Clean, structured data
PDFs, wiki pages and manuals arrive in very different shapes. Extraction and cleaning are built for each format, because inconsistent documents weaken retrieval.
RAG System Development · Answers with sources
RAG system development for answers from your own documents
theBlue.ai builds RAG systems for companies: retrieval-augmented generation that connects a language model to your own documents and data, so your team asks in plain language and gets an answer that names the document and page it came from. The knowledge stays current without retraining the model, and the system runs on-premise, in the cloud or hybrid.
Proof
Retrieval that pays off, in three numbers
theBlue.ai has delivered more than 100 AI and automation projects since 2016. Retrieval over a company’s own documents sits at the core of many of them, from product documentation to security questionnaires.
75%
Less time to complete a security questionnaire at apoQlar
$90K
Estimated saving a year across roughly 15 questionnaires
100+
AI and automation projects delivered
Some of the organisations we have built for
Where RAG helps
The answer exists, but nobody finds it
Contracts, policies, technical documentation, past projects: the answer is somewhere in your own files. Each card shows a system that finds it and names where it came from.
Product and technical documentation
Support and operations teams ask in plain language and get a precise answer from hundreds of product documents in seconds, with the document and section it came from.
Seconds, with sourcesapoQlar
Security and compliance questionnaires
Every answer is drafted from the company’s own policies and technical documentation, with document name and page, so one person verifies instead of eight to ten people searching.
−75% completion timeapoQlar
Citizen questions from official documents
Citizens ask in everyday language. A retrieval layer makes sure the answers come from official, approved documents across eleven knowledge areas, which regulated environments require.
11 knowledge areasPublic authority, under NDA
An internal knowledge assistant
Our own team asks questions about projects, proposals and internal documentation and gets answers with a pointer to the source. The system runs on our own GPUs, so the knowledge never leaves the company.
Local model, own GPUstheBlue.ai, in-house
How it works
One question, answered from your documents
Question
Where is customer data stored, and who can access it?
Passages found in your documents
Information security policy · p. 12
✓Data protection concept · p. 4
✓Wiki: Hosting architecture
✓Onboarding checklist · p. 2
Answer
Customer data is stored in the company’s own cloud environment in the EU.13 Access is limited to named administrators and every access is logged.2
- [1] Information security policy, p. 12
- [2] Data protection concept, p. 4
- [3] Wiki: Hosting architecture
Ready to verify
−75%Completion time, from about a month to under a week
6 → 2 weeksClient onboarding
$90KEstimated saving a year
Illustrative example. Figures from the apoQlar security questionnaire case study.
Built for production
What decides whether the answers can be trusted
Retrieval quality decides everything: if the system retrieves the wrong passages, even the best model gives a confident wrong answer. These six parts are where a convincing demo becomes a system you can rely on.
Chunks that keep their meaning
Documents are split into logical sections and embedded, so a question retrieves the passage that answers it with minimal delay.
Semantic and keyword search
Semantic search finds meaning even when the wording differs. Where precision matters, a hybrid with keyword search covers exact terms and codes.
A source on every answer
Each answer names the document and page it drew from, so your team can check it and use it directly in customer-facing work.
Context across a conversation
Follow-up questions keep their context while only the most relevant passages stay in the prompt, so accuracy and speed hold over several turns.
Beyond documents
RAG can also query databases or knowledge graphs, with the model writing the structured query, for questions whose answer sits in tables.
Control
Your knowledge stays where it belongs
Inside your environment
The index and the model run on-premise, in your cloud tenant or hybrid. For apoQlar all processing stays inside their own cloud environment.
Always current
Knowledge is supplied at the moment of the question. Update a document and the next answer reflects it, with no retraining.
A person verifies
Sources on every answer turn review into a quick check. At apoQlar a small team verifies what used to take eight to ten people.
Maintained by your experts
Subject experts update and extend the knowledge base themselves in editable lists, without waiting for a developer.
Testimonial
What the client says when the work is done
MedTech · Compliance
Security questionnaires answered by retrieval over your own documents
–75%Completion time, a month to under a week
$90KEstimated annual saving
Managing the completion of security questionnaires is no longer a logistical nightmare. The new system is easy to manage and ensures our responses are accurate and comprehensive.
Case studies
RAG systems on real client documents
Documentation search and questionnaire automation in MedTech, and answers from official documents in the public sector.
–75%
Completion time
$90K
Saved a year
MedTech · apoQlar
Security questionnaires answered in one week instead of one month
Every new hospital client required a completed security questionnaire: eight to ten people, about a month, answers pulled from policies across departments. A GenAI assistant now drafts the responses and a small team verifies them.
Read the case study
RAG
Over their own files
Seconds
With source references
MedTech · apoQlar
A retrieval assistant that answers from hundreds of product documents
Support staff searched through hundreds of product documents to answer customer and compliance questions. They now ask in plain language and get an answer with the source it came from.
Read the case study
11 areas
One domain agent each
Auto
Routing, no category picking
Government · Polish institution, under NDA
A multi-agent chatbot across eleven knowledge areas
The old chatbot asked citizens to pick a category before asking, and most did not know which one fitted their question. Specialised agents with retrieval now route each question themselves and match plain language to the technical documentation.
Read the case studyHow to start
Start with one knowledge base and one question
An MVP-first start works best: one clear business problem, a working prototype tested with real data and real users, and an architecture that can grow into production.
Describe your knowledge problem
Tell us which questions cost your team the most time to answer, and where the answers live. We come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
Describe your knowledge problemStart with the process analysis
Document sources mapped, data quality checked, and an architecture proposed with scope, timeline and cost. A standalone engagement with no commitment to proceed.
See how we work- 1Process analysisFixed price from €3k, architecture and cost in writing
- 2Build and integrateMilestone-based, first working components in six to eight weeks
- 3Go live and handoverDocumentation, training and ongoing support available
FAQ
Questions about RAG system development
Retrieval-augmented generation connects a large language model to your own documents and data. Before the model answers, the system retrieves the passages that match the question and adds them to the prompt, so the answer is grounded in your knowledge and can name its sources. theBlue.ai builds RAG systems that run on-premise, in the cloud or hybrid.
In two phases. First, retrieval: documents are split into sections and turned into embeddings, and a question finds the semantically closest passages, often combined with keyword search. Second, generation: the language model writes the answer from those passages. RAG can also query databases or knowledge graphs by having the model write a structured query.
For knowledge that changes, RAG. It supplies the relevant context at the moment of the question, so an updated document is reflected in the next answer without retraining. Fine-tuning shapes how a model behaves, for example a fixed output style or specialised vocabulary, and has to be repeated when the underlying information changes. The two combine well; see Custom LLM Development.
It significantly reduces them, because the model answers from retrieved evidence instead of memory alone. It does not replace good engineering: if the system retrieves the wrong passages, the answer can still be wrong. That is why data preparation, retrieval quality and a source on every answer matter, so a person can check what the answer rests on.
The sources you already have, whether standard software or built in-house: PDF policies and manuals, wiki pages, contracts, product documentation, databases and knowledge graphs. For apoQlar the system reads security policies stored as PDFs and technical documentation from Confluence. Each format gets its own extraction and chunking.
RAG does not retrain the model on your documents; it retrieves them at the moment of the question. Where the index and the model run is your decision: on-premise with local models, where documents never leave your network, or in your own cloud tenant, as for apoQlar. Our own internal knowledge assistant runs on our GPUs for the same reason.
Usually yes, with the preparation built in. Documentation rarely arrives in one clean format, so extraction, cleaning and grouping are developed for each document type. The process analysis checks the data first and says where preparation pays off, because clean structure improves every later stage.
The process analysis has a fixed price from €3k and ends with an architecture proposal that states scope, timeline and cost for the build. The build is priced in milestones, and first working components typically arrive six to eight weeks in, tested with your real documents and users.
Tell us which process is costing you the most
Describe the process and we’ll come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
- Phase 1 is a standalone engagement, with no commitment to proceed further.
- Senior engineers from day one. No account managers, no handoffs.
- We integrate, never replace. Your systems stay exactly as they are.