An AI system is only as good as the information it can reach. We organize your documents and data, build the pipelines that keep them current, and set up the search that lets your AI answer from your own sources, with references.
What we do
- RAG pipelines
- Semantic search
- Data pipelines
When an AI system gives a wrong answer, the cause is usually not the model. It is missing, outdated or badly organized information.
Your knowledge is spread across shared drives, PDFs, wikis, emails, databases and the memory of experienced colleagues. We bring it together, clean it, and structure it so that both people and AI find the right piece at the right moment.
Technically, this is called retrieval-augmented generation, or RAG: the AI first searches your sources, then answers from what it found and cites it. We build the ingestion, the indexing and the search, and we measure answer quality before anything goes live.
A good fit if
- Your teams lose time looking for documents and procedures
- You want an assistant that answers from your own content
- Your data lives in several systems that disagree
- An AI pilot gave answers that sounded confident but were wrong
We collect and process your documents wherever they live, including scans and complex PDFs.
- Google Drive, SharePoint, Notion, Confluence
- Text recognition for scanned documents
- Automatic updates when a source changes
Document ingestion
We collect and process your documents wherever they live, including scans and complex PDFs.
- Google Drive, SharePoint, Notion, Confluence
- Text recognition for scanned documents
- Automatic updates when a source changes
- 01
Week 1
Inventory
We list your sources, their owners, formats, quality and access rules.
- 02
Week 2
Architecture
We design the storage, indexing and search approach that suits your volume and constraints.
- 03
Weeks 3 to 6
Build and evaluate
We build the pipelines and the search, then measure answer quality on real questions.
- 04
Ongoing
Operate
Sources stay in sync, quality is monitored, and new content is added as you grow.
Databases
- PostgreSQL
- pgvector
- Supabase
- Qdrant
- Pinecone
Pipelines
- Python
- Airbyte
- dbt
- Apache Airflow
Retrieval
- LlamaIndex
- LangChain
- Hybrid search
- Rerankers
Sources
- Google Drive
- SharePoint
- Notion
- Confluence
- SQL databases
- 01
Accurate answers based on your own data
- 02
Fewer invented answers and more trust
- 03
One reliable knowledge base for people and agents
- 04
Less time spent searching, for every team
- Source inventory and data architecture
- Ingestion and sync pipelines
- Search and retrieval service
- Evaluation set and quality report
- Access control and documentation
Before answering, the AI searches your documents for the relevant passages, then writes its answer from them and shows its sources. It answers from your facts rather than from general knowledge.
Further reading
Related services
Book a free audit. We will look at your processes and recommend the service with the fastest return.