Can this work with data we can't send to a public AI API?

Yes — we can architect around your data governance requirements, including private hosting and access-scoped retrieval.

How do you keep the knowledge base up to date?

We build sync pipelines from your source systems (docs, CMS, internal wikis) so the retrieval index updates automatically as content changes.

Services/AI & Agentic Services/RAG Implementations

🔎 AI & Agentic Services

RAG Implementations

Give your AI a memory of your business.

Retrieval-Augmented Generation (RAG) connects an LLM to your actual business knowledge — documents, policies, product data, support history — so answers are grounded in your real information instead of a model's general training data. We build and maintain the retrieval pipeline, not just a demo: chunking strategy, embeddings, vector search, and citation of sources.

Discuss a RAG Use Case →

Sound familiar?

!Institutional knowledge is scattered across documents no one can search quickly

!Off-the-shelf chatbots hallucinate answers that don't match your actual policies or data

!Support and internal teams spend time answering the same repeat questions

!Sensitive data can't be sent to a general-purpose AI tool without proper controls

What's Included

✓

Knowledge Base Ingestion Pipeline

A pipeline that ingests and keeps your documents, data, and content current.

✓

Chunking & Embedding Strategy

Retrieval architecture tuned to your content type for accurate, relevant results.

✓

Vector Search Infrastructure

Production-grade vector search set up for your scale and latency needs.

✓

Source Citation & Grounding

Answers that cite their source, so accuracy can be verified, not just trusted.

✓

Access Controls & Data Security

Retrieval scoped to the right users and permissions, with sensitive data handled appropriately.

✓

Internal or Customer-Facing Interface

A search or chat interface for your team or customers, built on the retrieval pipeline.

Our Process

Knowledge Audit

We assess your documents and data sources — format, volume, sensitivity, and update frequency.

Pipeline Design

We design the ingestion, chunking, and embedding strategy specific to your content.

Build & Index

We build the retrieval pipeline and index your knowledge base.

Evaluate Accuracy

We test retrieval and generated answers against real questions before launch.

Deploy & Maintain

We deploy the interface and keep the knowledge base synced as content changes.

Tools & Technology We Use

Vector databases (Pinecone, Weaviate, pgvector)OpenAI, Anthropic & open-weight embedding modelsLlamaIndex / LangChainDocument processing (PDF, Notion, Confluence, SharePoint)Python / TypeScript

🔎

Accurate answers, grounded in your data

AI responses that cite real sources instead of guessing

Frequently Asked Questions

Fine-tuning bakes knowledge into the model itself and goes stale quickly. RAG retrieves current information at answer time, so it stays accurate as your documents change, and it can cite its sources.

Related Services

Ready to talk about rag implementations?

Book a free consultation and we'll show you exactly how this applies to your business.