Knowledge assistants
The retrieval engine behind an AI chatbot or internal helper, so it answers from your handbooks, policies and wikis instead of guessing.
Your data, made
answerable.
Retrieval-augmented generation is what makes an AI feature accurate instead of plausible. We build the pipeline that finds the right passage in your documents and hands it to the model, so answers are grounded in your reality and every one can point to its source.
Groundedanswers tied to
your own sources
your own sources
Currentcontent stays fresh
as yours changes
as yours changes
Measuredaccuracy scored,
not assumed
not assumed
Vector & hybrid search
Cited, source-linked answers
Permission-aware retrieval
Kept fresh automatically
Where retrieval belongs
The layer under
The layer under
every honest answer.
Retrieval is not one product. It is the foundation that a chatbot, an agent or a product feature stands on when it needs to be right about your data.
Document Q&A
Ask questions across contracts, reports, manuals or a research library and get a cited answer, not a folder full of files to read.
Grounding for agents
Give an AI agent reliable memory and context, so its decisions rest on your data rather than a stale training cutoff.
In-product search
Semantic search inside your own app that understands what a user means, not just the words they typed, over your catalog, tickets or content.
What’s included
The pipeline that
The pipeline that
decides answer quality.
Retrieval quality is the single biggest lever on how good an AI feature feels. These are the pieces we build and tune to move it.
01
Get your content in
Ingestion & parsing
We connect your sources, extract clean text from messy formats and set up a pipeline that keeps the index current as content changes.
PDF & docsWebsitesDatabasesSync jobs
02
Split it the right way
Chunking & embeddings
How content is split and embedded quietly decides whether retrieval works. We tune it for your material instead of using a blunt default.
Chunk strategyEmbeddingsMetadataVector store
03
Find the right passage
Search & re-ranking
Hybrid semantic and keyword search with a re-ranking step, so the model receives the passages that actually answer the question.
Vector searchHybridRe-rankingFilters
04
Answer with receipts
Grounding & citations
The model answers only from retrieved passages, links back to each source and declines when the answer is not there, no invented facts.
Source linksSnippetsAbstainConfidence
05
The right data for the right user
Permission-aware retrieval
Access rules applied at retrieval time, so a user only ever sees passages they are allowed to, the part most demos skip.
Row-level accessRolesTenancyAudit
06
Prove it works
Evaluation & monitoring
A question set with known answers, scored on retrieval accuracy and citation quality, plus monitoring so quality holds as content grows.
Eval setsAccuracy scoringGap detectionDrift alerts
How we work
From raw content
From raw content
to reliable retrieval.
We treat retrieval as an engineering problem with measurable quality, not a prompt you hope works. Here is the path.
Map the sources
We inventory what content matters, how it is structured and how it changes, then decide what to index and how often to refresh it.
Build the index
We parse, chunk and embed your content into a vector store with the metadata retrieval needs to filter by source, recency and permission.
Tune retrieval
We test against real questions, adjust chunking, search and re-ranking, and keep going until the right passages come back consistently.
Ground & secure
We wire in citations, the abstain behavior and access control at retrieval time, so answers are both honest and safe to show.
Evaluate
We score retrieval and answer quality on a fixed question set before launch and after every change, so improvements are proven, not felt.
Ship & maintain
We put it into your product or assistant, keep the index fresh and watch for drift as your content and questions evolve.
Direct answers
Questions before
Questions before
you build.
Retrieval is where most AI projects quietly succeed or fail, here are the questions worth asking first.
Ask us somethingA chatbot is the conversation. RAG is the retrieval underneath it that makes answers accurate. The same RAG pipeline can also power in-product search, document Q&A or an agent, the chat window is just one place it shows up.
As current as your content. We set up sync so new and changed documents flow into the index on a schedule or on update, so answers reflect the latest version rather than a one-time snapshot.
Yes. We apply access rules at retrieval time, so the system only ever pulls passages a given user is permitted to see. This matters the moment internal or customer data is in the index.
We build an evaluation set of real questions with known answers and score retrieval and citation quality on it. That turns “it feels better” into a number we can move and defend.
It depends on content volume, how many sources and the accuracy bar. After a short discovery we give a range tied to milestones, plus the ongoing embedding, vector store and model costs to run it.
Contact
Let’s make the next
Let’s make the next
move count.
Tell us what you are building. We will come back within one business day with questions, not a pitch deck.