Most enterprises do not need another system. They need the data inside the systems they already run to be trustworthy, connected and finally useful. We build the data and AI layer on top of SAP, Oracle and the rest of your estate — governance and pipelines first, then analytics, models and agents.
From governance and data quality through to production AI — delivered by senior engineers on your business hours.
Policies, roles, ownership and a data catalog that people actually use. Privacy and regulatory compliance, access control, lineage and traceability — so the business can trust what it is looking at and defend how it got there.
Profiling, cleansing and match-merge for customer, vendor, product and material master data. Quality rules with monitoring attached, so records stay clean after the project ends rather than degrading back to where they started.
Target-state data architecture and migration to a modern cloud platform — lakehouse, warehouse or hybrid — on AWS, Azure, GCP or Databricks. Designed for what the business will need next, not just what it reports on today.
Extraction from SAP and Oracle into a platform your analysts can reach, plus the connectors and custom extractors that commercial tools do not cover. Built with error handling, monitoring and documentation the next team can maintain.
Executive and operational dashboards on Power BI, Tableau and SAP Analytics Cloud. Semantic models and definitions agreed once, so finance and operations stop arguing about whose number is right.
Demand, revenue and inventory forecasting; churn and propensity models; recommender systems. Measured against a documented baseline, so the lift is a number rather than an assertion.
Retrieval-augmented copilots over ERP transactions, contracts and documentation. Agentic workflows for the repetitive work — invoice and purchase-order matching, vendor-master cleansing, document classification and exception handling.
Automation of the manual steps between systems, and bespoke development where the packaged product stops and your actual business process begins. Scoped to what earns its keep, not automation for its own sake.
Enablement, role-based training and change management. The most common reason a data programme underdelivers is not the technology — it is that nobody changed how they make decisions.
We are not reselling a platform, so we have no reason to push one. The right architecture depends on what you already own, what your team can operate and what the workload actually demands. We work fluently across the major enterprise and cloud stacks, and we will tell you when the simpler option is the better one.
Four ways in, depending on how well defined the problem is when you call.
Delivery teams across Latin America working US Eastern and Central hours, bilingual in English and Spanish, contracted in USD through our New York entity. Real-time collaboration and one point of accountability — without the travel premium or the twelve-hour handoff lag.
No. We work on top of the ERP and the integrator you already have. Our scope is the data and AI layer above the transactional system — governance, pipelines, analytics, models and agents. That keeps us additive rather than competing with an incumbent you are happy with.
SAP and Oracle on the ERP side; AWS, Azure, GCP and Databricks for cloud and data platforms; Power BI, Tableau and SAP Analytics Cloud for reporting; and the major commercial and open model providers for AI workloads. We are deliberately platform-neutral above the ERP.
Most start with a short diagnostic — we map the data estate, name the gaps and rank the use cases by value and feasibility. You keep that roadmap whether or not the work continues with us. From there, work is typically delivered as a scoped project or a dedicated pod.
Delivery is nearshore in Latin America, on US Eastern and Central business hours, with bilingual English and Spanish engineers. Contracting, account leadership and escalation are handled by our US entity in New York, in USD.
Senior. You meet the people who will do the work before anything is signed, and the names in the statement of work are the names on the calls.
Thirty minutes to map what you already run and where the fastest win is. If it is not a fit, we will say so.