← BackThe Code LawyersGet Started

Custom LLM Integration

Enterprise-grade AI embedded in your stack.

We embed fine-tuned LLMs directly into your systems. RAG pipelines, vector search, retrieval evaluation. SOC2-compliant. Cost controls included.

System Architecture

Custom LLM Integration

Inputs

Existing system architectureData sources (docs, DBs, APIs)Use case definition

Engineering

Model selection (GPT-4, Claude, open-source)RAG pipeline (embeddings -> vector DB -> retrieval)Prompt framework (versioned, evaluated)API layer (auth, rate limits, logging)Cost and latency monitoring

Outputs

LLM-powered feature in productionEvaluation benchmarks + test suiteDocumentation + runbooks

Post-Launch

Model performance monitoringPrompt drift detectionCost optimization reviews

How It Works

Our process, step by step

1

Requirements & Scope

Define use cases, data sources, and success metrics for your LLM integration.

2

Data Preparation

Clean, structure, and embed your proprietary data for retrieval.

3

Pipeline Development

Build RAG pipelines, fine-tuning workflows, and evaluation frameworks.

4

Deployment & Monitoring

Deploy to production with monitoring, cost controls, and continuous evaluation.

Technology

Tools & Stack

OpenAI
Anthropic
LangChain
LangGraph
Pinecone
Chroma
Python
FastAPI

What You Get

Deliverables

Custom LLM integration in your existing systems

RAG pipeline with your knowledge base

Evaluation framework and monitoring dashboard

Cost optimization and rate limiting

Documentation and team training

"LLMs are powerful but unpredictable. We treat every integration as a systems engineering problem with defined inputs, tested outputs, cost monitoring, and fallback behavior."

Ready to get started?

Tell us about your project and we'll get back to you within 24 hours.

Start This Project