Your LLM does not know what your business knows.
We build retrieval systems that fix that. Real data, real retrieval, honest outputs.
Live retrieval demo
Each query hits every document at once. The closest ones surface.
The problem
It will try to answer anyway.
A language model trained on the internet has no idea what is in your internal wiki, your contracts, your support history, or your product documentation. It will try to answer anyway. That is the problem.
RAG gives a model access to your specific data at query time. Instead of generating an answer from training memory, the model retrieves the relevant documents first, then responds based on what it actually found.
But retrieval is a full engineering problem, not a wrapper call. Most teams underestimate it. They stand up a basic vector search, it works on the first 10 queries, they ship it. Then they spend the next six months explaining to users why it returned the wrong document.
Here is what that looks like in practice. A user asks your support bot how to cancel their plan, and the bot hands back the onboarding guide, because "cancel your plan" and "set up your plan" sit close together in embedding space, both about accounts and billing. The model reads the setup steps, sounds completely sure of itself, and walks the customer through the opposite of what they asked. Nobody wrote that answer. The retrieval did.
What nobody warns you about
You can retrieve the right document and still get a wrong answer.
This is the part that surprises people. Retrieval failing is rarely retrieval returning nothing. Most of the time it returns something plausible, and a plausible wrong answer is far more dangerous than an empty one, because it reads as confident and nobody thinks to check it.
Embeddings match on topic, not intent. "How do I cancel" and "how do I sign up" land near each other because both are about accounts, so a naive vector search serves up the wrong half of your help center and the model has no way to know it got the wrong half.
Chunking can sever meaning. Cut a document on a fixed character count and sooner or later you split a number away from the row it belongs to, or a rule away from the exception printed right under it. The fragment still retrieves cleanly. It just no longer says what the page said.
And then there is the genuinely strange one. Researchers named it "lost in the middle": even when the correct passage is sitting right there in the context window, a model leans hard on what is at the very start and the very end and skims whatever is buried between them. Stuff in twenty documents to play it safe and the answer can get worse, not better, because the real evidence lands in the dead zone. More context is not the same as better retrieval, and treating the two as one thing is how clean data still produces a wrong answer.
How we build it
No generic template.
We design the pipeline around your data structure and your quality targets. These are the patterns we work with.
Chunk design that respects structure
Splitting on 500-character windows without regard for document structure is how you get retrieval that finds half an answer. We chunk based on your content type: headings, sections, or semantic boundaries, so a chunk still means what the page meant rather than ending mid-thought.
Hybrid search
Pure vector search misses exact matches: product codes, error strings, names. Pure keyword search misses meaning. We run both, the semantic side and an old-fashioned keyword index, then fuse the two result lists by rank instead of by raw score, because the scores are not on the same scale and naively blending them quietly breaks the ranking. The fusion step is boring, and it is also what makes hybrid search actually work.
Re-ranking
The first pass is built for recall: cast a wide net, pull fifty candidates, accept that most are noise. Then a second model, a cross-encoder that reads the query and each candidate together instead of comparing pre-computed vectors, scores them properly and keeps the three to five that genuinely answer the question. The first pass is fast and blunt, the second is slow and sharp. Most teams ship only the first and then wonder why their top result is almost, but not quite, right.
Order the context, do not just fill it
Once we have the few passages that matter, where they sit in the prompt is not an afterthought. A model reads the start and end of its context far more reliably than the middle, so the strongest evidence goes at the edges and we send a tight handful rather than everything we found. Retrieval quality is not only what you fetch. It is also what you decide not to send.
Agentic RAG
When a query is complex enough that one retrieval step does not cover it, the system retrieves iteratively. The model decides what to look for next based on what it already found, instead of trying to force a whole answer out of a single lookup.
Evaluation from day one
We build retrieval quality evaluation into the pipeline before launch, not after. We measure whether the right passages are being found and whether the answer actually rests on them, so when precision drifts you see it on a dashboard before a user feels it.
"Retrieval is not a feature. It is an engineering discipline. Build it wrong and no amount of prompt engineering will save you."
What you get
A pipeline you can maintain.
Not a prototype we hand off with a disclaimer. We document everything so your team can extend it without calling us back.
- Chunking and ingestion pipeline for your data sources
- Embedding plus vector store setup: Pinecone, pgvector, or Qdrant depending on your stack
- Hybrid search layer with tuned weights
- Re-ranking integration for precision
- Evaluation suite with recall and faithfulness metrics
- Documentation so your team can maintain and extend it
Not sure which retrieval pattern fits your data? Send us the requirement and we will tell you in the first conversation.
Invoke usIs this the right call
When this fits.
Good fit
- You have internal data that users need to query conversationally
- You have a knowledge base that keyword search is not getting through
- You tried a basic vector search and the quality is not there
- You are building a support bot, internal assistant, or document QA system
Wrong call
- You want general conversational capability with no company-specific data. Use a base model or fine-tune instead
- You need real-time data that changes by the second. RAG retrieves at query time but indexes on a lag
- Your data is so structured that a direct database query would serve the user better
Deployment and scale
On-prem, cloud, or hybrid.
We do not have a vendor preference and we do not push you toward one. The right environment is the one that fits your budget, your compliance rules, and where your data already lives.
Everything we build is containerized. Same stack, same behavior, whether it runs on AWS, your own servers, or a bare-metal box in your office. You are not locked into anything.
If your query volume is small today, the architecture does not punish you for that. When it grows, it scales without a rewrite. We design for that from the start, not as an afterthought.
What we settle before we begin: where your data lives, whether it can leave that environment, and what your latency target actually is. Everything else follows from those three.
Let's talk about your data.
Tell us what you are trying to make retrievable, what your users are asking for, and where current search is failing. We can tell you whether RAG is the right call, and what the pipeline should look like.