LLM Integration

Wire large language models into your existing product, backend, and data securely, reliably, and without a rebuild.

API Integration RAG Model Selection Cost Optimization Security
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Overview

What Our LLM Integration Team Delivers

Adding an LLM to your product is easy to prototype and hard to get right in production. Latency, cost, hallucination, and data privacy all need real engineering, not just an API key. We integrate LLMs such as Claude, GPT, and open-source models into existing applications, connecting them to your databases, internal tools, and business logic through clean, maintainable architecture.

We've integrated LLMs into iGaming platforms for player insights and support, FinTech products for document and transaction intelligence, and Healthcare systems for clinical documentation support, always with attention to data residency, access control, and audit logging that regulated industries require.

Where a single model call isn't enough, we build retrieval pipelines, function-calling layers, and caching strategies that keep responses accurate and costs predictable at scale.

When this service helps most

  • You have a working product and want to add AI features without a rewrite
  • Current AI prototype works in a demo but breaks down at real usage volume
  • Need to control LLM costs that are scaling faster than usage
  • Regulated data requires careful handling around the model call
Key Capabilities
Model selection and vendor evaluation
API integration into existing frontend/backend systems
RAG pipeline design for grounded, accurate responses
Function/tool calling for structured actions
Cost, latency, and token-usage optimization
Data privacy, access control, and audit logging
How We Work

Our LLM Integration Process

01
Requirements & Data Audit
02
Model & Vendor Selection
03
Architecture Design
04
Integration Build
05
Security & Compliance Hardening
06
Testing & Load Validation
07
Deployment & Cost Monitoring
Tech We Use

Technology Stack

Python
Node.js
Anthropic Claude API
OpenAI API
Python
Node.js
Anthropic Claude API
OpenAI API
LangChain
Pinecone / pgvector
Redis
FastAPI
LangChain
Pinecone / pgvector
Redis
FastAPI
Flexible Engagements

Choose the Right Delivery Model

We keep engagement models flexible so you can start small, move fast, or scale a dedicated product team when the roadmap grows.

01

Fixed Price

Best For: Well-defined projects

Clear scope, fixed budget, defined milestones. Perfect for MVPs and Phase 1 builds.

Clear milestones & budgets
Defined requirements upfront
Ideal for MVPs and POCs
No surprise invoices
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02

Time & Material

Best For: Evolving requirements

Agile-first approach where you pay for actual hours worked. Maximum flexibility.

Agile-first approach
Pay for actual hours worked
Scale team up or down
Weekly progress reports
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03

Dedicated Team

Best For: Long-term product dev

Fully embedded engineers, daily standups, and CTO-level technical oversight.

Full-time dedicated engineers
Daily standups & demos
CTO-level oversight
Transparent monthly billing
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Common Questions

Frequently Asked Questions

Straight answers about model choice, existing codebases, costs, and production readiness.

It depends on your accuracy needs, budget, data privacy requirements, and latency tolerance. We benchmark options against your actual use case before recommending one.
In most cases, yes. LLM integration typically adds a service layer and API calls into your current architecture rather than requiring a full rewrite.
We use caching, prompt optimization, smaller models for simple tasks, and usage monitoring to keep token costs predictable as your traffic grows.
Ready to Build?

Ready to Integrate an LLM Into Your Product?

Tell us what you're building. We'll recommend the right model and integration path.