Build RAG systems, LLM capabilities and agents where knowledge and work already live: ERP, CRM, DMS, SharePoint, Confluence, databases, APIs and internal applications.
I build the smallest useful integration layer between the existing system landscape and AI. For RAG, that covers real data-source connections, parsing, chunking and retrieval as well as permissions, source attribution, evaluation and operations – API-first and without an unnecessary parallel platform.
RAG system using real data sources instead of demo file uploads
ERP, CRM, DMS, SharePoint, Confluence, databases and APIs connected deliberately
Permissions, source attribution and data freshness preserved in retrieval
Measurable quality, logging and maintainable operations instead of a black box
A RAG system is more than a vector database plus a chat window. The complete path from source system to grounded answer matters: which data are connected, how they stay current, which users may retrieve which content and how retrieval and answers are evaluated.
SharePoint, Confluence, DMS, ERP, CRM, databases, APIs and file stores are connected through existing interfaces, webhooks or controlled sync jobs. Metadata, versions, deletions and updates are handled from the start.
Documents are parsed cleanly, processed with OCR where needed, chunked deliberately and indexed. Embeddings, lexical search, hybrid search or reranking are selected against real questions – with traceable sources instead of plausible black-box answers.
Role and permission filters remain effective during retrieval. A representative evaluation set, source freshness, logging, failure paths and monitoring create a measurable baseline before wider rollout.
The starting point is not a vector-database choice but a clear business process with real questions, real documents and real permissions. A focused pilot can then show quickly whether the approach works.
We identify user groups, typical questions, relevant source systems, document types, freshness requirements and permissions. This defines what the RAG system actually needs to deliver.
Parsing, OCR, chunking, metadata, embeddings and search logic are implemented on real data. Golden questions show early whether the right sources are found reliably and referenced correctly.
Authentication, roles, APIs, user interface, feedback and tool calls where relevant are integrated so users stay in their existing work context and permissions cannot be bypassed.
Retrieval quality, source coverage, source freshness, latency, cost and failure paths become measurable. Configuration, interfaces and monitoring are documented so the system remains maintainable.
This service fits when knowledge lives in SharePoint, Confluence, DMS, ERP, CRM, databases, files or business systems and should become a dependable RAG system, knowledge assistant or AI capability inside an existing workflow.
If stack, hosting or system boundaries are still fundamentally open, start with AI architecture consulting. If a RAG PoC already exists and retrieval quality, reliability or go-live confidence is the problem, production readiness is the better fit.
The services intentionally solve different jobs. This keeps it clear whether you need an architecture decision, concrete RAG/AI integration or evaluation before production.
If target architecture, hosting, permissions or system boundaries are open, decide the technical direction before implementation.
When the system works in principle but retrieval quality, reliability, guardrails or release criteria are missing.
When private cloud, EU hosting, on-premise LLMs or hybrid architecture materially shape the RAG and integration decision.
Name the use case, the most important data sources and the existing target system. I will assess what a focused pilot can look like and which integration path is likely to be the most maintainable and commercially useful.