Knowledge-base assistants
Internal Q&A over policies, manuals, wikis and past tickets, so staff stop searching and asking around.
AI & Machine Learning
RAG systems: AI that answers from your own data
Answers grounded in your documents, with the sources to prove it.
Point an assistant at your manuals, policies and past tickets. It finds the right passage, answers in plain language and links to the source, so every reply can be checked.
An AI assistant that knows your business.
Retrieval-augmented generation (RAG) connects a language model to your own information. When someone asks a question, the system first searches your documents, wiki, tickets or database for the most relevant passages, then asks the model to answer using only those passages — and shows where each answer came from. The model stays general; the knowledge stays yours and up to date.
A RAG system is only as good as its retrieval. We spend most of the effort there: cleaning and splitting documents sensibly, choosing embeddings and hybrid search, re-ranking results, respecting who is allowed to see what, and measuring answer accuracy on real questions from your team before anyone relies on it.
In depth
What goes into a RAG system people actually trust
01Retrieval comes first
Most disappointing RAG assistants fail before the language model is even involved: the search step returns the wrong passage, so the model answers the wrong question. We treat retrieval as the core of the product.
That means splitting documents along their real structure — sections, tables, clauses — rather than at a fixed length, combining semantic and keyword search so product codes and names are found exactly, and re-ranking the results before the model sees them.
02Grounded answers, with sources
The assistant is instructed to answer only from the passages it retrieved, and every answer links to the document and section it used, so anyone can check it in one click.
When your documents do not contain the answer, it says so and suggests who to ask. An honest “I don’t know” is what makes people trust the answers it does give.
03Permissions built in
A knowledge assistant must not become a way around your access rules. Each document carries its permissions into the index, and search is filtered by the person asking — so an employee never receives an answer drawn from a file they could not open themselves.
For customer-facing assistants, only content approved for publication is indexed at all.
04Measured before launch
Before anyone relies on it, we build a test set from real questions your team asks, with the correct answers. Every version is scored on it: did search find the right passage, and did the answer stay faithful to it.
After launch, unanswered questions and thumbs-down feedback feed the next round of tuning, and the index updates automatically as documents change.
05Your data stays yours
We choose models and hosting to match your rules: hosted APIs with no training on your data, EU-region infrastructure for GDPR, or open models running entirely inside your own cloud when documents must not leave it.
06Where it pays off first
Support teams answering from manuals and past tickets; new employees finding policies without asking around; sales and operations staff searching contracts, specifications and procedures; and regulated work where every answer has to point to its source.
It also works in Georgian and Russian as well as English — tested on real questions in each language.
RAG Systems
What we offer
Customer-facing support bots
Answers from your help centre and product docs, with sources and a hand-off to a human when needed.
Document ingestion pipelines
Loading PDFs, Word, web pages, Confluence, Notion or databases, and keeping the index in sync as they change.
Vector & hybrid search
Embeddings, keyword search and re-ranking combined for accurate retrieval, on pgvector, Qdrant, Pinecone or Elasticsearch.
Access control
Answers that respect permissions, so a user only ever sees content they are allowed to read.
Accuracy evaluation
Test questions with known answers, retrieval and faithfulness scores, and dashboards to track quality over time.
When it’s the right fit
- Knowledge is spread across documents that people struggle to search
- Support agents answer the same questions from the same manuals every day
- You need an AI assistant that cites sources, not one that sounds confident
- Your information changes too often to retrain or fine-tune a model
How we deliver
- 01Source mappingWe list the documents and systems to include, who may access them, and collect real questions with correct answers.
- 02Retrieval prototypeIngestion, chunking and search tuned until the right passages come back for your test questions.
- 03Assistant & interfaceAnswer generation with citations, in a web app, Slack, Teams or your own product.
- 04Evaluate & operateAccuracy measured before launch, index updates automated, and feedback from users fed back into tuning.
Technology
Tools we use for RAG Systems
- LangChain
- LlamaIndex
- pgvector
- Qdrant
- Pinecone
- Elasticsearch
- OpenAI
- Python
Frequently asked questions
What is the difference between RAG and fine-tuning?
Fine-tuning changes how a model writes and behaves; RAG gives it facts to work from at question time. For answering questions about your own, changing documents, RAG is usually cheaper, easier to keep current and able to cite sources.
Which document formats can a RAG system use?
PDF, Word, Excel, HTML, Markdown, and content in tools such as Confluence, Notion, Google Drive, SharePoint or your own database. Scanned documents can be included with OCR.
How accurate is a RAG assistant?
We measure it on a set of real questions with known answers before launch and report the numbers. When the answer is not in your documents, the assistant is built to say so rather than guess.
How do you stop the assistant from making things up?
By controlling what it may answer from: only retrieved passages, a citation on every answer, and an explicit instruction to decline when the sources do not cover the question. We then test it with questions that have no answer in your documents, to confirm it declines instead of guessing.
How do you handle who can see what?
Permissions from the source systems travel with each document into the search index, and every search is filtered by the identity of the person asking. People only get answers from content they could already open.
How long does it take and what does it cost?
A focused assistant over one well-defined set of documents is typically a working prototype in 3–6 weeks. Cost depends on the number and state of the sources, the permission model and where it must run; our estimator gives a first range in a few minutes, and the scoping call that follows is free.
Can it run without sending our documents to OpenAI?
Yes. We can use hosted models under terms that exclude training on your data, keep everything in EU-region cloud infrastructure, or deploy open models entirely inside your own environment.
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