is built for organizations that want to move beyond pilots and run production-grade AI that earns its place in the technology stack.
Generative AI is not a single tool - it is a discipline stack. Entiovi's practice is organized into five interconnected capability areas that span the full journey from raw model selection to a governed, production-deployed enterprise system.
Teaching AI to speak the language of a specific domain - literally and technically.
Entiovi works with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), Direct Preference Optimization (DPO), and Low-Rank Adaptation (LoRA / QLoRA) - selecting the right approach based on data volume, latency requirements, and deployment constraints. For organizations that cannot route data through third-party APIs, models are built and served entirely within the client's own infrastructure.
Explore LLM Development & Fine-tuning 02Giving AI a live, accurate memory - connected to an organization's own knowledge, not frozen in training data.
Entiovi builds RAG systems using dense retrieval (bi-encoder, cross-encoder re-ranking), hybrid search (BM25 + vector), and evaluation frameworks that measure faithfulness, relevance, and answer completeness.
Explore Retrieval-Augmented Generation 03AI that works across all the data formats an enterprise actually produces - not just text.
Entiovi designs and deploys systems built on architectures including GPT-4o, Gemini 1.5 Pro, LLaVA, Whisper, and custom vision-language models (VLMs). Deployment use cases range from automated document processing - invoices, lab reports, clinical notes with embedded images - to quality inspection systems that combine live camera feeds with natural language alerts.
Explore Multimodal AI 04The craft and science of making AI models perform reliably, consistently, and measurably in production.
Entiovi builds prompt pipelines, evaluation harnesses, and LLM-as-judge frameworks that systematically measure model performance across accuracy, tone, safety, and task completion. The toolset includes RAGAS, LangSmith, PromptFlow, and custom evaluation suites - giving organisations a scientific, reproducible method for improving AI applications over time.
Explore Prompt Engineering & Evaluation 05From a working prototype to a production system - secure, governed, monitored, and built to scale.
Entiovi architects GenAI deployment stacks on AWS Bedrock, Azure OpenAI Service, GCP Vertex AI, and on-premises GPU clusters. LLMOps pipelines are implemented end-to-end - versioned prompts, A/B testing frameworks, cost monitoring, drift detection, and human-in-the-loop review workflows. Guardrail layers are built using NeMo Guardrails and custom policy engines to ensure AI behaviour stays within the boundaries that the business and its regulators require.
Entiovi's GenAI engineering practice is built on a foundation that goes deeper than API integration. The team works at the architecture layer across five domains.
Evaluation and deployment spans the full model spectrum - proprietary frontier models (OpenAI, Anthropic, Google), open-weight models (Llama 3.1, Mistral, Phi-3, Qwen), and domain-specific models (BioMedLM, FinBERT-derived architectures, Code Llama). Model selection is driven by a structured evaluation matrix covering task performance, context window requirements, total cost of ownership, data residency constraints, and latency SLAs.
High-throughput production systems are built on optimized inference stacks using vLLM (PagedAttention for memory efficiency), TensorRT-LLM, and ONNX Runtime for edge deployment. Quantization strategies (INT4, INT8, FP16) are tuned to the hardware profile - maximizing performance without sacrificing output quality.
Complex multi-step GenAI workflows are orchestrated via LangGraph, LlamaIndex, and custom DAG pipelines - handling context management, memory, tool use, and parallel inference paths.
Every production GenAI system ships with a full observability layer: trace-level logging, latency profiling, token cost accounting, hallucination detection metrics, and automated regression testing across prompt versions.
Security implementation covers prompt injection defences, output sanitization, PII detection and redaction pipelines, role-based access to model capabilities, and audit logging built to satisfy SOC 2, ISO 27001, and GDPR requirements.
GenAI has been deployed across healthcare, financial services, logistics, manufacturing, and government. Each engagement sharpens the practice. No patterns are locked to a single vertical.
Entiovi is not a reseller for any single cloud or model provider. Architecture recommendations are driven by the client's constraints, not commercial relationships.
Published research is tracked and translated into production-ready engineering patterns faster than most enterprises can complete a vendor evaluation. Clients benefit from the frontier without carrying the research risk.
Every system is designed with auditability, explainability, and compliance built in from the start - not retrofitted at the end.
An audit of the existing data landscape, identification of the highest-ROI GenAI use cases, infrastructure readiness assessment, and a prioritised roadmap as a deliverable.
A working PoC built on actual client data, within the actual client environment, measured against agreed success metrics.
Full-stack engineering: model pipeline, integration layer, observability, security hardening, and handover documentation.
Post-deployment, Entiovi offers managed LLMOps, continuous model evaluation, cost optimisation, and capability extension as the model landscape evolves.
Every week, competitors are learning what works in GenAI. The cost of waiting is not just opportunity cost - it is organizational lag that compounds. Entiovi's team will give an honest assessment of what is feasible, what is valuable, and what the right first step looks like.
