We start by understanding technical feasibility, required integrations, and expected ROI, with honesty about what makes sense to do now and what's worth postponing.
Contributing to the ecosystem is different from consuming it
Most integrators apply AI modules on Drupal without understanding how they were built or where they're headed. At esinergia we have a full-time contributor dedicated exclusively to the Drupal AI Initiative — the official program that defines AI integration standards in the ecosystem. When we implement semantic search, agents, or automation on Drupal, we do it from inside the standard. The client gets implementations that evolve with the ecosystem, not ones that become orphaned in the next major version.
Verifiable authority in the Drupal and Acquia ecosystem
Verifiable credentials from the global Acquia program and the Drupal community.
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What we build with AI on Drupal
Technical capabilities with real evidence — implemented on active enterprise platforms, not in lab demos.
Semantic search on proprietary content
RAG architectures that allow users to find precise information within the organization's knowledge repository — without exposing data to public models. Implemented on educational and healthcare platforms with thousands of indexed documents.
Agents integrated into the editorial workflow
Automation of repetitive editorial tasks: content classification, metadata generation, duplicate detection, and structure suggestions. The editorial team operates faster without changing their workflow.
Content personalization by profile
Dynamic content recommendation based on user behavior, profile, and context. Implementable on the existing Drupal platform without replacing the content architecture.
Support assistants on knowledge base
Agents that answer frequently asked questions, guide self-service flows, and escalate to humans when the inquiry requires it — integrated directly into the client's Drupal portal.
AI-augmented development pipelines
Assistants integrated into esinergia's development cycle: code review, test generation, early vulnerability detection. The client receives more deliverables per sprint with the same team.
Integration with private and corporate models
Connection with OpenAI, Azure OpenAI, local models (Llama, Mistral), and client proprietary models under architectures that guarantee the organization's data is not used to train external models.
How we apply AI in Drupal projects
Six types of engagement for organizations that want to incorporate AI into their Drupal platforms with real technical judgment.
AI use case discovery and strategy
Identification of the AI use cases with the highest ROI in your Drupal platform. Technical roadmap with validated priorities and proposed architecture before the first sprint.
AI-Ready modernization
Refactoring of legacy Drupal platforms toward architectures ready to support AI workloads — without stopping operations or accumulating new technical debt.1
AI adoption audit
Assessment of the current state of the Drupal platform against the technical requirements to incorporate AI — with gap diagnosis and prioritized action plan.
Frequently asked questions about AI in Drupal
What CTOs, CIOs, and technical leaders ask us when evaluating AI incorporation into their Drupal platforms.
We design architectures with data isolation from the start — private models, enterprise instances from recognized providers, or local models deployed on the client's infrastructure. The organization's data is not used to train external models. Usage policies are documented and form part of contractual obligations, especially in regulated sectors like healthcare and education.
It depends on the version and technical state of the platform. On Drupal 10 and 11 most capabilities are implementable on the existing architecture. On earlier versions we evaluate whether a parallel AI microservices layer is the right approach or whether progressive modernization is the correct path. We determine this in the initial session before the first sprint.
A generic chatbot responds with the knowledge of the base language model — which may be incorrect, outdated, or irrelevant to the organization's context. A RAG architecture connects the model to the organization's own content repository, indexed and updated in real time. Responses come from the client's verified knowledge, not from the internet. The difference in accuracy and reliability is significant for enterprise environments.
The Drupal AI Initiative program defines AI integration standards in the official Drupal ecosystem. Actively contributing with a dedicated FTE means we know the technical roadmap before it's public, that implementations follow the official standard, and that they don't become orphaned in the next major version. We're not consumers of the ecosystem — we're part of who builds it.
Do you have an AI use case for your Drupal platform and don't know where to start?
