LLM systems grounded in your actual data.
Feeble builds LLM and retrieval-augmented generation systems that connect models to the right documents, transcripts, records, and workflow context before asking them to answer or act.
Automate the loop after you understand the bottleneck.
People ask the same questions even though the answer exists somewhere.
Search returns documents, but users still cannot find the exact answer or section.
AI outputs sound plausible but are not grounded in approved source material.
Support, education, or operations teams spend too much time retrieving context manually.
Production systems, not prompt demos.
Source inventory and authority rules for documents, transcripts, records, articles, tickets, and internal notes.
Chunking, embedding, retrieval, ranking, and citation patterns matched to the content type.
LLM workflows for question answering, summarization, extraction, classification, and content generation.
Evaluation sets that test answer quality, retrieval accuracy, refusal behavior, and edge cases.
Interfaces, automations, or internal tools where users can ask questions and inspect source context.
Logging and review loops for improving prompts, retrieval, and source coverage after launch.
From workflow map to live automation.
Audit the corpus
We identify source types, freshness requirements, permissions, duplicate content, and which materials should be trusted.
Design retrieval
We choose chunking, embeddings, filters, ranking, citations, and fallback behavior based on how users ask questions.
Build the LLM flow
We connect retrieval, prompts, model calls, tools, interfaces, and downstream automations into one tested workflow.
Evaluate answers
We test real questions, missing context, confusing sources, hallucination risk, and source attribution before launch.
Improve from usage
We monitor logs, failed questions, bad retrieval, and content gaps so the system gets better with real use.
The architecture changes with the workflow.
- RAG quality depends more on source structure and retrieval design than model choice.
- Citations help users trust answers, but only if retrieval returns the right source chunks.
- Long documents need careful chunking or the model sees fragments without enough context.
- An assistant should refuse when the source material does not support an answer.
Related work
Educational Video Assistant
Eliminated 90% of repeat content requests.
Project Management & History Tracking
Created a searchable timeline that future AI agents now rely on.
AI-Powered Content Generation & Management
3–5x faster production without sacrificing brand quality.
What is RAG development?
RAG development means building retrieval-augmented generation systems. The system searches approved source material, gives relevant context to an LLM, and uses that context to answer, summarize, classify, or generate output.
When should a business use RAG?
Use RAG when the answer depends on private, changing, or domain-specific information that a model will not know by default. Common use cases include internal knowledge assistants, support tools, education search, policy lookup, and content workflows.
What data can an LLM system search?
An LLM system can search documents, transcripts, webpages, tickets, CRM records, SOPs, notes, product data, and other structured or unstructured sources. The important decision is which sources are authoritative enough to ground answers.
How do you reduce hallucinations in LLM apps?
We reduce hallucinations by grounding answers in retrieval, writing refusal rules, showing source context, testing against real questions, logging failures, and keeping humans in the loop for high-risk outputs.
Does every AI assistant need a vector database?
No. Small or highly structured knowledge bases may work with ordinary database search, filters, or deterministic lookup. Vector search becomes useful when users ask natural-language questions across larger or less structured content.