Service / AI and Automation
RAG / LLM Integrations
Grounding language models in your own data, safely and accurately.
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Large language models are powerful but know nothing about your business by default. Retrieval-Augmented Generation (RAG) grounds a model's responses in your actual documents, product data, or internal knowledge, so it answers from real, current information instead of its general training data.
We design and build RAG pipelines and broader LLM integrations for internal knowledge tools, customer-facing search, and any application where accuracy grounded in your own data matters more than a generic AI answer.
Business problems solved
Internal knowledge scattered across docs, wikis and Slack that's genuinely hard to search. A generic AI tool that gives plausible-sounding but wrong answers about your specific product or policies. The need to use an LLM's capabilities without exposing sensitive data to it inappropriately.
Our approach
We design the retrieval layer carefully — what gets indexed, how it's chunked, how relevance is ranked — because a RAG system is only as good as what it retrieves; the generation step is almost secondary. We test extensively against real questions your users actually ask, not idealized ones, before calling it ready.
Data handling and access control are designed in from the start, particularly for anything touching sensitive or access-restricted information.
Technologies & tools
- LangChain
- LlamaIndex
- Pinecone
- Weaviate
- OpenAI API
- Anthropic API
- Python
Process
- 01
Data audit
Identify what content should be retrievable and how it needs to be structured.
- 02
Build the retrieval pipeline
Indexing, chunking and relevance ranking — the part that matters most.
- 03
Integrate the LLM
Connect retrieval to generation, with grounding and citation where useful.
- 04
Test against real questions
Validate accuracy against the actual questions users will ask, not idealized ones.
- 05
Launch & monitor
Track answer quality and retrieval accuracy in production, and tune over time.
Capabilities
- Retrieval pipeline tuned to your actual content structure
- Source citation so answers are verifiable, not just plausible
- Access-control-aware retrieval for sensitive content
- Support for multiple LLM providers, not locked to one
- Ongoing accuracy monitoring post-launch
- Clear data-handling design for sensitive information
Frequently asked
Talk to an expert.
Tell us what you're trying to move. We'll tell you whether RAG / LLM Integrations is the lever.