RAG & intelligent search: answers from YOUR documents, with sources
We build assistants and search engines that answer questions about your own documents and data —contracts, manuals, policies, tickets, your knowledge base— citing the exact source of every answer and minimizing hallucinations, instead of a generic chatbot that makes up what it doesn't know.
RAG (Retrieval-Augmented Generation) is the architecture that connects a language model (GPT-4o, Claude) to your own content.
Instead of asking the model to "remember" —and risk it inventing— we first retrieve the fragments of YOUR documents that actually answer the question and pass them to the model as context, forcing it to answer only from that evidence and to cite where it came from. The process: we ingest your documents (PDF, Word, Excel, Confluence, SharePoint, emails, tickets), split them into chunks, generate embeddings (numeric representations of meaning) and store them in a vector database such as pgvector or Pinecone. When someone asks, we search by meaning —not exact words— for the most relevant passages, and the model writes the answer with citations to the source. The result: verifiable answers about your information, with semantic search included.
Why iTechDev
Fixed budget
Scope and price defined before we start. No hourly billing, no ambiguous scope.
Code 100% yours
All code and configuration are your property from the first commit. No vendor lock-in.
Progress every 2 weeks
Live functional demos each sprint. You see real progress, not a months-long black box.
Engineering with process
CMMI Level 2, 5.0★ on Clutch and 200+ projects. Nearshore team in Monterrey + Texas, in your time zone (CST).
When you need it
What's included
Ingestion & indexing of your sources
We connect and process your sources: PDF, Word, Excel, emails, tickets, SharePoint, Confluence or your database. We apply OCR to scanned or image-based documents so their text becomes searchable, normalize the content and split it into chunks with their metadata (source, date, section, permissions).
Embeddings & vector database
We generate embeddings for each chunk and store them in pgvector (on your PostgreSQL) or Pinecone, depending on your infrastructure. This enables search by meaning rather than exact words, and is the foundation of all retrieval.
RAG pipeline with hallucination control
We orchestrate retrieval + generation: hybrid retrieval (semantic + keyword), result re-ranking, and prompts that force the model to answer only from the retrieved evidence and to say "I don't know" when the context falls short, instead of inventing.
Citations & sources on every answer
Every answer links to the exact document, page or passage it came from, so anyone can verify it. Without citations there's no trust: this is the heart of the approach and the difference from a generic chatbot.
Search & chat UI
A ready interface for your team: semantic search and/or a conversational assistant with history, source previews, filters by document type and respect for the asker's permissions. We integrate it into your intranet, portal or app.
Accuracy evaluation
We define a set of real questions with their correct answers and measure how well the system responds (coverage, citation accuracy, hallucination rate). We iterate with data, not hunches, and re-measure before every change.
How we work
Source & question discovery
We map what documents and data exist, in what format and with what permissions, and gather the real questions your team needs to resolve. That defines the scope and the initial evaluation set.
Measurable proof of concept
We build a RAG over a representative subset of your documents and evaluate it with real questions. Before committing the full scope, you see answers with their citations and an honest accuracy measurement.
Pipeline & ingestion build
We implement ingestion, OCR, embeddings, the vector database and the full RAG pipeline with CI/CD, automated tests and code reviews. We connect your real sources and the search/chat UI.
Tuning & quality control
We tune chunking, retrieval, re-ranking and prompts against the evaluation set to raise accuracy and lower hallucinations. We validate quality with our internal ARIA platform before go-live.
Deployment & updates
We launch in your cloud or on-premise, with automatic re-indexing when your documents change, usage and answer monitoring, a runbook and documentation. The code is 100% yours from the first commit.
Tech stack
The tools and platforms we build it with — chosen for your problem, not for hype.
Frequently asked questions
Are my documents exposed or used to train models?
No. Your documents are yours and are not used to train public models. We can deploy everything in your cloud (Azure, AWS, GCP) or fully on-premise, and use enterprise APIs that don't retain or train on your data, or self-hosted open models if you need nothing to leave your network. The vector database (pgvector or Pinecone) lives wherever you decide, and we honor each user's permissions so everyone only queries what they're allowed to.
How do you keep the assistant from making up answers (hallucinations)?
With RAG the answer is built only from the fragments retrieved from your documents, not from the model's "memory." We add prompts that force it to cite the source and to reply "I didn't find this in the documents" when evidence falls short, hybrid retrieval and re-ranking to bring back the right context, and an evaluation set with which we measure the hallucination rate. We don't promise zero hallucinations —no honest team does— but we reduce them measurably and make them detectable because every answer comes with its verifiable citation.
Which document formats do you support?
PDF (including scanned ones, via OCR), Word, Excel, PowerPoint, plain text, HTML and Confluence or SharePoint pages, plus emails, tickets and records from your database. For images and scanned documents we apply OCR to extract their text. Important, for honesty: this is text understanding, not "computer vision" — we don't interpret the visual content of photos or blueprints; we extract and understand the text they contain.
Does it update itself when we add or change documents?
Yes. We set up automated ingestion: when you upload, edit or delete a document in the connected source, the system reprocesses it, regenerates its embeddings and updates the index, on a schedule or triggered by the change. Answers reflect the current version and, thanks to citations, you can always confirm which document and date they came from.
Do we own the code and everything built?
Yes, 100%. The pipeline code, the prompts, the vector database configuration, the UI and the documentation are yours from the first commit, with no vendor lock-in. We work with a CMMI Level 2 certified process and over 200 delivered projects, with development in Monterrey and Texas. Our own internal ARIA platform and the assessments you'll see on this site are direct proof that this capability already runs in production.
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