Our Services
Enterprise Knowledge Search
Your RAG prototype hallucinates, misses documents, and can't handle real queries. We replace it with production-grade hybrid search — combining keyword precision with semantic understanding — so your team finds exactly what they need, every time.
The problem
Your RAG doesn't work — and everyone knows it.
You built a RAG prototype in a weekend. It worked on 10 test documents. Then you pointed it at your real knowledge base — thousands of PDFs, internal wikis, technical manuals — and it fell apart.
Users ask a question and get a confident answer that's completely wrong. The system misses documents that are clearly relevant. Keyword searches return nothing because the user phrased the query differently than the document. Semantic search surfaces vaguely related content but misses the exact match.
The result: nobody trusts the search, people go back to Ctrl+F and email, and your AI investment sits unused.
How we fix it
Four steps to search that actually works.
Audit your knowledge base
We map your data sources, document types, access patterns, and current search pain points. You get a clear picture of what's broken and why.
Design the retrieval architecture
Hybrid search combining keyword matching with semantic embeddings. We design the pipeline, select embedding models, and define chunking strategies for your content.
Build and index
We ingest your documents, build the vector index, configure BM25 for keyword search, and wire up the reranking layer. Everything is tested against real queries from your team.
Deploy and iterate
Production deployment with monitoring, relevance dashboards, and feedback loops. Your search gets better over time — not worse.
What you get
Measurable results, not promises.
Mission report
"Our RAG used to hallucinate answers and miss half the documents. Now it finds the right passage in under a second."
Under the hood
Open-source search stack.
No black boxes. Every component is auditable, replaceable, and yours.
Apache Lucene
The foundation of modern search — a high-performance, full-featured text search engine library powering hybrid retrieval with BM25 and beyond.
Search Engine
Docling
Advanced document parsing and conversion — extracts structured content from PDFs, DOCX, and other formats for clean, reliable ingestion.
Document Processing
Kotlin + Koog
Kotlin powers our backend with strong typing and coroutines. Koog, JetBrains' Kotlin-native AI framework, orchestrates intelligent agents with type-safe, coroutine-driven pipelines.
AI Agent Framework
DVC
Git-based version control for data and ML pipelines — track datasets, models, and experiments with full reproducibility across your team.
Data Version ControlCommon questions about enterprise search.
What is the difference to Elasticsearch?
Elasticsearch is a powerful full-text search engine, but it only matches keywords — it doesn't understand meaning. Our approach combines keyword matching with semantic vector search, so queries find relevant documents even when the wording differs. Add a reranking layer on top, and you get results that are both precise and contextually aware — something Elasticsearch alone can't deliver.
What document formats do you support?
Everything your team actually uses: PDF, Word, Excel, PowerPoint, HTML, Markdown, plain text, and scanned documents via OCR. We handle nested tables, headers, footers, and multi-column layouts. If your documents have structure, we preserve it in the index.
Why is this better than RAG with pgvector?
pgvector is a great starting point, but it's limited to pure vector similarity search inside PostgreSQL. Our approach combines keyword matching with semantic vector search and adds a reranking layer on top — delivering far more precise and contextually aware results. We also handle document ingestion, chunking, and metadata filtering at scale, which pgvector leaves entirely to you.
How do you prevent hallucinations?
By design. Our system retrieves first, then generates. Every answer includes source citations pointing to the exact document and passage. If the retrieval layer doesn't find relevant content, the system says so — it doesn't make something up.
What about multilingual and multimodal content?
We select embedding models that handle multilingual content natively — a German query can surface an English document and vice versa. Beyond text, our pipeline also processes multimodal content such as images, tables, and scanned PDFs. Docling extracts structured information from complex document layouts, so nothing gets lost regardless of format or language.
How long does implementation take?
We start with a one-week sprint using our managed service — we ingest a sample of your data and deliver first real answers fast, so you can see the value before committing to a full rollout. From there, we adapt and harden the system for your production environment week by week, continuously improving relevance, coverage, and performance as we learn from your real queries and feedback.
Let's fix your search.
30 minutes, no pitch deck. We'll look at your current knowledge base and show you what production-grade search looks like.