What Generative AI capabilities does VividLogic offer?

Our Generative AI work covers LLM Integration, RAG Architecture, Prompt Engineering, and AI Agents.

Generative AI

We build generative AI solutions that ship to production. From LLM integration to RAG architectures, our GenAI engineering delivers systems with evaluation harnesses, cost optimization, and observability.

Our Approach

We start with your use case and design GenAI solutions that are production-ready. Our GenAI projects include prompt engineering, evaluation frameworks, and cost optimization.

Capabilities

LLM Integration

OpenAI, Anthropic, Azure OpenAI integration with proper guardrails.

RAG Architecture

Retrieval-augmented generation with vector databases.

Prompt Engineering

Optimizing prompts for accuracy, cost, and latency.

AI Agents

Building autonomous agents that handle complex workflows.

How It Comes Together

A typical AI system architecture

Data

Training and evaluation data sourced, labeled, and validated.

Model

Models trained, fine-tuned, or integrated via API.

Evaluate

Accuracy, cost, and latency benchmarked before release.

Deploy

Production rollout with guardrails and human oversight where needed.

Monitor & Retrain

Drift detection triggers retraining before quality degrades.

In Practice

A GenAI demo is easy; a GenAI feature that survives real users, cost constraints, and edge cases in production is the actual hard problem. We build evaluation harnesses before we build the feature itself, a documented set of test cases and quality criteria that let us measure whether a prompt or model change is actually an improvement, rather than eyeballing a handful of outputs and hoping. RAG architectures are only as good as their retrieval step, and we spend proportionally more engineering effort there, chunking strategy, embedding model selection, hybrid search, than on the generation step most teams focus on by default. Cost and latency get modeled explicitly against expected usage volume before launch, since a GenAI feature that's economically unworkable at scale is a failure discovered far too late if nobody ran the numbers up front. We also build guardrails, input validation, output filtering, and fallback behavior for low-confidence responses, into every system we ship, because a generative feature that occasionally produces something wrong or inappropriate in front of a customer is a real business risk, not an edge case to shrug off.