RAG Consulting & AI Knowledge Platforms

Build the Foundation for Smarter, Context-Aware AI

Your AI is only as good as the data it can retrieve.

Shipshape Data designs and implements Vector and RAG databases that give large language models instant access to the right context, improving accuracy, grounding, and trust in every response.

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From Static Data to Dynamic Context

We design retrieval-augmented generation (RAG) systems that let your models think with your data, combining embeddings, metadata, and search precision.

Whether powering a chatbot, document assistant, or analytics engine, we ensure your AI pulls answers from verified sources every time.

This isn’t just about storing vectors, it’s about creating context: smarter outputs, faster performance, and trustworthy automation.

Contextual efficiency

Ensure AI responses are based on your verified data, not generic model memory.

Reliable retrieval

Vector databases designed for millisecond query times, even at scale.

Smarter automation

Enable search, chat, and summarisation to access grounded knowledge.

Data integrity

Every record versioned, validated, and embedded with traceable lineage.

Outcomes That Drive Real Business Impact

We design RAG databases that deliver precision, speed, and reliability, powering AI that speaks from data, not assumptions.

Faster Decisions

Query and retrieve verified knowledge instantly from complex datasets.

Lower
Costs

Reduce cloud spend by optimising vector storage and inference.

Improved Accuracy

Ground AI responses in factual, organisation-specific data every time.

Is your business ready for AI?

Our free AI Readiness Assessment helps you uncover how prepared your organisation really is, so you can identify gaps, strengthen your foundation, and confidently move toward AI-driven growth.

Free AI-readiness assessment

“Working with the team at Shipshape has been really positive. The team were very flexible in addressing our requests. When we encountered problems, they rolled up their sleeves and found solutions for us”

Edt Goris

Partner & Country GM, Slimstock

Slimstock Elevates Website Engagement With Custom AI Chat Interface

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Our Approach: From Data to Deployment

Every RAG system we deliver follows a rigorous process, from understanding your data landscape to designing, testing, and scaling production-ready retrieval infrastructure

Step 1

Define the Data Scope
We identify the datasets, documents, and sources your AI needs to access in real time.

Step 2

Embed and Index
We transform text, tables, and metadata into dense vector representations optimised for retrieval.

Step 3

Architect the Vector Store
We select and configure the right database for your performance and compliance needs.

Step 4

Integrate with Your Model
We connect your database to your LLM or API pipeline, creating seamless retrieval-augmented generation workflows.

Step 5

Govern and Optimise
We implement observability, drift detection, and feedback loops to ensure your RAG system remains accurate, fast, and secure.

Built on the Platforms You Trust

We design and deploy RAG and vector database architectures using industry-leading infrastructure and frameworks, ensuring every system scales with your business and data.