6–10 weeks
Data & Knowledge Systems
Build the retrieval and knowledge infrastructure your AI needs to be accurate, not just fast.
What You Get
Outcomes
Tangible results you can expect from this engagement.
Deliverables
What's Included
Concrete outputs you receive at the end of the engagement.
- 1 Data architecture assessment and knowledge mapping
- 2 RAG pipeline with retrieval evaluation and tuning
- 3 Document ingestion and processing infrastructure
- 4 Vector database setup and embedding optimization
- 5 Data governance and access control implementation
Who It's For
Recommended For
Measurement
Success Metrics
How we track and prove the impact of this engagement.
Why Knowledge Infrastructure Matters
Most AI projects that fail don’t fail because of the model. They fail because the model doesn’t have access to the right information at the right time, in the right format. You can have the best language model available, but if it’s answering questions from incomplete or outdated context, it will confidently give wrong answers.
This is the knowledge infrastructure problem, and it’s the foundation that every other AI capability depends on. Customer support AI needs accurate product documentation. Internal assistants need current policy information. Analytics tools need clean, connected data. Without a solid retrieval layer, you’re building on sand.
We build the data and knowledge systems that make your AI accurate and trustworthy—not just responsive.
How We Build RAG Systems That Work
Retrieval-Augmented Generation sounds simple in concept: find relevant documents, feed them to the model, get a grounded answer. In practice, every step hides complexity that determines whether your system is useful or frustrating.
Ingestion and processing. Documents come in different formats, structures, and quality levels. A 200-page regulatory filing, a two-paragraph Slack policy update, and a spreadsheet of product specifications all need different handling. We build processing pipelines that extract text, preserve structure, handle tables and images, and normalize content for consistent retrieval.
Chunking strategy. How you split documents into retrievable pieces has an outsized impact on answer quality. Chunks too small lose context. Chunks too large dilute relevance. We test multiple strategies—fixed-size, semantic, document-structure-based—and evaluate against your actual query patterns to find what works for your content.
Embedding and indexing. We select and configure embedding models based on your content type and query patterns, set up vector databases for fast similarity search, and build hybrid retrieval that combines semantic search with keyword matching for better recall.
Retrieval evaluation. Before any user touches the system, we build a test suite of representative queries with known good answers. We measure retrieval relevance, answer accuracy, and source attribution quality. This evaluation suite becomes an ongoing quality gate for any changes to the pipeline.
Data Governance Is Not Optional
Every knowledge system we build includes access controls, audit logging, and data lineage tracking. When an AI answers a question, you need to know: what documents did it use? Was the user authorized to see those documents? When were those documents last updated?
This isn’t just about compliance—though it matters for regulated industries. It’s about trust. If your team doesn’t trust the AI’s answers, they won’t use it. Source attribution and access controls are how you build that trust.
What This Enables
A well-built knowledge system is a platform, not a project. Once you have reliable retrieval infrastructure, you can build customer-facing search, internal assistants, compliance monitoring, automated document review, and dozens of other capabilities on top of it. The investment in getting the foundation right pays dividends across every AI initiative that follows.
Risk Management
Risks & Mitigations
We plan for what can go wrong so you don't have to.
Poor retrieval quality leads to inaccurate or hallucinated answers
We build retrieval evaluation into the pipeline from day one—testing against known question-answer pairs and measuring relevance scores before any user-facing deployment.
Sensitive documents exposed through search to unauthorized users
We implement document-level access controls that mirror your existing permissions. The AI can only retrieve documents a user is already authorized to see.
Data pipeline can't keep up with document volume or update frequency
We design for your actual throughput requirements with incremental indexing, parallel processing, and backpressure handling. We load-test before launch.
Architecture
System Architecture
FAQ
Frequently Asked Questions
What document types can you handle?
PDFs, Word documents, PowerPoint, HTML, Markdown, plain text, and most structured data formats. We can also process scanned documents with OCR, though accuracy depends on scan quality. If you have specialized formats, we'll assess them during discovery.
How do you handle documents that change frequently?
We build incremental indexing pipelines that detect changes and re-process only affected documents. For high-frequency updates, we can set up near-real-time sync. The goal is that your knowledge base stays current without manual intervention.
What's the difference between RAG and fine-tuning?
RAG retrieves relevant documents at query time and uses them as context for the model's response. Fine-tuning changes the model's weights based on your data. RAG is better for factual, document-grounded answers where you need source attribution. Fine-tuning is better for adapting tone, format, or specialized reasoning. We usually recommend starting with RAG.
Can this work with data that has compliance restrictions?
Yes. We design systems that keep data within your security boundary—on-premises, in your VPC, or in compliant cloud regions. We support encryption at rest and in transit, role-based access, and audit logging for every query and retrieval.
Ready to get started?
Let's scope a data & knowledge systems engagement for your team. 30-minute call, no pitch deck.
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