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 the technology applies, what use cases to prioritize, with what technical prerequisites, and with what measurable success metric.
Applying AI with enterprise judgment is applying method before fashion
We identify where to apply models with measurable ROI, validate use cases with real data, and build the solutions 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.
16 years of enterprise engineering across regulated sectors in LATAM 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 judgment is 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
What we build in this service
Six intelligent capability fronts that can be executed independently or as an integrated protocol, according to your digital ecosystem's maturity and the use cases prioritized by measurable ROI.
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 identify use cases with measurable ROI, map available data architecture, and validate technical feasibility before committing to construction. Judgment is defined before the prototype.
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.
Universidad de La Sabana: finalist Acquia Awards 2026 in AI applied to learning
esinergia built the integration of Drupal AI Initiative modules on Universidad de La Sabana's platform submitted as finalist for Acquia Awards 2026 in the "Best Use of AI for Learning & Acceleration" category. Models applied with academic judgment and enterprise DXP architecture, not as a demonstration but as an operational asset in production on +30 months of continuous uptime.
Frequently asked questions
Direct answers to the most common questions before getting started.
We start by listening to your organization's operational context. Then we identify use cases prioritized by ROI and validated technically: RAG over your own content assets, conversational agents for user service, assisted generation in the editorial flow, experience personalization. Each case is validated with real data before committing to construction.
Depending 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 load for editorial teams, 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.
In practice
These cases require a named client with written permission before publishing. Current copy is placeholder. Do not implement with real names until signed documents are received. Leandro Olaya updates when available.
Applying AI well starts by asking where
