LLM integration and RAG
Texas Agentic Systems. LLM integration and RAG with permissions and evals
We connect large language models to your knowledge and tools with clear permissions, evaluation, and cost controls so answers and actions stay trustworthy.
When LLM integration is the right move
- Your product needs grounded answers from private data
- Teams paste sensitive docs into public chat tools today
- You need tool use with audit trails
- Pilot quality was fine until real users and real edge cases arrived
What a solid RAG build includes
- Data connectors, chunking, and freshness rules
- Retrieval design with citations where users need proof
- Prompt and policy layers for brand and safety
- Tool and API integration with least privilege
- Eval sets, regression checks, and monitoring
- Cost and latency budgets for production traffic
RAG, fine-tuning, or tools?
| Approach | Use when |
|---|---|
| RAG | Knowledge changes often and must stay attributable |
| Fine-tuning | Style or task behavior matters more than fresh facts |
| Tools / agents | The model must take actions, not only answer |
Many systems combine RAG with tools. That often becomes AI agent development or AI development services for the full product surface.
Why LLM integration projects stall without engineering
A prototype that answers from a folder of PDFs is easy. Production LLM integration is harder: permissions, document freshness, citations, latency, and cost all show up once real users arrive. Teams searching for RAG development services usually hit that wall after a promising pilot.
We treat retrieval and model wiring as product infrastructure. The goal is grounded answers and safe tool use your security team can accept, not a notebook that only works for the person who built it.
How a RAG engagement feels with us
You work with one senior solutions architect who designs connectors, retrieval, policies, and evals. Frameworks stay optional. Architecture and measurement stay mandatory.
We start with a bounded corpus and a clear user journey, then expand sources once quality holds. When the product needs a chat UI or multi-step actions, we connect the same foundation to AI chatbot development or agent work instead of bolting on a second stack.
LLM integration FAQs
Let's Chat About Your AI Development Needs
Tell us the data sources, the user journey, and your constraints. We will propose a RAG or hybrid design.