AI & LLM Integration for SaaS Products
We build LLM-powered features that ship to production — not demos. SemeLabs integrates OpenAI and Anthropic Claude models into web and mobile products using LangChain orchestration, retrieval-augmented generation (RAG) with Pinecone, and robust evaluation, so your AI features are reliable, fast, and cost-controlled.
What we build
- LLM integration with OpenAI, Anthropic Claude, and custom models
- Retrieval-augmented generation (RAG) pipelines with Pinecone and PostgreSQL
- AI agents and workflow automation with LangChain
- Intelligent chatbots and virtual assistants trained on your data
- Document processing, extraction, and summarization pipelines
- Prompt engineering, evaluation harnesses, and cost/latency optimization
Why teams choose SemeLabs for LLM work
Most agencies bolt a chat window onto an API key. We design the full lifecycle: data ingestion, chunking and embedding strategy, retrieval quality, guardrails, streaming UX, observability, and cost management. Your LLM feature behaves predictably under real user load.
Because we also build multi-tenant SaaS platforms, we integrate AI where it belongs — inside your product's permissions, billing, and data model — rather than as a disconnected add-on.
Our LLM stack
- Models: OpenAI GPT models, Anthropic Claude
- Orchestration: LangChain, custom TypeScript/Python pipelines
- Vector search: Pinecone, pgvector on PostgreSQL
- Infrastructure: AWS, Vercel, Node.js, Python, Redis
Frequently Asked Questions
Which LLM providers do you work with?
We primarily build with OpenAI and Anthropic Claude APIs, orchestrated with LangChain or custom pipelines. We help you choose the right model per feature based on quality, latency, and cost, and design the integration so you can switch providers later.
Can you add AI features to our existing SaaS product?
Yes. We integrate LLM features into existing codebases — respecting your authentication, multi-tenancy, and billing — and typically ship a first production feature within weeks, starting with a scoped pilot.
How do you keep our data private when using LLM APIs?
We use API tiers with no-training guarantees, redact sensitive fields before requests where needed, isolate tenant data in retrieval, and can deploy within your cloud environment. Data handling is documented as part of every engagement.
How much does LLM integration cost?
A scoped pilot (one production feature, e.g. RAG search or an assistant) is typically a fixed-price engagement delivered in a few weeks. Larger AI product builds are scoped after a free consultation.
Ready to build?
Tell us about your project and we'll get back to you within 24 hours.
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