From data strategy to production AI.
thaink² supports enterprises across the data and AI journey from architecture and custom agent design to deployment, pipelines and production monitoring.
From data strategy to production AI.
thaink² supports enterprises across the data and AI journey from architecture and custom agent design to deployment, pipelines and production monitoring.
From foundation to production.
From foundation to production.
Enterprise AI does not become operational by adding a model on top of existing systems. It requires the right data foundation, an agent architecture designed around the business workflow, reliable access to enterprise systems, and an operating environment capable of running, monitoring and improving those agents over time.
thaink² brings these layers together through three complementary service areas from defining the architecture, to building specialized AI agents, to integrating and operating them in production.
Data Strategy & Architecture
Build the right foundation before building the AI layer. We help structure the data architecture, clarify the target environment and define a realistic path from existing systems to production use cases.
Data and architecture assessment
Target architecture and data foundations
End-to-end guidance from strategy to implementation
Custom AI Agents & Models
When a standard agent is not enough, we design AI agents around your business processes, data and operational context.
Agent and model design
Training and adaptation to the use case
Business integration around your workflows
Integration & MLOps
Move from a working AI use case to a production system that can be deployed, connected and operated over time.
Deployment into the target environment
Data and AI pipelines
Monitoring and production operations
Strategy. Build. Operate.
Strategy. Build. Operate.
AI initiatives often get stuck between a promising proof of concept and a system the business can actually use. The data may be fragmented, the agent disconnected from operational tools, or the deployment too fragile to move beyond experimentation.
Our services cover the complete path from business problem to production: understand the environment, design the right architecture, build the intelligence layer, connect it to enterprise systems and make it reliable enough to operate over time.
Strategy
Define what should be built before deciding how to build it.
We start with the business problem, the decisions that need to improve and the workflows where AI can create meaningful value. From there, we assess the existing data landscape, systems, dependencies and technical constraints to determine what is realistically possible.
The objective is to align business priorities with a target data and AI architecture before unnecessary technology is introduced and to establish a roadmap that technical and business teams can execute together.
Business priorities · AI readiness · Data landscape · Target architecture · Roadmap
Strategy
Define what should be built before deciding how to build it.
We start with the business problem, the decisions that need to improve and the workflows where AI can create meaningful value. From there, we assess the existing data landscape, systems, dependencies and technical constraints to determine what is realistically possible.
The objective is to align business priorities with a target data and AI architecture before unnecessary technology is introduced and to establish a roadmap that technical and business teams can execute together.
Business priorities · AI readiness · Data landscape · Target architecture · Roadmap
Build
Turn the use case into an AI system that can actually perform the work.
Once the foundations are clear, we design and build the agentic layer around the workflow itself. That can mean a specialized agent, a knowledge-driven assistant, an analytical workflow or a multi-agent system combining several capabilities.
We define the agent mission, select the appropriate model, connect enterprise knowledge and data, attach the tools required to perform actions and design the orchestration needed to produce reliable outputs rather than another isolated chatbot experience.
Agent design · Models · RAG · Data access · Tools · Multi-agent orchestration
Build
Turn the use case into an AI system that can actually perform the work.
Once the foundations are clear, we design and build the agentic layer around the workflow itself. That can mean a specialized agent, a knowledge-driven assistant, an analytical workflow or a multi-agent system combining several capabilities.
We define the agent mission, select the appropriate model, connect enterprise knowledge and data, attach the tools required to perform actions and design the orchestration needed to produce reliable outputs rather than another isolated chatbot experience.
Agent design · Models · RAG · Data access · Tools · Multi-agent orchestration
Integrate
Connect the intelligence layer to the systems where the business already operates.
An agent only becomes useful when it can access the right context and interact with the systems involved in the workflow. We connect AI applications to enterprise data, databases, APIs, business software, documents and collaboration tools while respecting the access and infrastructure constraints of the organization.
The goal is to make AI part of the existing operating environment instead of creating another disconnected application employees have to work around.
Databases · APIs · Enterprise apps · Knowledge · Authentication · Workflows
Integrate
Connect the intelligence layer to the systems where the business already operates.
An agent only becomes useful when it can access the right context and interact with the systems involved in the workflow. We connect AI applications to enterprise data, databases, APIs, business software, documents and collaboration tools while respecting the access and infrastructure constraints of the organization.
The goal is to make AI part of the existing operating environment instead of creating another disconnected application employees have to work around.
Databases · APIs · Enterprise apps · Knowledge · Authentication · Workflows
Operate
Make the system reliable enough to become part of everyday operations.
A successful prototype proves that something can work. Production requires making sure it can run repeatedly, automatically and visibly.
We help deploy the required runtime, automate executions through schedules, events and workflows, and put the monitoring layer in place so teams can inspect runs, tool calls, errors and operational behavior. The system can then evolve based on real usage instead of remaining frozen at the POC stage.
Deployment · Automation · Monitoring · Logs · Evaluation · Operations
Operate
Make the system reliable enough to become part of everyday operations.
A successful prototype proves that something can work. Production requires making sure it can run repeatedly, automatically and visibly.
We help deploy the required runtime, automate executions through schedules, events and workflows, and put the monitoring layer in place so teams can inspect runs, tool calls, errors and operational behavior. The system can then evolve based on real usage instead of remaining frozen at the POC stage.
Deployment · Automation · Monitoring · Logs · Evaluation · Operations
Built for teams turning enterprise data into working AI systems.
DATA & IT · AI & ENGINEERING · BUSINESS TEAMS · LEADERSHIP
What services does thaink² provide?
thaink² provides Data Strategy & Architecture, Custom AI Agents & Models, and Integration & MLOps services covering the journey from data strategy to production.
What types of industry data can thaink² work with?
Yes. Custom AI Agents & Models are designed around specific business contexts, including the data, models, tools and integration required for the target workflow.
Can thaink² help move an existing AI prototype into production?
Yes. Integration & MLOps covers production architecture, deployment, pipelines and operational considerations.
Do we need to replace our existing data stack?
No. The architecture is designed around the organization’s existing data, systems and infrastructure.
Built for teams turning enterprise data into working AI systems.
DATA & IT · AI & ENGINEERING · BUSINESS TEAMS · LEADERSHIP
What services does thaink² provide?
thaink² provides Data Strategy & Architecture, Custom AI Agents & Models, and Integration & MLOps services covering the journey from data strategy to production.
What types of industry data can thaink² work with?
Yes. Custom AI Agents & Models are designed around specific business contexts, including the data, models, tools and integration required for the target workflow.
Can thaink² help move an existing AI prototype into production?
Yes. Integration & MLOps covers production architecture, deployment, pipelines and operational considerations.
Do we need to replace our existing data stack?
No. The architecture is designed around the organization’s existing data, systems and infrastructure.
Insights for building AI that works in the real world.
Explore practical guides, technical deep dives and field insights on enterprise data, AI agents, architecture, integration and production operations. From defining the right foundations to deploying reliable agentic systems, our resources cover the full path from idea to production.
What if your next analysis were ready before your next meeting?
Show us your environment and a use case. See how thaink² can query, monitor, and act on your data.
Our mission
Our vision
Move from data you look at to data that works continuously for your business.
We believe the next generation of Data platforms will do more than show what happened. They will monitor, explain, anticipate, and prepare decisions before some questions even need to be asked.
What is an Agentic Data Platform?
An Agentic Data Platform uses specialized AI agents to work with enterprise data. Unlike a static dashboard or a simple chatbot, agents can explore multiple sources, run analyses, generate visualizations, produce deliverables, and execute recurring missions.
What’s the difference between Proactive Mode and Exploratory Mode?
In Exploratory Mode, the user asks a question and thaink² runs the analysis on demand. In Proactive Mode, a mission is defined in advance: agents monitor the data based on a set schedule or specific conditions and automatically deliver the relevant results.
Do I need to know SQL to use thaink²?
Not for business use cases. Users can ask questions in natural language and get analyses, tables, charts, and dashboards without writing SQL queries themselves.
What data sources can thaink² connect to?
The platform is designed to work with the systems already in place across your organization: databases, data warehouses, ERP, CRM, APIs, files, and document repositories. Available connectors depend on your environment and use case.
What types of outputs can the agents produce?
Depending on the agent and use case, outputs can include analyses, tables, charts, dashboards, forecasts, alerts, summaries, and reports. The goal is to deliver an actionable result, not just a text response.
Can thaink² be deployed on our own infrastructure?
thaink² supports deployment options designed around enterprise requirements, including organizations that need greater control over their infrastructure, models, and data. Our teams help define the architecture that best fits the project.
Which part of the thaink² ecosystem is open source?
ApowerB is the open-source agentic framework developed by thaink². It enables technical teams to build and operate their own agents, with support for RAG, Text-to-SQL, multi-LLM setups, and APIs.
How do I get started with thaink²?
The easiest way to start is with a concrete use case: reporting, analysis, forecasting, document search, or monitoring. An initial session helps define the available data, expected outcomes, and the most suitable deployment approach.
Join our Discord community
Connect with builders, Data teams, and AI practitioners. Share ideas, get technical help, discuss agentic architectures, and follow the latest developments around ApowerB.
Our newsletter (Soon)
Data that takes action, straight to your inbox.
Field insights, agentic architectures, benchmarks, use cases, and the latest from ApowerB. Only what’s worth reading.
What if your next analysis were ready before your next meeting?
Show us your environment and a use case. See how thaink² can query, monitor, and act on your data.
Our mission
Our vision
Put the power of data in the hands of decision-makers.
Today, too many business questions still depend on an export, a dashboard, or the availability of a Data team. thaink² changes that by letting teams query their data directly, while agents handle the analysis and recurring work.
What is an Agentic Data Platform?
An Agentic Data Platform uses specialized AI agents to work with enterprise data. Unlike a static dashboard or a simple chatbot, agents can explore multiple sources, run analyses, generate visualizations, produce deliverables, and execute recurring missions.
What’s the difference between Proactive Mode and Exploratory Mode?
In Exploratory Mode, the user asks a question and thaink² runs the analysis on demand. In Proactive Mode, a mission is defined in advance: agents monitor the data based on a set schedule or specific conditions and automatically deliver the relevant results.
Do I need to know SQL to use thaink²?
Not for business use cases. Users can ask questions in natural language and get analyses, tables, charts, and dashboards without writing SQL queries themselves.
What data sources can thaink² connect to?
The platform is designed to work with the systems already in place across your organization: databases, data warehouses, ERP, CRM, APIs, files, and document repositories. Available connectors depend on your environment and use case.
What types of outputs can the agents produce?
Depending on the agent and use case, outputs can include analyses, tables, charts, dashboards, forecasts, alerts, summaries, and reports. The goal is to deliver an actionable result, not just a text response.
Can thaink² be deployed on our own infrastructure?
thaink² supports deployment options designed around enterprise requirements, including organizations that need greater control over their infrastructure, models, and data. Our teams help define the architecture that best fits the project.
Which part of the thaink² ecosystem is open source?
ApowerB is the open-source agentic framework developed by thaink². It enables technical teams to build and operate their own agents, with support for RAG, Text-to-SQL, multi-LLM setups, and APIs.
How do I get started with thaink²?
The easiest way to start is with a concrete use case: reporting, analysis, forecasting, document search, or monitoring. An initial session helps define the available data, expected outcomes, and the most suitable deployment approach.
Join our Discord community
Connect with builders, Data teams, and AI practitioners. Share ideas, get technical help, discuss agentic architectures, and follow the latest developments around aPowerB.
Our newsletter (Soon)
Data that takes action, straight to your inbox.
Field insights, agentic architectures, benchmarks, use cases, and the latest from ApowerB. Only what’s worth reading.