Antenor Services 04 of 07 RAG & Knowledge Systems
RAG development services
Retrieval-augmented generation, GraphRAG and semantic search over the documents and data your teams actually use. Custom AI knowledge assistants built on your data, running in your environment, answering with citations.
Who this is for
- CTOs and Heads of Data whose institutional knowledge is trapped in contracts, wikis, tickets and PDFs.
- Teams that tried a generic chatbot on company documents and watched it hallucinate.
- Regulated businesses that cannot ship documents to a third-party SaaS.
- Law firms and research teams sitting on decades of case law, filings and documents that must be searched by meaning, not keywords.
The problem we remove
The gap between a RAG demo and a RAG system is engineering: parsing real documents, preserving permissions, measuring accuracy, refusing gracefully. We build knowledge systems the way we build the rest of our software, with an evaluation harness and a success metric agreed before we write code.
What we build
Custom RAG development
End-to-end retrieval-augmented generation on your corpus: ingestion and parsing, chunking strategy, embeddings, hybrid retrieval, reranking and answer generation with citations. Tuned and measured on your documents, not on a demo dataset.
- vector databases
- embeddings
- LangGraph
- LlamaIndex
GraphRAG consulting
When answers depend on relationships, not passages, we build a knowledge graph over your data and let retrieval traverse it. GraphRAG handles the multi-hop questions standard RAG gets wrong: exposure across contracts, dependencies across systems, connections across cases. Few firms offer this commercially; it is one of our sharpest edges.
- knowledge graphs
- entity extraction
- graph traversal retrieval
Enterprise search and semantic search
Search engines that understand what users mean, not just what they type: semantic, phonetic and hybrid retrieval over millions of records. Long before RAG existed we built XLBase, our own high-performance search engine, and on it the legal case-law retrieval system acquired by Jurisprudencia Argentina — the ancestor of the RAG systems we build today. The same discipline built the phonetic trademark search behind DeMarcas.
- Elasticsearch
- OpenSearch
- hybrid retrieval
Verified answers, claim by claim
Citations are not enough. On top of retrieval we run a verification layer that checks every claim in the consolidated answer back against its source passages, flagging or dropping any statement the documents do not support. The system that summarizes is audited by a system that fact-checks it, so what reaches a lawyer or an analyst is an answer whose every assertion traces to a citable source.
- claim verification
- faithfulness scoring
- source attribution
LLM knowledge base development
The job to be done: chat with your company documents and trust the answer. We turn policy manuals, contracts and support history into an assistant your teams query in plain language, with every answer linked to its source.
- access controls
- citations
- evaluation harness
Proof, not promises
Search that had to work where exact match fails.
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Discovery
We map the process, the constraint and the money attached to it.
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Data Audit
We test whether your data can carry the model before promising results.
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Proof of Concept
A pilot built against a success metric agreed before we write code.
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Production
Deployed into your stack with your team, not handed off as slides.
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Monitoring
Models drift. We keep watching them after launch, not just at delivery.
Straight answers
The questions buyers actually ask, answered with numbers where they exist.
RAG vs fine-tuning: which one do we need?
RAG when the knowledge changes and answers must cite sources: policies, contracts, product data. Fine-tuning when you need to change model behavior, style or output format. Many production systems combine both, but if your answers must quote your documents, start with RAG.
What is GraphRAG and when is it worth it over standard RAG?
GraphRAG builds a knowledge graph of the entities and relationships in your data, and retrieval traverses that graph instead of just matching text. It pays off when questions span multiple documents or hops, such as which contracts are exposed to a given supplier. For single-document lookups, standard RAG is cheaper and sufficient.
How do you keep our proprietary data secure in a RAG system?
The system deploys inside your cloud account or on-premises, and your data is never used to train third-party models. Document-level permissions carry through to retrieval, so users only get answers drawn from documents they are already allowed to read.
How do you prevent hallucinations, and how accurate are the answers?
Every answer is grounded in retrieved passages and cited, and the system refuses when retrieval confidence is low. Before launch we measure accuracy against a golden set of questions your team writes, so the accuracy figure is yours, not a vendor benchmark.
How much does it cost to build an enterprise RAG system?
It depends on corpus size, document formats and security requirements. A pilot on a bounded corpus is a few weeks of work; enterprise deployments with SSO, access controls and a full evaluation harness take longer. Data preparation, cleaning, parsing and chunking is usually the largest cost driver, which the data audit sizes upfront.
Bring us the bottleneck.
One session with a senior engineer. We'll tell you whether AI pays for it, and what it takes to ship.