Enterprise RAG Development Services on Your Infrastructure
A model that guesses the numbers in your financial tables is a liability with a chat window.
STX Next builds retrieval-augmented generation (RAG) systems on DocSpeaker, our self-hosted RAG platform. Your teams get answers from contracts, manuals, regulations, and reports, with a citation for every claim and exact figures pulled from the tables inside them. Your data stays in your environment. The code is yours.
What our RAG development services deliver
STX Next provides retrieval-augmented generation services for enterprises that need AI answers they can verify. We deliver on DocSpeaker, a modular RAG platform that runs on-premise, in your private or sovereign cloud, or fully air-gapped with local models.
DocSpeaker converts tables in PDFs into queryable database records, turns charts and diagrams into searchable text, and selects the right retrieval strategy for each question.
Every engagement starts with a fixed-price proof of concept on your real documents and ends with an answer quality report and full code ownership.
Where standard RAG fails, and what DocSpeaker does instead
A basic RAG setup embeds your documents, runs one similarity search, and hopes for the best. That works for a demo. It breaks on the documents that matter most.
Numbers get approximated
Standard OCR
Vector search treats a financial table like a paragraph, so the model paraphrases figures it should quote.
DocSpeaker
Extracts tables from PDFs into structured Postgres schemas, and the model runs SQL against them. A question like "how many contracts exceed 1 million" gets a COUNT, not an estimate.
Charts and diagrams are invisible
Standard OCR
Pipelines skip images, so everything in a chart or engineering diagram is lost.
DocSpeaker
Multimodal RAG uses vision models to describe each figure and adds that description to the answer context.
One retrieval method for every question
Standard OCR
Uses a single similarity search for everything.
DocSpeaker
Elastic RAG includes 10 retrieval strategies and an Adaptive Router that picks one per query, so simple questions stay fast and cheap while complex ones get deeper analysis.
No way to prove quality
Standard OCR
Relies on guesswork and subjective impressions, leaving accuracy unmeasured and unverified.
DocSpeaker
Benchmarks answers against 21 evaluation metrics (including RAGAS and BERTScore) to track accuracy and compare configurations objectively.
Data that cannot leave the building
Standard OCR
Might send sensitive documents to external APIs and third-party cloud services.
DocSpeaker
Runs fully on-premise and air-gapped with local models via Ollama, so sensitive documents never reach an external API.
Lock-in to one model or vendor
Standard OCR
Tightly couples your application to a specific proprietary model provider or vector database.
DocSpeaker
You own the code. Embedding models, vector databases, and LLMs can be swapped as your requirements or budget change.
DocSpeaker: the enterprise RAG platform behind every delivery
DocSpeaker is a production-ready RAG platform we deploy and adapt for each client. It ships as a set of containers and starts with a single docker compose up, so your team works with a running system from the first week instead of a blank repository.
Elastic RAG
Ten retrieval strategies, including HyDE, Multi-Query, and Corrective RAG. Administrators switch between them in the admin panel with no code changes and no redeployment.
Table-RAG
Converts PDF tables into structured database records. The model answers numerical questions with precise SQL queries, removing hallucinated figures in financial content.
Multimodal RAG
Parses charts, diagrams, and figures using vision models to convert them into searchable text descriptions, making visual information fully available to answers.
Document processing you can inspect
Answers with sources
Controlled access through data bundles
A test bench before rollout
Deployed where your data is allowed to live
Your security and regulatory constraints decide the architecture, not our preferences.
Three ways to start with STX Next RAG consulting
A private knowledge workspace built on Open WebUI and n8n, with department-level data isolation. The fastest way to give teams secure, document-aware AI while you plan a larger build.
A working DocSpeaker prototype connected to a sample of your documents. You receive an answer quality report with named failure cases, a clear go or no-go recommendation, and full ownership of the code.
The path from MVP to production: complex document pipelines, SSO and OAuth, multi-tenant access control, infrastructure scaling, monitoring, and handover to your team.
How our RAG implementation services work
Discovery and document sample
We agree on the use case, the questions users need answered, and a representative set of documents. We also map your hosting and compliance constraints.
Ingestion and parsing review
We load your documents into DocSpeaker and review the parsed output with you. If scans, tables, or layouts need special handling, this is where we find out.
Strategy tuning
We test retrieval strategies, chunking, and models against your real questions in the admin chat and pick the configuration that performs best.
Quality report and decision
You get a benchmark of answer quality across the evaluation metrics, a list of failure cases, and an honest recommendation on whether to proceed.
Production build
We add SSO, access control, integrations, and monitoring, then deploy to your target environment.
Handover and support
Code, documentation, and configuration go to your repository. Ongoing support is available if you want it.
RAG case studies
Linde: multilingual RAG for global technical documentation
Global engineering teams at Linde struggled to search fragmented multilingual PDFs, scans, and tables for critical equipment specs and safety policies.
STX Next delivered an enterprise RAG platform in Linde's Azure environment. Employees now query technical documentation in natural language and receive verified answers with exact source citations. The multilingual system stays continuously synchronized with changing documentation while an integrated monitoring framework tracks answer quality over time.
Industrial | Germany
Podimo: semantic search and a conversational search assistant
Podimo's original search struggled with natural language queries, delivering irrelevant results to listeners.
STX Next combined semantic and lexical search using learning-to-rank models and launched a conversational search assistant to clarify user intent. We also built a semantic vector store covering over 11 million audio records with real-time embedding generation and quantization. This delivered 27% better search quality and a 3.5% lift in conversion.
Audio streaming | Denmark
Tour Partner Group: AI extraction from booking emails
Sales specialists at TPG handled hotel booking requests by reading emails manually, and response times suffered.
STX Next built a pipeline that classifies emails, detects their language, translates them, and extracts the key booking details. It then checks the details against live availability and event data, suggests hotel allocations, and drafts the reply. Checking whether dates were available went from 30 minutes to 15 seconds.
Hospitality and travel | UK
RAG solutions for regulated and document-heavy industries
Banking and Financial Services
Assistants that help advisors and consultants find answers across 100 to 200 internal regulations and policies, with Table-RAG for figures in financial reports and sovereign cloud hosting where regulators require it.
Oil&Gas and Energy
Enterprise knowledge base AI for engineering documentation, HSE manuals, and upstream and downstream procedures, available to field and plant teams in their own language.
Manufacturing and Industrials
Fast access to equipment specifications, maintenance procedures, and safety protocols, including the values locked in technical tables and diagrams.
Why choose STX Next as your RAG development company
Every engagement starts with a fixed-price PoC on your documents and an honest go or no-go recommendation.
DocSpeaker gives your project working ingestion, retrieval, evaluation, and access control from day one, adapted to your needs rather than built from scratch.
21 evaluation metrics turn "it seems to work" into numbers your stakeholders can review.
Full code ownership and deployment on-premise, in sovereign cloud, or in your own cloud tenant.
20+ years of Python engineering, 1,000+ delivered projects, ISO/IEC 27001 certified information security, and AWS Advanced Tier Services Partner status.
Test RAG on your own documents
Book a 30-minute call with our AI team. Bring a description of your documents and the questions your teams need answered. We will tell you whether a DocSpeaker PoC makes sense and what it would take.
FAQ
How long does a RAG project take?
A RAG Mini PoC takes 2 to 4 weeks. An Agentic AI Workspace goes live in about 2 weeks. A full enterprise RAG deployment usually starts at 10 weeks, depending on the number of document sources, integrations, and access control requirements.
How much do RAG development services cost?
Our entry points are fixed-price, so you get a working prototype and a quality report before you commit a larger budget. Enterprise deployments are scoped individually. Contact us for a quote based on your documents and environment.
How do we know RAG will work on our documents?
That depends on your documents, which is why we test on them first. The Mini PoC connects DocSpeaker to a sample of your real content, benchmarks answer quality, lists the questions it gets wrong, and gives you a go or no-go recommendation.
Can DocSpeaker run fully on-premise?
Yes. DocSpeaker supports air-gapped deployment with local embedding models and local LLMs via Ollama, so no document or query leaves your infrastructure. It also runs in sovereign clouds such as CloudFerro, or in your own Azure or AWS tenant.
How accurate are the answers on tables and financial data?
DocSpeaker's Table-RAG loads tables from your PDFs into a Postgres database, and the model answers numerical questions with SQL queries over those values. This avoids the approximation that happens when tables are handled as plain text.
Which LLMs can we use?
Local open-weight models through Ollama and Hugging Face, or API models such as OpenAI. You can assign different models to different user groups, and swap models later without rebuilding the system.
How do you control who sees which documents?
Administrators assign each user a data bundle that defines the knowledge base, API keys, and model they can use. Production deployments add SSO or OAuth and multi-tenant access control.
Do we own the code?
Yes. All code, configuration, and documentation go to your repository. There is no license lock-in.
Do you offer RAG as a service?
We deploy DocSpeaker in your environment rather than hosting your documents on a shared platform. If you prefer not to run it yourselves, we can provide ongoing support and operation after handover.
What is DocSpeaker?
DocSpeaker is STX Next's self-hosted RAG platform. We use it as the foundation for client RAG projects, then adapt retrieval, document processing, access control, and deployment to each client's requirements.
