We start by listening to what problem you expect to solve and what information assets your organization has today. Then we'll honestly tell you whether this technology fits, what use cases to prioritize, with what technical prerequisites, and with what measurable success metric.
Applying AI with judgment is applying method before fashion
We identify where to apply models with verifiable ROI, validate use cases with real data, and build the capabilities the business can operate. The promise of "transformation with AI" isn't built with licenses, it's built with judgment over regulated sectors, governable data architecture, and verifiable method
Our engineering trajectory across regulated sectors in Latin America showed us that the difference between a technical capability and a business asset is the judgment to apply it. Models don't replace that judgment, they leverage it. And it's built on governable data architecture, not on license promises
The value of AI isn't in the model, it's in applying it with judgment
Applied AI that responds to every role
In an enterprise organization each role puts different priorities in play when applying AI. Our model responds to all three
Drupal AI Initiative modules operating in production over +30 months of continuous uptime
Finalist
Acquia Awards 2026, Best Use of AI for Learning and Acceleration with Universidad de La Sabana
What we build in this service
Six applied AI capabilities in three groups that can be executed independently or as an integrated protocol, according to your digital ecosystem's maturity and cases prioritized by verifiable ROI.
Why esinergia for your applied AI
We're not an agency with AI added at the end of the project. We're a technical partner that applies AI with neutral architectural judgment on the Drupal enterprise ecosystem, with continuous contribution to the global ecosystem, verifiable evidence in production, and capabilities validated internally before applying them to clients.
Credentials behind every delivery
Four verifiable reasons on how we apply AI with judgment.
How we work
Three lifecycle phases applied to the vertical service, because intelligent capabilities cross the full cycle: they're discovered, built, and evolve with the business.
01
Discover
We audit AI opportunities in your ecosystem with verifiable ROI, map available data architecture, and validate technical feasibility before committing to construction. Prioritized use cases + technical prerequisites + estimated effort + realistic implementation sequence.
02
Build
Prototype validated with real data, model chosen according to the case (pre-trained with RAG or specialized), integration with the existing digital ecosystem, and user testing before production.
03
Evolve
Response quality monitoring, model refinement with operational feedback, expansion of use cases, and continuous ROI measurement. Intelligent capability matures with operations, it's not delivered and forgotten.
Applied AI in production
Projects with technical and business evidence. No vanity metrics.
Frequently asked questions
Direct answers to the most common questions before getting started.
We start by listening to your operational context. Then we identify use cases prioritized by verifiable ROI and validated technically: RAG over your own content assets, conversational agents, assisted generation in the editorial flow, experience personalization. Each case is validated with real data before committing to construction.
Depends on the case. For RAG and semantic search: structured and governable content architecture, Drupal is the engine that sustains it best in regulated sectors. For conversational agents: integration with core systems. For personalization: consolidated data layer. We do the technical diagnosis in discovery, before committing scope.
With verifiable business metrics, not vanity model metrics. Average response time, user adoption rate, reduction in editorial team load, conversion increase, perceived response quality. The metric is defined before building, not after.
Yes. Most of our clients operate in regulated sectors (healthcare, government, education, financial services). We apply encryption, granular access controls, anonymization, and compliance with sector-specific frameworks, over the ISMS esinergia operates. Operational continuity is a contractual obligation in those sectors; so is sensitive data handling.
We combine both depending on the case. For most enterprise cases, pre-trained models with RAG over the client's own data deliver the best balance between implementation time and response quality. For specific cases with sufficient proprietary data and fine control needs, we build specialized models. We are Gold Sponsors of the Drupal AI Initiative with a dedicated developer and continuous contribution to the drupal.org project, verifiable in the global community.
No. We apply AI with neutral architectural judgment on the Drupal enterprise ecosystem. The technical route is chosen by the case, full control, balance between control and speed, or maximum SaaS speed. We choose with you the one that best serves the business, not by commercial default.
Applying AI well starts by asking where.
