KPIs, reports and dashboards
Leaders and teams use shared metric definitions. They can trace the data behind a result and see when it was refreshed.
Data and context · Context Lakehouse
Data you can trust. Context your AI can use.
Systems hold orders, products and customer records. Documents describe rules, while people know the exceptions. We bring this knowledge together in Context Lakehouse, a foundation of AI Engine for reports, assistants and agents.
We build on Azure and Databricks.
Microsoft Azure
Databricks
Architecture
From sources to shared context.
A lakehouse combines data storage with preparation for analytics and AI. We design three layers so each piece of information has a source, passes checks and makes sense to the business.
Context LakehouseAI Engine
Reports and dashboardsAssistants and agents
- 03
Ready for AI
We agree concepts, relationships and KPIs: what customer, availability, revenue or margin mean. The semantic layer provides verified models and views for further use.
- 02
Cleaned
We align identifiers, formats and units. We join records, detect gaps and duplicates, and agree ownership of accuracy and freshness.
- 01
Raw
Data from source systems enters the lakehouse through agreed APIs, connectors or files. We retain its origin and change history.
- ERP
- CRM
- PIM
- Microsoft 365
- Helpdesk
- Documents
Integrations · permissions · quality · lineage
Use cases
Shared definitions. More ways to work.
A report, an assistant’s answer and an agent’s action should use the same data and rules.
Assistants with richer context
Current system data joins knowledge from documents. An assistant can connect a procedure with an order, product or service history.
Text-to-SQL agents
Turn natural-language questions into queries against agreed data views. Results enrich the agent’s context while remaining within granted permissions.
Context Lakehouse → AI Engine
One product model. A shared source of knowledge.
Product knowledge is one application of Context Lakehouse. We connect PIM, catalogue sheets, selection tools and product properties with pricing and availability from company systems.
Explore product knowledge →Sources with a common identity
A product has consistent identifiers, units, properties and versions across its sources. We retain the origin of each fact and the person responsible for it.
A model with selection rules
Parameters, compatibility and configuration rules describe which variants work together. A designer, consultant and customer can use the same verified product knowledge.
Context for assistants and agents
The assistant explains selection criteria. The Product agent checks the configuration, and the Quoting agent uses it in an offer. People review exceptions.
- PIM
- Catalogue sheets
- Selection tools
- Properties, prices and availability
Shared product model
- Designer
- Consultant
- Customer
- Agent
- AI assistant
Security
Security is part of the architecture.
We design data scope, access and operations together with your IT team, using the existing environment where it meets requirements.
Access by role
User and agent identity, permissions for tables, rows and columns, and separate environments. Sensitive data is available only within the agreed scope.
Quality and lineage
Checks for completeness, freshness and consistency. Data owners, alerts and a record of the path from source to report or answer.
Integrations and operations
Controlled agent access to tools through MCP, monitoring and resource limits. We agree updates, incident handling and responsibility after launch.
Technologies
Technology we use in our own work.
We build on proven Azure and Databricks technology. As Microsoft and Databricks partners, we use it for client data foundations and in our own work at WeAreFuture.
Microsoft Azure
Cloud infrastructure, identity, networking and integrations. We adapt the architecture to business requirements and your IT practices.
Databricks
Lakehouse, data processing and analytics. Unity Catalog supports shared governance of access, data lineage and AI assets.
First step
Start with a question your business needs answered.
You do not have to move all your data at once. We choose an area that can deliver value and build a foundation for further development.
01 · Review the starting point
Business goals, available sources, current architecture, quality and access constraints.
02 · Agree the first scope
The data model, integrations, metric definitions and questions used to evaluate the solution.
03 · Launch and develop
A first report, assistant or agent. Review results, improve the solution and decide on further sources and use cases.
Let’s talk about the work you want to improve.
Tell us how a process works today. Together, we will explore where AI Engine can help and which module to start with.
Book a callOr write to: hello@wearefuture.ai
