How do you isolate tenants in a shared vector store?

Namespace or partition per tenant with the filter applied at query construction rather than in application code, plus tests that specifically attempt cross-tenant retrieval. This is the highest-severity failure mode in multi-tenant RAG, so it is verified rather than assumed.

What retrieval accuracy is realistic?

It depends entirely on your corpus, which is why we measure on your data before promising anything. The valuable output is a number you can track and improve, not a benchmark from someone else's documents.

Why not just fine-tune a model on our documents?

Fine-tuning teaches style and format well but is a poor way to store facts — it cannot cite sources, and updating a single document means retraining. RAG updates the moment you re-index and can always show its working. Most projects that think they need fine-tuning need better retrieval.

How accurate can we expect it to be?

That depends on your corpus, and we will measure it rather than promise a number. The evaluation harness gives you a retrieval accuracy figure on your own documents before anything reaches users — if it is not good enough, we tune until it is.

RAG Systems · SaaS

RAG Systems for SaaS

Ship retrieval that is actually accurate, not a demo that impresses once.

Most SaaS teams can build a working RAG prototype in a week and then spend six months discovering it is right seventy percent of the time. We build the evaluation harness first, so retrieval accuracy is a tracked number, and tune chunking, hybrid search, and reranking against it.

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Every claim: GroundedEvery claim
Search strategy: HybridSearch strategy
Retrieval accuracy: MeasuredRetrieval accuracy

Typical stack

LlamaIndex
LangChain
Pinecone
pgvector
Cohere Rerank

Use cases

What this handles for SaaS companies

Add retrieval over customer-uploaded documents with strict tenant isolation
Build in-product search that answers questions rather than returning a link list
Ground AI features in your documentation so they never invent product behaviour
Instrument retrieval quality per release so regressions fail CI instead of shipping

A retrieval layer with a measured accuracy figure you can defend to customers.

Context

The saas problem underneath it

SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.

The AI feature on the roadmap keeps slipping behind committed work
A prototype exists but nobody is confident enough to put it in front of customers
Inference cost per account is unmodelled, so pricing is guesswork
Support volume grows with the customer base and headcount follows

What we build

Inside a RAG system

Most failing RAG deployments fail at retrieval, not generation. We build the evaluation harness first: a set of real questions with known-correct source passages, so retrieval accuracy is a number we can improve rather than a feeling. Chunking strategy, hybrid search, and reranking then get tuned against that number.

Document parsing that preserves tables, headings, and layout structure
Chunking strategies tuned per document type rather than a fixed token count
Hybrid dense and sparse retrieval with a cross-encoder reranking pass
Citation grounding that ties every claim to a specific retrieved passage
Retrieval evaluation harness with recall and precision tracked per release
Incremental re-indexing so updated documents are searchable immediately

Deliverables

What ships with the engagement

Indexed corpus with a documented chunking and embedding strategy
Golden evaluation set of questions with verified source passages
Retrieval quality report benchmarked before and after tuning
Query API or chat surface, depending on how you will consume it
Runbook for adding new document types to the pipeline

FAQ