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Use cases:

How Zeeqwell creates value in practice

THE ZEEQWELL WAY

Context in Action

How organizations use Zeeqwell to create real impact

Zeeqwell turns existing data, systems and knowledge into practical, explainable AI use cases — without replacing what already works.

SERVICE DESK & Customer support cases

From fragmented tickets to contextual resolution.

Zeeqwell connects your service desk data, historical cases and internal knowledge into a single, reasoning-driven support layer.

Support teams can ask natural questions like “Have we seen this issue before?” or “What usually resolves this type of case?” and get answers grounded in verified tickets, documentation and system data.

Typical sources connected

Service desk systems (e.g. Zendesk, ServiceNow, Jira Service Management)
Knowledge bases & documentation (Confluence, SharePoint, PDFs)
Historical tickets and case databases

Business impact

Faster case resolution and shorter response times
Fewer escalations and reduced manual searching
Consistent, explainable answers — even in regulated environments

Sales Patterns & Customer Behavior

Problem:

Changes in customer segments are difficult to detect
Deviations in buying behavior are identified too late
Campaign performance is analyzed manually and after the fact

Customer value:

Improved customer segmentation
Earlier detection of changing customer needs
More relevant and better-targeted offers

How Zeeqwell enables this:
Zeeqwell connects sales data, customer information and campaign results into a single contextual view, enabling AI-driven analysis across systems.

Typical integrations (examples):

CRM systems (e.g. Salesforce, Dynamics 365, HubSpot)
Sales & order databases (SQL databases, data warehouses)
Marketing platforms (campaign data, outreach tools)
Customer interaction data (cases, support history, communications)

What users can ask:

“Which customer segments show changing buying patterns?”
“Are there early signals of declining interest in specific products?”
“Which campaigns performed better or worse than expected?”
“What customer behaviors differ from historical norms?”

Operations & Maintenance

Problem:

Fault history is spread across multiple systems
Previous solutions are hard to find
Technicians rely heavily on individual experience and tribal knowledge

Customer value:

Faster fault resolution
Reduced dependency on key individuals
Better reuse of historical knowledge and experience

How Zeeqwell enables this:
Zeeqwell connects operational data, maintenance history and technical documentation into a single contextual intelligence layer.

Instead of searching across systems or relying on memory, technicians can ask questions and get answers grounded in verified historical data.

Typical integrations (examples):

Maintenance & asset management systems (CMMS, EAM)
Operational logs & work orders
Technical documentation & manuals (PDFs, drawings, procedures)
Historical incident and maintenance databases

What users can ask:

“Have we seen this fault before and how was it resolved?”
“Which components fail most often and why?”
“What actions usually resolve this issue fastest?”
“Summarize maintenance history for this asset.”

Zeeqwell uses RAG and reasoning agents to turn operational data into actionable guidance — directly at the point of work.

Administration & Internal Processes

Problem:

Information is spread across multiple systems and documents
Repetitive internal questions handled manually
Limited visibility into ongoing cases, responsibilities and status
Knowledge locked to individuals rather than shared processes

Customer value:

Reduced administrative workload
Faster internal responses and decision-making
A shared, consistent view of cases, processes and history
Less dependency on individuals and tacit knowledge

How Zeeqwell enables this:
Zeeqwell connects administrative systems and documentation and makes them accessible through AI-driven reasoning.

Typical integrations (examples):

Microsoft 365 (Outlook, SharePoint, OneDrive, OneNote)
ERP / Finance systems (e.g. SAP, Dynamics, Unit4)
Case & workflow systems (e.g. ServiceNow, Jira, custom tools)
Policies, procedures & internal guidelines (PDF, Word, intranet)

What users can ask:

“Which internal cases are currently waiting for approval?”
“What is the latest decision regarding this supplier?”
“How is this process normally handled?”
“Show similar cases from last year.”

Zeeqwell uses RAG and reasoning agents to provide answers grounded strictly in verified internal data.

Reporting & KPI Management

Problem:

Reports are built manually
Heavy reliance on spreadsheets
KPIs are difficult to interpret without additional analysis
Reports are produced, but rarely used in daily decision-making

Customer value:

Faster reporting without manual compilation
More time spent on analysis instead of administration
Clear, actionable decision support — not just numbers

How Zeeqwell enables this:

Zeeqwell removes the need for traditional BI workflows by acting as a reasoning layer directly on top of the underlying data.

Instead of building dashboards, users can ask questions in natural language and get answers grounded in:

raw KPI data
historical performance
definitions, assumptions and context

Reports become dynamic, explainable and interactive, rather than static outputs.

Typical integrations (examples):

Data warehouses & databases (SQL databases, cloud data platforms)
Finance and operational systems (ERP, performance systems)
KPI definitions and documentation (Excel, PDFs, internal documents)

What users can ask:

“Which KPIs are currently deviating from expected levels?”
“What explains the change in this KPI compared to last quarter?”
“Which metrics require management attention right now?”
“Summarize the key insights behind this month’s performance.”

Zeeqwell uses RAG and reasoning agents to turn KPI data into understandable insights, without relying on dashboards or BI tools.

SERVICE DESK & Customer support cases

From fragmented tickets to contextual resolution.

Zeeqwell connects your service desk data, historical cases and internal knowledge into a single, reasoning-driven support layer.

Typical sources connected

Service desk systems (e.g. Zendesk, ServiceNow, Jira Service Management)
Knowledge bases & documentation (Confluence, SharePoint, PDFs)
Historical tickets and case databases

Business impact

Faster case resolution and shorter response times
Fewer escalations and reduced manual searching
Consistent, explainable answers — even in regulated environments

Sales Patterns & Customer Behavior

Problem:

Changes in customer segments are difficult to detect
Deviations in buying behavior are identified too late
Campaign performance is analyzed manually and after the fact

Customer value:

Improved customer segmentation
Earlier detection of changing customer needs
More relevant and better-targeted offers

How Zeeqwell enables this:
Zeeqwell connects sales data, customer information and campaign results into a single contextual view, enabling AI-driven analysis across systems.

Typical integrations (examples):

CRM systems (e.g. Salesforce, Dynamics 365, HubSpot)
Sales & order databases (SQL databases, data warehouses)
Marketing platforms (campaign data, outreach tools)
Customer interaction data (cases, support history, communications)

What users can ask:

“Which customer segments show changing buying patterns?”
“Are there early signals of declining interest in specific products?”
“Which campaigns performed better or worse than expected?”
“What customer behaviors differ from historical norms?”

Operations & Maintenance

Problem:

Fault history is spread across multiple systems
Previous solutions are hard to find
Technicians rely heavily on individual experience and tribal knowledge

Customer value:

Faster fault resolution
Reduced dependency on key individuals
Better reuse of historical knowledge and experience

How Zeeqwell enables this:
Zeeqwell connects operational data, maintenance history and technical documentation into a single contextual intelligence layer.

Instead of searching across systems or relying on memory, technicians can ask questions and get answers grounded in verified historical data.

Typical integrations (examples):

Maintenance & asset management systems (CMMS, EAM)
Operational logs & work orders
Technical documentation & manuals (PDFs, drawings, procedures)
Historical incident and maintenance databases

What users can ask:

“Have we seen this fault before and how was it resolved?”
“Which components fail most often and why?”
“What actions usually resolve this issue fastest?”
“Summarize maintenance history for this asset.”

Zeeqwell uses RAG and reasoning agents to turn operational data into actionable guidance — directly at the point of work.

Administration & Internal Processes

Problem:

Information is spread across multiple systems and documents
Repetitive internal questions handled manually
Limited visibility into ongoing cases, responsibilities and status
Knowledge locked to individuals rather than shared processes

Customer value:

Reduced administrative workload
Faster internal responses and decision-making
A shared, consistent view of cases, processes and history
Less dependency on individuals and tacit knowledge

How Zeeqwell enables this:
Zeeqwell connects administrative systems and documentation and makes them accessible through AI-driven reasoning.

Typical integrations (examples):

Microsoft 365 (Outlook, SharePoint, OneDrive, OneNote)
ERP / Finance systems (e.g. SAP, Dynamics, Unit4)
Case & workflow systems (e.g. ServiceNow, Jira, custom tools)
Policies, procedures & internal guidelines (PDF, Word, intranet)

What users can ask:

“Which internal cases are currently waiting for approval?”
“What is the latest decision regarding this supplier?”
“How is this process normally handled?”
“Show similar cases from last year.”

Zeeqwell uses RAG and reasoning agents to provide answers grounded strictly in verified internal data.

Reporting & KPI Management

Problem:

Reports are built manually
Heavy reliance on spreadsheets
KPIs are difficult to interpret without additional analysis
Reports are produced, but rarely used in daily decision-making

Customer value:

Faster reporting without manual compilation
More time spent on analysis instead of administration
Clear, actionable decision support — not just numbers

How Zeeqwell enables this:

Zeeqwell removes the need for traditional BI workflows by acting as a reasoning layer directly on top of the underlying data.

Instead of building dashboards, users can ask questions in natural language and get answers grounded in:

raw KPI data
historical performance
definitions, assumptions and context

Reports become dynamic, explainable and interactive, rather than static outputs.

Typical integrations (examples):

Data warehouses & databases (SQL databases, cloud data platforms)
Finance and operational systems (ERP, performance systems)
KPI definitions and documentation (Excel, PDFs, internal documents)

What users can ask:

“Which KPIs are currently deviating from expected levels?”
“What explains the change in this KPI compared to last quarter?”
“Which metrics require management attention right now?”
“Summarize the key insights behind this month’s performance.”

Zeeqwell uses RAG and reasoning agents to turn KPI data into understandable insights, without relying on dashboards or BI tools.

Build applications on context

Seamless User Experience

Zeeqwell enables teams to build custom applications, interfaces and workflows directly on the platform - tailored to their organization, data and processes.

Custom Applications

Build what you need. Nothing more.

Create internal apps, dashboards and tools for specific roles and use cases.
From operations and reporting to decision support and automation.

Real-Time Context

Always grounded in reality.

Applications built on Zeeqwell operate on live, contextualized data across systems, ensuring decisions are based on what is actually happening.