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Company Memory: the data layer that lets AI actually work.
Your systems store information. Twinny turns it into context: a shared company memory that gives every agent and digital worker what it needs to decide and execute.
Definition
Who it is for
- Data Leaders
- CIO / CTO
- Data architecture
- AI Leaders
Company Memory. The Company Memory is a company’s AI-ready data and context layer. It unifies the Data Lake, ERP and CRM data, documents, email, conversations and internal software into a common model, with permissions and traceability, so that any authorised agent can retrieve the right context for each customer, order, record or process. It is not just RAG over documents: it is structured data, knowledge and business rules accessible as services.
The problem: fragmented information, AI without context
Company information is usually spread across CRM, ERP, email, documentation, conversations and internal tools. An agent that only sees one of those sources answers poorly or cannot act. A Data Lake without a context layer does not solve it either: the data is there, but the AI does not know what it means or what it can do with it.
Data Lake + enterprise data + context + AI
A company generates information constantly from dozens of systems and channels. Your Company Memory is built on what already exists, with no migrations, from four families of sources that converge into one shared memory.
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Business systems
- SAP
- Salesforce
- Microsoft Dynamics
- CRM
- ERP
- Vertical systems (PMS, EHR, SIS)
- Proprietary software
- Internal tools
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Communication
- Phone
- WhatsApp Calls
- WhatsApp Messaging
- SMS
- Web
- Chat
- Social media
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Corporate knowledge
- Documentation
- Knowledge bases
- SharePoint
- Files
- Procedures
- Policies
- Catalogues
- Internal information
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Data
- Data Lakes
- Databases
- Data Warehouses
- APIs
- History
- Analytics
- Events
- Structured and unstructured data
Your Company MemoryEvery system creates data. Twinny turns it into shared intelligence.
Feeds
- Digital Workers
- MCPs
- AI Agents
- Authorised external agents
- Enterprise Data Layer / Data Lake: ingestion, normalisation and quality
- Data and knowledge model: entities (customer, order, record), relationships, rules
- Semantic layer: vector search, RAG over documents and queryable structured data
- Permissions and traceability: who can see what and what was consulted in each decision
- Consumption by agents via MCP and APIs
More than RAG
RAG (retrieval-augmented generation) solves search over documents. A digital worker also needs real-time structured data (balance, stock, order status), business rules, per-customer history and memory of previous conversations. The Company Memory combines all three: document knowledge, system data and conversational context.
- AI-ready data: normalised, versioned and with measured quality
- Vector database and semantic search over internal documentation
- Structured real-time queries to ERP, CRM and databases
- Per-customer and per-process memory, with configurable consent and retention
- Data in European territory or in your infrastructure
Roots in data and Big Data
Twinny grew out of experience in enterprise data and Big Data architectures. That is why the Company Memory is not an add-on to the agent: it is the foundation on which digital workers and the AI Operating Layer are built. We design the data model, ingestion and governance before putting an agent to work.
One memory, every agent
The Company Memory is built once with the first process and reused by every following digital worker. It is what makes the second and third project faster: the context already exists.
Frequently asked questions
- What is the Company Memory?
- The AI-ready data and context layer that unifies Data Lake, systems (ERP, CRM), documents and conversations with permissions and traceability, so agents retrieve the right context.
- Is it the same as RAG?
- No. RAG is one part (search over documents). The Company Memory adds real-time structured data, business rules and conversational memory, accessible as services.
- Do we have to migrate our data?
- No. It is built on existing sources through connectors, ingestion and a semantic layer. Your systems remain the source of truth.
- Where does the data live?
- In a European cloud (AWS Frankfurt or Ireland), in your infrastructure or in a hybrid model, according to your data policy. No model training on your data.
- Which agents can use it?
- Twinny’s digital workers and, when the customer authorises it, other agents through MCP and the Agent Gateway, always with permissions and scopes.
Start with one process.
Tell us what you want to automate and you will receive an initial proposal within 24 hours.