Integration & MLOps
Move from a working AI use case to a production system that can be deployed, connected and operated over time.
Integration & MLOps
Move from a working AI use case to a production system that can be deployed, connected and operated over time.
Connect. Deploy. Operate.
Connect. Deploy. Operate.
Moving an AI system into production means connecting it to the enterprise environment, deploying it on the right infrastructure and making its behavior observable over time. We build the operational layer required to turn a working prototype into a system teams can rely on.
Mode proactif
Connect the agent to the environment where the work already happens.
We integrate AI workflows with the data sources, APIs, applications, files and collaboration systems they need to operate. The goal is not to create another isolated AI interface, but to make the agent part of the existing enterprise environment. We define the right integration method for each source, handle the required access layer and make sure the workflow receives the context it needs at the right moment.
Databases · APIs · Enterprise apps · Files · Authentication
Mode proactif
Connect the agent to the environment where the work already happens.
We integrate AI workflows with the data sources, APIs, applications, files and collaboration systems they need to operate. The goal is not to create another isolated AI interface, but to make the agent part of the existing enterprise environment. We define the right integration method for each source, handle the required access layer and make sure the workflow receives the context it needs at the right moment.
Databases · APIs · Enterprise apps · Files · Authentication
Mode proactif
Create the technical environment the agent needs to run consistently.
Behind every production agent sits a runtime: services, storage, databases, credentials, configuration and the components that execute the workflow. We structure that environment so the application is not dependent on a developer’s local setup or a one-off prototype. This provides a more stable foundation for deployments, integrations, background execution and future changes to the agentic system.
Runtime · Services · Storage · Configuration · Secrets
Mode proactif
Create the technical environment the agent needs to run consistently.
Behind every production agent sits a runtime: services, storage, databases, credentials, configuration and the components that execute the workflow. We structure that environment so the application is not dependent on a developer’s local setup or a one-off prototype. This provides a more stable foundation for deployments, integrations, background execution and future changes to the agentic system.
Runtime · Services · Storage · Configuration · Secrets
Mode proactif
Deploy according to your infrastructure and control requirements.
We help move the application into an environment appropriate for production, whether that means a managed cloud setup, containerized deployment or infrastructure controlled by your own teams. The underlying aPowerB ecosystem supports deployment patterns including Docker, Kubernetes and Helm, allowing the operating model to follow the organization’s security, scalability and infrastructure requirements rather than forcing every customer into the same hosting model.
Cloud · Containers · Kubernetes · Helm · Self-hosting
Mode proactif
Deploy according to your infrastructure and control requirements.
We help move the application into an environment appropriate for production, whether that means a managed cloud setup, containerized deployment or infrastructure controlled by your own teams. The underlying aPowerB ecosystem supports deployment patterns including Docker, Kubernetes and Helm, allowing the operating model to follow the organization’s security, scalability and infrastructure requirements rather than forcing every customer into the same hosting model.
Cloud · Containers · Kubernetes · Helm · Self-hosting
Automate Execution
Turn an agent from something users call manually into a workflow that can run by itself.
Production use cases often need to react to business events, incoming information or recurring schedules. We configure the execution logic required to trigger agents and workflows automatically through schedules, webhooks, file events, messages or defined business conditions. This is what turns an AI capability into an operational process rather than another tool employees need to remember to open.
Schedules · Webhooks · Events · Conditions · Workflows
Automate Execution
Turn an agent from something users call manually into a workflow that can run by itself.
Production use cases often need to react to business events, incoming information or recurring schedules. We configure the execution logic required to trigger agents and workflows automatically through schedules, webhooks, file events, messages or defined business conditions. This is what turns an AI capability into an operational process rather than another tool employees need to remember to open.
Schedules · Webhooks · Events · Conditions · Workflows
Observe & Diagnose
Know what happened when an agent runs, especially when something goes wrong.
Once agents operate continuously, teams need visibility into their executions. We put the monitoring layer in place so runs, sessions, tool calls, errors and operational events can be inspected rather than treated as a black box. This gives technical teams the information required to troubleshoot failures, understand agent behavior and follow what the system actually did during a production workflow.
Runs · Sessions · Tool calls · Errors · Logs
Observe & Diagnose
Know what happened when an agent runs, especially when something goes wrong.
Once agents operate continuously, teams need visibility into their executions. We put the monitoring layer in place so runs, sessions, tool calls, errors and operational events can be inspected rather than treated as a black box. This gives technical teams the information required to troubleshoot failures, understand agent behavior and follow what the system actually did during a production workflow.
Runs · Sessions · Tool calls · Errors · Logs
Evaluate & Improve
Production is the beginning of the feedback loop, not the end of the project.
Agent performance needs to be reviewed over time as data changes, workflows evolve and models or integrations are updated. We help establish the operational feedback needed to assess behavior, usage, quality and cost, identify recurring weaknesses and decide where the system should be improved. The goal is to make the deployed agent maintainable and observable instead of freezing the first working version indefinitely.
Quality · Usage · Cost · Evaluation · Continuous improvement
Evaluate & Improve
Production is the beginning of the feedback loop, not the end of the project.
Agent performance needs to be reviewed over time as data changes, workflows evolve and models or integrations are updated. We help establish the operational feedback needed to assess behavior, usage, quality and cost, identify recurring weaknesses and decide where the system should be improved. The goal is to make the deployed agent maintainable and observable instead of freezing the first working version indefinitely.
Quality · Usage · Cost · Evaluation · Continuous improvement
Move your AI system from prototype to production.
DATA & IT · AI & ENGINEERING · PLATFORM TEAMS · OPERATIONS
Can thaink² integrate with our existing enterprise systems?
Yes. AI workflows can be connected to the systems and information they require, including databases, APIs, files, enterprise applications and collaboration tools. The integration architecture is defined around the actual workflow so that the agent becomes part of the existing environment instead of operating as an isolated AI application.
Can thaink² be deployed on our own infrastructure?
Yes. The underlying platform supports several deployment models, including containerized and self-hosted environments. Depending on your infrastructure requirements, deployments can be designed around technologies such as Docker, Kubernetes and Helm as well as supported cloud environments.
What can be monitored once an agent is in production?
Production operations require visibility into what agents actually do. Runs, sessions, tool calls, errors, logs and other execution information can be inspected to understand behavior and diagnose failures. The wider thaink² platform also provides operational capabilities around usage, monitoring and evaluation.
Can you help productionize an AI prototype we already have?
Yes. You do not need to start from scratch. We can assess an existing prototype, identify what is missing for production, connect it to the required systems, define the runtime and deployment architecture and add the operational controls needed for recurring use. The objective is to preserve what is already useful while addressing the gaps between a working demonstration and a maintainable production system.
Move your AI system from prototype to production.
DATA & IT · AI & ENGINEERING · PLATFORM TEAMS · OPERATIONS
Can thaink² integrate with our existing enterprise systems?
Yes. AI workflows can be connected to the systems and information they require, including databases, APIs, files, enterprise applications and collaboration tools. The integration architecture is defined around the actual workflow so that the agent becomes part of the existing environment instead of operating as an isolated AI application.
Can thaink² be deployed on our own infrastructure?
Yes. The underlying platform supports several deployment models, including containerized and self-hosted environments. Depending on your infrastructure requirements, deployments can be designed around technologies such as Docker, Kubernetes and Helm as well as supported cloud environments.
What can be monitored once an agent is in production?
Production operations require visibility into what agents actually do. Runs, sessions, tool calls, errors, logs and other execution information can be inspected to understand behavior and diagnose failures. The wider thaink² platform also provides operational capabilities around usage, monitoring and evaluation.
Can you help productionize an AI prototype we already have?
Yes. You do not need to start from scratch. We can assess an existing prototype, identify what is missing for production, connect it to the required systems, define the runtime and deployment architecture and add the operational controls needed for recurring use. The objective is to preserve what is already useful while addressing the gaps between a working demonstration and a maintainable production system.
Ressources pour aller plus loin
Guides, benchmarks, retours terrain et analyses pour comprendre comment les agents IA transforment l’accès, l’analyse et l’exploitation des données en entreprise.
Et si votre prochaine analyse était déjà prête avant votre prochaine réunion ?
Montrez-nous votre environnement et un cas d’usage. Découvrez comment thaink² peut interroger, surveiller et exploiter vos données.
Qu’est-ce qu’une Agentic Data Platform ?
Une Agentic Data Platform utilise des agents IA spécialisés pour travailler sur les données de l’entreprise. Contrairement à un dashboard statique ou à un simple chatbot, les agents peuvent explorer plusieurs sources, conduire une analyse, produire des visualisations, générer des livrables et exécuter des missions récurrentes.
Quelle différence entre le mode Proactif et le mode Exploratoire ?
En mode Exploratoire, l’utilisateur pose une question et thaink² mène l’analyse à la demande. En mode Proactif, une mission est définie à l’avance : les agents surveillent les données selon la fréquence ou les conditions prévues et livrent automatiquement les résultats utiles.
Faut-il connaître SQL pour utiliser thaink² ?
Non pour les usages métier. Les utilisateurs peuvent poser leurs questions en langage naturel et obtenir analyses, tableaux, graphiques et dashboards sans écrire eux-mêmes leurs requêtes SQL.
À quelles sources de données thaink² peut-il se connecter ?
La plateforme est conçue pour travailler avec les sources déjà présentes dans l’entreprise : bases de données, Data Warehouses, ERP, CRM, API, fichiers et espaces documentaires. Les connecteurs disponibles dépendent de votre environnement et du cas d’usage.
Quels types de résultats les agents peuvent-ils produire ?
Selon l’agent et le cas d’usage : analyses, tableaux, graphiques, dashboards, prévisions, alertes, synthèses et rapports. L’objectif est de restituer un résultat exploitable, pas uniquement une réponse textuelle.
Peut-on déployer thaink² sur notre propre infrastructure ?
thaink² propose des scénarios de déploiement adaptés aux contraintes d’entreprise, notamment pour les organisations qui souhaitent conserver davantage de contrôle sur leur infrastructure, leurs modèles et leurs données. Nos équipes définissent l’architecture adaptée au projet.
Quelle partie de l’écosystème thaink² est 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.
Comment démarrer avec thaink² ?
Comment démarrer avec thaink² ?
Rejoins notre communauté Discord
Connect with builders, Data teams, and AI practitioners. Share ideas, get technical help, discuss agentic architectures, and follow the latest developments around ApowerB.
Bientôt
Des données qui agissent, directement dans votre boîte de réception.
Retours d'expérience, architectures d'agents, benchmarks, cas d'usage et nouveautés APowerB. Uniquement ce qui mérite d'être lu.
Et si votre prochaine analyse était déjà prête avant votre prochaine réunion ?
Montrez-nous votre environnement et un cas d’usage. Découvrez comment thaink² peut interroger, surveiller et exploiter vos données.
Qu’est-ce qu’une Agentic Data Platform ?
Une Agentic Data Platform utilise des agents IA spécialisés pour travailler sur les données de l’entreprise. Contrairement à un dashboard statique ou à un simple chatbot, les agents peuvent explorer plusieurs sources, conduire une analyse, produire des visualisations, générer des livrables et exécuter des missions récurrentes.
Quelle différence entre le mode Proactif et le mode Exploratoire ?
En mode Exploratoire, l’utilisateur pose une question et thaink² mène l’analyse à la demande. En mode Proactif, une mission est définie à l’avance : les agents surveillent les données selon la fréquence ou les conditions prévues et livrent automatiquement les résultats utiles.
Faut-il connaître SQL pour utiliser thaink² ?
Non pour les usages métier. Les utilisateurs peuvent poser leurs questions en langage naturel et obtenir analyses, tableaux, graphiques et dashboards sans écrire eux-mêmes leurs requêtes SQL.
À quelles sources de données thaink² peut-il se connecter ?
La plateforme est conçue pour travailler avec les sources déjà présentes dans l’entreprise : bases de données, Data Warehouses, ERP, CRM, API, fichiers et espaces documentaires. Les connecteurs disponibles dépendent de votre environnement et du cas d’usage.
Quels types de résultats les agents peuvent-ils produire ?
Selon l’agent et le cas d’usage : analyses, tableaux, graphiques, dashboards, prévisions, alertes, synthèses et rapports. L’objectif est de restituer un résultat exploitable, pas uniquement une réponse textuelle.
Peut-on déployer thaink² sur notre propre infrastructure ?
thaink² propose des scénarios de déploiement adaptés aux contraintes d’entreprise, notamment pour les organisations qui souhaitent conserver davantage de contrôle sur leur infrastructure, leurs modèles et leurs données. Nos équipes définissent l’architecture adaptée au projet.
Quelle partie de l’écosystème thaink² est 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.
Comment démarrer avec thaink² ?
Comment démarrer avec thaink² ?
Rejoins notre communauté Discord
Connect with builders, Data teams, and AI practitioners. Share ideas, get technical help, discuss agentic architectures, and follow the latest developments around aPowerB.
Bientôt
Des données qui agissent, directement dans votre boîte de réception.
Retours d'expérience, architectures d'agents, benchmarks, cas d'usage et nouveautés APowerB. Uniquement ce qui mérite d'être lu.