Custom AI Agents & Models
When a standard agent is not enough, we design AI agents around your business processes, data and operational context.
Custom AI Agents & Models
When a standard agent is not enough, we design AI agents around your business processes, data and operational context.
Design. Equip. Orchestrate.
Design. Equip. Orchestrate.
A useful enterprise agent is more than a model and a prompt. We define its mission, select the right intelligence layer, connect the knowledge and tools it needs, and design the orchestration required to turn a specific business workflow into a working AI system.
Use Case Design
Define exactly what the agent is responsible for.
We start by translating the business process into a clear agent mission: what information it receives, what it needs to understand, which decisions it can make, what it should produce and where human intervention remains necessary. This prevents the project from becoming a generic conversational assistant and creates a concrete specification around a repeatable business task that can actually be tested and improved.
Mission · Inputs · Decisions · Outputs · Escalation
Use Case Design
Define exactly what the agent is responsible for.
We start by translating the business process into a clear agent mission: what information it receives, what it needs to understand, which decisions it can make, what it should produce and where human intervention remains necessary. This prevents the project from becoming a generic conversational assistant and creates a concrete specification around a repeatable business task that can actually be tested and improved.
Mission · Inputs · Decisions · Outputs · Escalation
Agent Architecture
Choose the right execution pattern for the problem.
Some workflows only need one specialized agent. Others require several agents, deterministic steps, routing logic or different capabilities working together. We design the architecture around the complexity of the task instead of forcing every use case into the same pattern. The objective is to keep simple workflows simple while giving more demanding processes the orchestration structure they need to remain understandable and controllable.
Single agent · Multi-agent · Routing · Orchestration
Agent Architecture
Choose the right execution pattern for the problem.
Some workflows only need one specialized agent. Others require several agents, deterministic steps, routing logic or different capabilities working together. We design the architecture around the complexity of the task instead of forcing every use case into the same pattern. The objective is to keep simple workflows simple while giving more demanding processes the orchestration structure they need to remain understandable and controllable.
Single agent · Multi-agent · Routing · Orchestration
Model Selection
Use the model that fits the workload — not simply the most powerful one.
Different tasks have different requirements around reasoning quality, latency, cost, context size and deployment constraints. We select and configure the model approach according to the agent’s actual mission and the environment in which it will operate. Because the underlying architecture is not tied to a single model provider, the intelligence layer can evolve as requirements, economics and model capabilities change.
Model fit · Reasoning · Latency · Cost · Flexibility
Model Selection
Use the model that fits the workload — not simply the most powerful one.
Different tasks have different requirements around reasoning quality, latency, cost, context size and deployment constraints. We select and configure the model approach according to the agent’s actual mission and the environment in which it will operate. Because the underlying architecture is not tied to a single model provider, the intelligence layer can evolve as requirements, economics and model capabilities change.
Model fit · Reasoning · Latency · Cost · Flexibility
Enterprise Knowledge
Ground the agent in the information your organization actually trusts.
When a workflow depends on internal knowledge, we connect the agent to the relevant documents and knowledge sources through retrieval-based architectures such as RAG. Instead of expecting the model to know company-specific information, the agent retrieves the context required for the question or task before generating its response. This creates a much stronger foundation for internal search, document analysis and knowledge-intensive workflows.
RAG · Documents · Retrieval · Business context
Enterprise Knowledge
Ground the agent in the information your organization actually trusts.
When a workflow depends on internal knowledge, we connect the agent to the relevant documents and knowledge sources through retrieval-based architectures such as RAG. Instead of expecting the model to know company-specific information, the agent retrieves the context required for the question or task before generating its response. This creates a much stronger foundation for internal search, document analysis and knowledge-intensive workflows.
RAG · Documents · Retrieval · Business context
Tools & Actions
Move beyond answering questions and let the agent perform work.
A useful enterprise agent often needs to interact with systems, not only generate text. We connect the tools required by the workflow so the agent can query databases, retrieve information, work with files, call APIs or interact with supported business applications. Each capability is attached according to the mission of the agent, keeping the action surface focused on what the workflow actually requires.
Databases · APIs · Files · Business applications · Tools
Tools & Actions
Move beyond answering questions and let the agent perform work.
A useful enterprise agent often needs to interact with systems, not only generate text. We connect the tools required by the workflow so the agent can query databases, retrieve information, work with files, call APIs or interact with supported business applications. Each capability is attached according to the mission of the agent, keeping the action surface focused on what the workflow actually requires.
Databases · APIs · Files · Business applications · Tools
Multi-Agent Orchestration
Combine specialized capabilities when one agent should not do everything.
Complex workflows can be decomposed into agents with different responsibilities and connected through sequential, parallel or routing patterns. One agent can retrieve information, another analyze it and another produce the final output, while the orchestration layer coordinates how work moves between them. This lets us build more structured systems without hiding the entire business process inside one oversized prompt.
Sub-agents · Sequential · Parallel · Routing · Workflows
Multi-Agent Orchestration
Combine specialized capabilities when one agent should not do everything.
Complex workflows can be decomposed into agents with different responsibilities and connected through sequential, parallel or routing patterns. One agent can retrieve information, another analyze it and another produce the final output, while the orchestration layer coordinates how work moves between them. This lets us build more structured systems without hiding the entire business process inside one oversized prompt.
Sub-agents · Sequential · Parallel · Routing · Workflows
Evaluation & Production Readiness
Test the behavior before the agent becomes part of the business process.
A working demo is not the same thing as a production-ready agent. We review how the agent behaves on expected tasks, edge cases and failure scenarios, and verify that its outputs, integrations and operational constraints match the intended workflow. Where required, the design can then be refined before wider deployment so teams are not discovering fundamental weaknesses after the system is already being used.
Testing · Failure cases · Output quality · Production readiness
Evaluation & Production Readiness
Test the behavior before the agent becomes part of the business process.
A working demo is not the same thing as a production-ready agent. We review how the agent behaves on expected tasks, edge cases and failure scenarios, and verify that its outputs, integrations and operational constraints match the intended workflow. Where required, the design can then be refined before wider deployment so teams are not discovering fundamental weaknesses after the system is already being used.
Testing · Failure cases · Output quality · Production readiness
Turn a business workflow into an AI agent built for the job.
AI & ENGINEERING · DATA & IT · OPERATIONS · BUSINESS TEAMS
What can thaink² build as a custom AI agent?
We build agents around specific enterprise workflows rather than generic chatbot experiences. An agent can be designed to analyze data, retrieve internal knowledge, work with documents, interact with tools and APIs, generate structured outputs or coordinate several steps and specialized agents within a broader workflow.
How is a custom agent different from a standard chatbot?
A chatbot primarily answers messages. A custom enterprise agent is designed around a mission and can be given access to business context, data, knowledge and tools required to complete that mission. Its behavior, model, instructions, actions and orchestration are configured according to the workflow rather than around open-ended conversation alone.
Can you work with different AI models?
Yes. The underlying thaink²/aPowerB architecture supports multiple model providers rather than tying every agent to a single LLM. Model selection can therefore be adapted to requirements such as reasoning quality, latency, cost, context needs and the technical constraints of the application.
Can multiple agents work together?
Yes. When the workflow benefits from specialization, several agents can be orchestrated through sequential, parallel or routing patterns. For example, one agent can retrieve context, another analyze data and another generate the final output. We choose the simplest architecture that can reliably perform the required task rather than introducing multi-agent complexity by default.
Turn a business workflow into an AI agent built for the job.
AI & ENGINEERING · DATA & IT · OPERATIONS · BUSINESS TEAMS
What can thaink² build as a custom AI agent?
We build agents around specific enterprise workflows rather than generic chatbot experiences. An agent can be designed to analyze data, retrieve internal knowledge, work with documents, interact with tools and APIs, generate structured outputs or coordinate several steps and specialized agents within a broader workflow.
How is a custom agent different from a standard chatbot?
A chatbot primarily answers messages. A custom enterprise agent is designed around a mission and can be given access to business context, data, knowledge and tools required to complete that mission. Its behavior, model, instructions, actions and orchestration are configured according to the workflow rather than around open-ended conversation alone.
Can you work with different AI models?
Yes. The underlying thaink²/aPowerB architecture supports multiple model providers rather than tying every agent to a single LLM. Model selection can therefore be adapted to requirements such as reasoning quality, latency, cost, context needs and the technical constraints of the application.
Can multiple agents work together?
Yes. When the workflow benefits from specialization, several agents can be orchestrated through sequential, parallel or routing patterns. For example, one agent can retrieve context, another analyze data and another generate the final output. We choose the simplest architecture that can reliably perform the required task rather than introducing multi-agent complexity by default.
Go deeper into enterprise AI agents.
Explore technical guides, design patterns and practical insights on building AI agents around real business workflows from model selection and RAG to tools, multi-agent orchestration, evaluation and agent architecture.
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.
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.
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.