The right slice of your organization’s knowledge, ready for AI.
DAIMO turns SharePoint, file shares, decades of email, and a million PDFs into the curated dataset your AI was supposed to be reading. Inside your perimeter. No bytes leave.
Most AI projects skip the most important step.
Teams ship solid retrieval pipelines and agent frameworks and then point them at a corpus nobody curated. Is this file safe? Is it current? Is it relevant? Has it been enriched so the model can find the right answer? Without that, even the best systems surface customer IDs, privileged content, and board materials to anyone who asks.
KVKK, PDPL, the EU AI Act, and sector rules in banking, healthcare, and insurance require proof of where personal data lives and how AI systems handle it. Global SaaS tools were not built for that.
Curate before you compute.
DAIMO maps your data, removes what shouldn’t be there, and enriches everything with the context AI needs. Whatever the system reaches for, the right answer is already there.
Sensitive data removed.
Every file screened against 40+ entity types, including T.C. Kimlik No, Saudi National ID, and Emirates ID. Nothing regulated reaches the prompt.
Relevant files only.
Outdated, off-topic, and low-quality content is filtered out. Your AI retrieves from the slice that matters.
Enriched with metadata.
Sensitivity, document type, business unit, and custom taxonomies, auto-tagged by LLMs you control.
From file dump to AI-ready knowledge.
The same SharePoint library, before and after DAIMO. Raw files become a labelled, governed dataset your retrieval stack can trust.
SharePoint, Documents
support_guide_v3.pdf
Jan 12 2024
hr_policy_2019.docx
Mar 3 2019
SharePoint, Documents › Enriched by DAIMO
support_guide_v3.pdf
Jan 12 2024
Topic
SupportSensitivity
SafeRelevant to
EMEA support AIDocument status
Currenthr_policy_2019.docx
Mar 3 2019
Topic
HRSensitivity
SafeRelevant to
Colleague AssistantDocument status
OutdatedHow DAIMO works. Five steps.
01
Connect to your sources.
DAIMO indexes SharePoint, file shares, S3, Azure Blob, and email archives in place. OCR, parse, chunk, one pass, no exports.
02
Tag at scale.
Auto-classification across the corpus. Sensitivity labels, PII detection, document type, business unit, and a taxonomy that fits your domain.
03
Slice for the use case.
Assemble purpose-built datasets for RAG, Copilot, and agent workflows, by relevance, topic, jurisdiction, quality, or clearance.
04
Deliver to your pipelines.
Write metadata back to the source system, or ship a curated dataset to your retrieval stack. Either way, every team works from the same enrichment.
05
Maintain over time.
DAIMO watches the source. New documents are enriched as they land. Slices refresh so your AI never goes stale.
Built for the enterprise perimeter.
Your perimeter, your control.
On-premise, air-gapped, or sovereign cloud. No outbound calls. Refuses to start if a third-party cloud key is present.
Bring your own LLM.
Any OpenAI-compatible endpoint, self-hosted Llama, Mistral, Qwen, or Gemini. Plug in what you already run.
One metadata standard.
Centralized taxonomy, tag definitions, and owners across every unstructured system. Every team starts from the same foundation.
Installed in thirty minutes.
Single-tenant, single-command install. Helm chart for Kubernetes and OpenShift.
Petabyte scale.
Worker queue and vector store sized for enterprise corpora. Thousands of files per minute.
Audit-ready.
A standing record of what is safe to feed into AI systems. KVKK, PDPL, and EU AI Act reviews start here.
Sixty days to a go / no-go.
Day 0–14
Discovery.
Staging deployment in your data center, pointed at one repository.
Day 15–45
Classification & policy.
Auto-classification across the dataset. Compliance reviews PII accuracy.
Day 46–60
Enforcement & handover.
Signed annual contract, or a clear list of gaps. Either is useful.