AI Applications & Knowledge

RAG Development

A language model is only as good as what you put in front of it. Most disappointing AI answers are retrieval failures wearing a model's clothing: the right passage existed and was never fetched. We build and tune the retrieval layer, then measure it, so the model is arguing from the correct evidence.

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The business problem

The demo worked because the corpus was small

Naive vector search performs well on a few hundred documents and degrades badly past a few hundred thousand. Chunking splits a table from its heading. Semantically similar passages crowd out the one that is actually authoritative. Recency loses to relevance, so a superseded policy outranks the current one. None of this is visible without measurement, so teams ship, get plausible answers, and only discover the failure rate when somebody acts on a wrong one.

What we do

Build the retrieval, then prove it

We start by building an evaluation set from real questions and known-correct sources, because you cannot tune what you cannot score. Then we work the pipeline: chunking that respects document structure, hybrid search combining keyword and vector, metadata filters for recency and authority, and reranking on the shortlist. Where relationships between entities matter more than passage similarity, we use a graph rather than pretending vectors are enough. Every change is scored against the same set, so improvement is demonstrated rather than asserted.

RAG Development

Capabilities

Enterprise RAG
Advanced RAG
GraphRAG
Hybrid Search
Vector Search
Retrieval Optimization
RAG Evaluation

Common use cases

Ground a customer-facing assistant so every claim it makes traces to a published document.
Retrieve across contracts where the answer depends on which agreement governs, not on wording similarity.
Search technical documentation where the correct passage is a table or a diagram caption.
Rebuild an existing assistant that answers plausibly but is wrong often enough to have lost trust.

How we deliver

Shape

Turn the request into a specification: who uses it, what a correct answer is, who decides.
Ground

Connect to the content and systems that hold the answers, respecting existing permissions.
Evaluate

Score against a labelled set built from your own cases, before anyone outside sees it.
Ship & operate

Staged release with monitoring, cost control and a regression suite that guards quality.

Technology

OpenAI
Anthropic
Azure OpenAI
pgvector
Elasticsearch
Microsoft 365
SharePoint
Confluence
Salesforce

Security & governance

Answers are grounded in your own content and carry citations, so a reader can check them. Retrieval respects the permissions already set on the source, which means a user never sees through the application what they could not see directly. Prompts, retrieved context and responses are logged for audit, and evaluation runs continuously rather than once at launch.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We measure before we tune

Most retrieval work is done by intuition: change the chunk size, read a few answers, decide it feels better. We build the labelled set first, which is unglamorous and is the entire difference between tuning and guessing. It also leaves you with a regression suite, so a model upgrade six months from now cannot quietly degrade quality without anyone noticing.

Selected clients

Frequently asked questions

How do you measure whether retrieval is good?

Against a labelled set of real questions paired with the passages that genuinely answer them. We report whether the correct passage was retrieved at all, and where it ranked. Answer quality is scored separately, because a good answer from the wrong source is still a failure.

Do we need a vector database?

Often not. pgvector inside an existing PostgreSQL instance handles a great many enterprise workloads without adding infrastructure to operate. We recommend a dedicated vector store when scale or feature requirements justify it, not by default.

What is GraphRAG and do we need it?

It retrieves over a graph of entities and relationships rather than over isolated passages. It earns its extra complexity when answers depend on connections across documents, for example ownership chains or dependency structures. For most question-answering over prose, hybrid search plus reranking performs better for less effort.

Can you improve a system we already built?

Yes, and it is common. We start by building the evaluation set and measuring what you have. That usually locates the failure precisely, and the fix is frequently narrower than a rebuild.

How does this stay accurate as content changes?

Indexing follows the source on a schedule or on change notification, and the evaluation set runs continuously rather than once. Retrieval quality is monitored the way any other production metric is.

Related capabilities

AI Applications & Knowledge

Enterprise Knowledge AI

Turn fragmented internal knowledge into secure assistance that respects existing permissions.

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AI Data & Models

AI Data Engineering

The pipelines, quality controls and vector stores that AI systems depend on to be correct.

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AI Data & Models

Model Engineering

Fine-tuning, embeddings, optimization and serving, including open-source and self-hosted models.

Learn more

Discuss Your AI Initiative

Retrieval systems that ground answers in your own content, with citations and measurable accuracy.

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