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O INNOV GROUP

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ChatGPT & LLM Integration
AI Integration

ChatGPT & LLM Integration

Large language models wired directly into your product, workflow or platform.

ChatGPT, Claude, Gemini and open-source models are reshaping how products work — but only when they are integrated properly. We design and implement LLM-powered features that are accurate, fast, cost-efficient and built around your actual use case. Prompt architecture, retrieval-augmented generation, fine-tuning and production monitoring included. No generic wrappers. No shortcuts.

How we can help

Pick the right model for your use case, latency requirements and cost profile — from GPT-4o to open-source alternatives running on your own infrastructure.

Design the prompt architecture with system prompts, few-shot examples and chain-of-thought patterns that produce reliable, consistent outputs.

Build RAG pipelines so your LLM answers from your own documents and knowledge base, not just its training data.

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Why it matters

LLMs that work reliably in your specific context

Prompt architecture that produces consistent outputs
Reliability

Prompt architecture that produces consistent outputs

Raw API calls to an LLM are not a product. We engineer the prompt layer, system instructions and output validation so your AI feature behaves predictably — even at the edge cases.

  • Structured prompt engineering
  • Output validation & fallbacks
  • Few-shot calibration
  • Regression test suites
Retrieval-augmented generation from your own data
Knowledge

Retrieval-augmented generation from your own data

LLMs hallucinate when they lack context. We build RAG pipelines that retrieve relevant chunks from your documents, databases and knowledge bases before the model responds — so answers are grounded in your truth.

  • Vector database integration
  • Chunking & embedding strategy
  • Hybrid semantic + keyword search
  • Source attribution
What we build

Capabilities

Everything within this service, engineered end to end. No generic templates. No shortcuts.

Core Capability

LLM API integration

OpenAI, Anthropic, Google and open-source model integration for any platform.

How we deliver this
What We Cover

One relationship that covers every layer of your AI and automation build.

Strategy, architecture, execution — coordinated by one team with one shared understanding of your goals. No gaps. No briefing five vendors.

O Innov Group
GPT+
OpenAI, Anthropic, Gemini & open-source
RAG
Knowledge-grounded responses
<200ms
Target streaming response initiation
Eval
Harness built for every integration
How we deliver

A process built on rigor

We research before we execute. Every engagement follows a structured path from discovery to long-term partnership.

STEP 01

Use case definition

We map exactly where an LLM adds value and where it doesn't — before a line of code is written.

STEP 02

Architecture design

Model selection, prompt design, retrieval strategy and integration plan.

STEP 03

Build & evaluate

Integration, eval harness and iterative refinement against your quality bar.

STEP 04

Deploy & monitor

Production deployment with logging, cost monitoring and continuous improvement.

What you walk away with

Concrete deliverables, documented and built to last.

Model integrationPrompt libraryRAG pipelineVector databaseStreaming UIEval harnessCost dashboardMonitoring
Questions

Frequently asked

Still wondering about something? Reach out and we'll answer directly.

We integrate with OpenAI, Anthropic, Google Gemini and open-source models (Llama, Mistral, etc.) depending on your requirements.

Retrieval-augmented generation lets your LLM answer from your own documents and data. If your use case requires factual accuracy about your business, you almost certainly need it.

Through caching, model routing, prompt compression and monitoring — we track cost per call from day one.

Yes — when prompt engineering alone can't achieve the consistency you need, we design and run fine-tuning pipelines.

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