Dx1 · Assessment
We map the context, identify prioritized use cases, and come away with an honest recommendation.
2-4 weeksRAG · Internal assistant for documents
“ChatGPT for your company”: ask questions and get answers with exact citations from your documents.
What you get
The result the client can interact with, not the stack underneath.
Your company has 5 years’ worth of documents, manuals, policies, and procedures spread across SharePoint, Drive, Notion, and email. Whenever someone has a question—“What’s the expense policy?” or “What does the contract with client X say?”—they spend 20 minutes searching, or ask a colleague who then spends another 10 explaining. A well-implemented RAG assistant answers in 3 seconds, with an exact citation from the original document. For Mexican mid-market companies with 50-500 employees, the combined time savings amount to 5-15 hours per person per month.
What exactly it is
Technical definition—for Google, ChatGPT, and your CTO.
RAG (Retrieval-Augmented Generation) is an AI architecture that combines a language model with a search system over your private knowledge base. Unlike using ChatGPT directly, well-implemented RAG: (1) searches YOUR documents before answering, (2) cites the exact source for each answer, (3) doesn’t make up information that isn’t in the knowledge base, and (4) keeps your data private. It’s the standard solution for internal corporate assistants, interactive knowledge bases, semantic search across repositories, and onboarding new employees.
Includes
- ✓“ChatGPT” for internal documents (policies, manuals)
- ✓Onboarding assistant for new employees
- ✓Interactive knowledge base for processes
- ✓Semantic search in SharePoint, Drive, and Notion
- ✓Legal assistant for contracts and precedents
How ia¹ implements it01 02 03
Dx1 + More2 + Rt3 → sustainable advantage.
At ia¹, we implement RAG with an open, production-ready stack: automated ingestion from sources (SharePoint, Drive, Notion, Confluence, S3) with LlamaIndex or LangChain; embeddings with OpenAI text-embedding-3-large or Cohere multilingual; vector stores in Pinecone, Qdrant, or pgvector, depending on scale; reranking with Cohere Rerank or Voyage to improve relevance; and a final LLM using Claude Sonnet 4.6 or GPT-4o. A privacy audit is mandatory at the outset: where the data lives, who has access, and what is logged. Timeline: 4-8 weeks for a pilot with 1-3 sources, 10-16 weeks for a complete corporate assistant.
Im2 · Implementation
We build the first end-to-end use case. Open stack, your code, clear success metrics.
4-12 weeksRt3 · Retainer
Ongoing support: new cases, monitoring, adjustments, and capability transfer.
from month 3We start with the assessment
Activate my knowledge
30 minutes. No cost. You’ll leave with an honest recommendation — whether you work with us or not.
Frequently asked questions
What we’re asked most often.
What is RAG, and why is it the most widely adopted AI implementation among Mexican companies?
RAG (Retrieval-Augmented Generation) is a technique that combines a language model with a search system over your document repository: the model doesn’t make things up—it retrieves relevant excerpts from your PDFs/Docs/Wiki/SharePoint and answers based on them, citing sources. It’s the most widely adopted AI use case among Mexican mid-market companies because it solves a universal pain point: business knowledge is scattered, and teams lose hours searching for it. Typical implementations include “ChatGPT for our manuals,” “a legal assistant for current contracts,” and “internal support for policies and SOPs.”
How accurate is RAG, and can it make mistakes or invent answers?
Well-implemented RAG is 85-95% accurate when source documents are well organized. Unlike a raw LLM, RAG cites its sources—the user can verify the answer by going to the original document. To reduce hallucinations, we implement prompts that require the assistant to answer only from retrieved context, relevance checks (if there isn’t a sufficient match, the bot responds, “I don’t have that information”), and mandatory citations. Even so, in critical cases (legal, medical), RAG is an assistant, not an authority—a human must validate the answer before acting on it.
How long does it take and how much does it cost to implement a RAG assistant for my company’s documents?
Typical timeline: 4-8 weeks to production for a focused use case (one department, one document repository). Cost: $20-80K MXN upfront, depending on document volume (<1000 docs is quick; >10,000 requires careful architecture) and integrations (Drive, SharePoint, Box, Notion, Confluence). Monthly costs for tokens, vector store (Pinecone, Qdrant, pgvector), and observability: $8-25K MXN/month. Clear ROI when the team was spending more than 20 hours/week searching for scattered information.
Is my confidential information safe when I use it for RAG? Does the model train on my data?
With Claude Enterprise, OpenAI Enterprise, and Azure OpenAI Service, the answer is no: these platforms are contractually prohibited from training on your data. RAG is the safest approach to enterprise AI because your documents are never used to train the model—they’re only retrieved as relevant excerpts for each query and discarded afterward. At ia¹, we always implement with Enterprise plans, encryption at rest and in transit, and segregation by department where applicable.