AI & LLM Integration Agency
Add a support chatbot, RAG search, or an LLM workflow to your product — built for production, not a demo.
Direct Answer
To add AI to an existing product — a support chatbot grounded in your docs, semantic search, or an LLM-driven workflow — you need production engineering, not a proof-of-concept: retrieval, caching, evaluation, guardrails, and cost control. Weblaud LLC builds production AI and LLM integrations (RAG pipelines, vector search, agent workflows) on a fixed 4–14 weeks sprint scope ($4,500 – $18,500), wired into the app and data you already have.
What We Build
RAG Chatbots Grounded in Your Data
Support and knowledge assistants that answer from your actual docs and database — with retrieval and caching, not hallucinated guesses.
Semantic & Vector Search
Search that understands meaning, not just keywords — powered by embeddings and a vector database tuned for your content.
LLM Workflows & Agents
Automate classification, extraction, drafting, and multi-step tasks with LLM workflows wired into your existing systems.
Production Guardrails
Evaluation, prompt versioning, cost controls, and fallbacks — the engineering that keeps an AI feature reliable and affordable at scale.
Frequently Asked Questions
- What's the best way to add an AI chatbot to my product?
Use a RAG (retrieval-augmented generation) approach: the chatbot retrieves relevant passages from your own documentation and data, then an LLM answers grounded in that context — which keeps answers accurate and current. Weblaud LLC builds these production RAG chatbots wired into your existing app and content.
- Should I fine-tune a model or use RAG?
For most product use-cases, RAG is the better starting point: it keeps answers grounded in your current data, is cheaper to run, and updates instantly when your content changes. Fine-tuning suits narrow style or format needs. Weblaud advises on the right approach for your case.
- Can you integrate AI into our existing application?
Yes — most AI engagements are integrations into an existing product rather than greenfield builds. Weblaud wires retrieval, LLM calls, caching, and guardrails into your current stack on a fixed 4–14 weeks sprint.
Tell us what you're building.
We'll come back within a day with a clear plan — no jargon, no lock-in, no pitch deck.
15-min session · No commitment · Response within 24 h