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d.AP
What d.AP is
d.AP is an AI context platform for enterprises that need AI agents, applications, and BI tools to work with business knowledge held across systems. Its ontology-based knowledge graph connects CRM, ERP, and analytics using shared semantics while leaving data in its source systems. Federators connect through REST, JDBC, and SOAP to systems including MES, IoT platforms, and document stores, without copying data or requiring ETL.
The visual Workbench supports ontology modeling, isolated testing of changes, and Git-based version control. Aluna turns natural-language requests into graph queries and returns answers with sources, logic, and filters. Knowledge is available through MCP, A2A, and REST/JSON. Live graph dashboards expose metrics with inspectable SPARQL definitions, and can be embedded in other dashboards and apps.
The product describes SSO, SAML, role-based access, and granular permissions. Answers can be traced through sources, joins, filters, and aggregation, with lineage and reasoning traces exportable for audits. Ontologies use OWL and can be exported for reuse. d.AP runs only in cloud environments, in a European cloud as customer-account managed service or digetiers-operated SaaS. Go-live with five to six sources typically takes three months; each additional source takes one to two weeks. Custom enterprise licensing is based on sources and use cases, not token consumption, with pricing on request.
Who it suits
d.AP suits enterprises that want a shared knowledge layer across business systems for agents, applications, and BI. It is relevant for teams that need ontology modeling, traceable answers, and federated access to data that remains in source systems.
What it does well
- Connects enterprise systems through shared semantics without copying data.
- Workbench supports isolated ontology testing and Git-based version control.
- Aluna returns answers with sources, logic, and filters.
- Live dashboards use inspectable SPARQL metric definitions.
- OWL ontologies can be exported for reuse.
- Supports SSO, SAML, role-based access, and granular permissions.
What to know first
- Runs in cloud environments only.
- Pricing is available on request.
- A five-to-six-source go-live typically takes three months.
Limits
The product runs in cloud environments only. A go-live with five to six data sources typically takes three months, and each additional source takes one to two weeks.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Custom enterprise licensing | Based on sources and use cases; not token consumption | On request |
As digetiers.com lists them, read 8 Oct 2026. The maker's own page has the final word.
Getting started
- Visit https://www.digetiers.com/en/product.
- Request a live demonstration by appointment or contact the maker with questions.
- Discuss sources, use cases, and the deployment context for custom enterprise licensing.
- Plan a cloud rollout; five to six sources typically take three months to go live.
Verdict
Choose d.AP if your enterprise needs a governed knowledge layer across business systems for AI agents, applications, and BI. Its federated connections and traceable answers are central strengths. Look elsewhere if you require an on-premises deployment or a published price.
Questions
How is d.AP priced?
It uses custom enterprise licensing based on sources and use cases, rather than token consumption. Pricing is on request.
What does d.AP connect to?
Federators connect through REST, JDBC, and SOAP to systems such as ERP, CRM, MES, IoT platforms, and document stores.
Which interfaces can use its knowledge?
d.AP exposes knowledge through MCP, A2A, and REST/JSON for agents, applications, and BI tools.
Where can d.AP be deployed?
It runs in cloud environments in a European cloud, either in the customer's account as a managed service or as SaaS operated by digetiers. The product currently runs in cloud environments only.
Can its ontology be reused elsewhere?
Yes. Ontologies are modeled in OWL and can be exported in that format for reuse on another platform.
How long does implementation take?
A go-live with five to six data sources typically takes three months. Each additional source takes one to two weeks.
Facts from d.AP's own pages
- Access controls
- The product describes support for SSO, SAML, role-based access, and granular permissions. · digetiers.com, 8 Oct 2026
- AI agent
- Aluna translates natural language into graph queries and returns answers with their sources, logic and filters. · digetiers.com, 8 Oct 2026
- Audience
- The product is aimed at enterprises seeking a shared knowledge layer for agents, applications, and BI across business systems. · digetiers.com, 8 Oct 2026
- Auditability
- Answers can be traced to their source, joins, filters, and aggregation, and the product describes exporting lineage and reasoning traces for audits. · digetiers.com, 8 Oct 2026
- Commercial model
- Licensing is based on the sources and use cases used, rather than token consumption; the page does not state a price. · digetiers.com, 8 Oct 2026
- Dashboards
- Dashboards are live graph views whose metrics retain inspectable SPARQL definitions and can be embedded in other dashboards and apps. · digetiers.com, 8 Oct 2026
- Data portability
- The ontology is modeled in OWL and can be exported in that open format for reuse on other platforms. · digetiers.com, 8 Oct 2026
- Deployment scope
- The company says d.AP currently runs in cloud environments only. · digetiers.com, 8 Oct 2026
- Deployment timing
- The maker says go-live with five to six sources typically takes three months and each additional source takes one to two weeks. · digetiers.com, 8 Oct 2026
- Federation
- It connects to data in source systems through adapters without copying the data or requiring ETL. · digetiers.com, 8 Oct 2026
- Founded
- 2020 · digetiers.com, 28 Sept 2026
- Headquarters
- Stuttgart, Germany · digetiers.com, 28 Sept 2026
- Hosting
- d.AP runs in a European cloud environment, either in the customer's account as a managed service or as SaaS operated by digetiers. · digetiers.com, 8 Oct 2026
- Integrations
- Federators connect to interface technologies including REST, JDBC and SOAP, and to systems such as ERP, CRM, MES, IoT platforms and document stores. · digetiers.com, 8 Oct 2026
- Intended users
- The product is presented for enterprises connecting business knowledge across systems so AI agents, applications and BI tools can use it. · digetiers.com, 8 Oct 2026
- Interfaces
- d.AP exposes knowledge through MCP, A2A and REST/JSON for agents, applications and BI tools. · digetiers.com, 8 Oct 2026
- Knowledge graph
- Its ontology based knowledge graph connects CRM, ERP and analytics through shared semantics without copying data. · digetiers.com, 8 Oct 2026
- Ontology workbench
- The Workbench provides visual ontology modeling, isolated testing of changes and Git based version control. · digetiers.com, 8 Oct 2026
- Portability
- The ontology is stored in OWL and can be exported for reuse on another platform. · digetiers.com, 8 Oct 2026
- Purpose
- d.AP is an AI context platform that models enterprise facts, relationships and rules for use by AI agents. · digetiers.com, 8 Oct 2026
- Security and auditability
- The product describes granular role based access and traceability from answers to their sources, logic and filters. · digetiers.com, 8 Oct 2026
- Source connectivity
- Federators connect through interface technologies including REST, JDBC, and SOAP to systems such as ERP, CRM, MES, IoT platforms, and document stores. · digetiers.com, 8 Oct 2026
- Support and sales
- The maker offers a live demonstration by appointment and says it replies personally to questions. · digetiers.com, 8 Oct 2026
- Time to go live
- A go live with five to six data sources typically takes three months, and each additional source takes one to two weeks. · digetiers.com, 8 Oct 2026
Where d.AP stands on Freedom251
- Knowledge Graph Software#27 of 33