AI-Ready Data Preparation
We get your data ready so the AI actually works
Most AI projects don't stall on the model: they stall on the data. We curate, structure and index your information so your assistants and agents answer accurately.
From scattered documents to reliable context
The problem
- Your knowledge lives in PDFs, wikis, tickets and spreadsheets that no model can read well.
- You tried an assistant with RAG and it answers vaguely or makes things up: the problem is the source.
- There's no way to know which document backed each answer.
- Every new version of a document leaves the assistant out of date.
What's included
- Ingestion of unstructured sources: documents, wikis, tickets, email and knowledge bases.
- Cleaning, deduplication and normalization of the corpus, with explicit curation criteria.
- Chunking, embeddings and vector indexing with Amazon Bedrock Knowledge Bases and Amazon OpenSearch.
- Metadata and permission filters so each user only retrieves what they're allowed to see.
- Incremental refresh pipelines: when the source changes, the index updates.
- Retrieval evaluation: we measure answer accuracy against a set of real questions.
80% of an assistant's quality comes from data preparation
Picking the model is the easy part. What separates a demo from an assistant people actually use in production is corpus quality, chunking, permission metadata and continuous evaluation. That's where we focus.
How we work
Discovery
We identify which knowledge sources the use case needs and what shape they're in.
Plan & Quote
We define the corpus, the indexing strategy and the evaluation set, with closed scope and price.
Execution
We build the ingestion and indexing pipelines and measure retrieval accuracy until we hit the agreed threshold.
Hand-off & MSP
We leave refresh automated and monitored. We can keep operating and evolving the corpus.
Stack & technologies
Data ready for RAG, agents and evaluation
FAQ
Isn't this just doing RAG?
It's the half almost nobody does well. RAG is the architecture; data preparation is what determines whether that architecture answers well or hallucinates.
Do I have to move all my documents to AWS?
The documents that feed the index do live in your AWS account, under your control and your access policies. We don't train third-party models with your information.
How do you measure that it worked?
We build a set of real business questions with expected answers and measure retrieval accuracy before and after each iteration.
Does it apply to agents, not just chat?
Yes. An agent that queries your data needs exactly the same curated, indexed base, plus a well-modeled permission layer.
