AI platforms in production connected to your ERP, CRM, ITSM and M365 — on a data layer that is modeled, measured and governed. Not a lab pilot: systems running with real users, built by a team that runs its own company on agents.
What we hear in management committees — and how we solve it with AI in production: disconnected systems converging into a single assistant.
"We have 10+ disconnected systems (ERP, ITSM, CRM, M365) and our teams lose hours copying data between them"
We connect all your systems behind one AI assistant that queries, cross-references and automates across them — in plain language.
"The BI team can't keep up; executives are deciding on stale data"
Automated KPI reporting, real-time dashboards and predictive ML — all reachable from a conversation.
"Our helpdesk gets hundreds of repetitive tickets and resolution time is far too high"
Classification, routing and automatic resolution of routine tickets with AI — no human in the middle.
"The same KPI gives a different number depending on which area reports it, and nobody knows which one is right"
A single data model and semantic layer: one definition of customer, revenue and margin, with lineage showing where each figure came from.
"We want to deploy AI on our data, but it's duplicated, uncatalogued and has no clear owner"
Governance and quality first: catalog, golden records, rules monitored against SLAs, and named data owners. Only then does AI answer with data you can trust.
"The board is asking for an AI usage policy and we have nothing to answer with: no criteria, no owners, no risk controls"
An AI governance and ethics framework: usage policy, committee, risk classification per use case and defined roles — aligned to NIST AI RMF and ISO/IEC 42001.
"If an agent decides badly or the model discriminates, who answers for it, and how do we prove it in an audit?"
Explicit limits on what each agent may do, human-in-the-loop on sensitive decisions, bias evaluation and an auditable trail for every decision.
"We want to implement AI but we don't know where to start or how to justify the investment"
An AI roadmap with use cases prioritized by ROI. We start with an 8-week pilot with clear metrics.
A copilot that answers with inconsistent figures destroys trust faster than it builds it. That's why every platform we deploy rests on a data layer that is structured, measured and governed — our founder's specialty, with 18+ years leading analytics and data quality in telecommunications.
ETL/ELT and streaming from ERP, CRM, ITSM, M365 and legacy systems into your warehouse or lakehouse. Orchestration, reprocessing and end-to-end observability.
Data warehouse / lakehouse with a dimensional model and certified metrics: one definition of customer, product and revenue for the whole organization.
Profiling, validation rules, deduplication, golden records and continuous monitoring with data SLAs and alerts when things drift.
Catalog, lineage, business glossary, data owner and steward roles, access policies and compliance with Peru's Law 29733 on personal data protection.
The same data architecture, microservices, integrations and agents we keep in production for our clients — and for ourselves.
ent/01Data Architecture and ModelingData warehouse, lakehouse and dimensional models with a semantic layer on BigQuery, Databricks, Snowflake or Synapse.
ent/02Data Governance and QualityCatalog, lineage, data owners and stewards, access policies; profiling, rules, deduplication, MDM and SLA monitoring. The specialty of the house.
ent/03Pipelines and DataOpsETL/ELT, streaming, orchestration (Airflow, Dataflow) and observability with data CI/CD and MLOps.
ent/04AI Governance and Ethical FrameworkUsage policy, governance committee, risk classification per case, bias evaluation, human-in-the-loop and agent auditing. Aligned to NIST AI RMF and ISO/IEC 42001.
ent/05Cultural Transformation and Change ManagementResistance mapping, executive sponsorship, a champion network, role-based upskilling and measured adoption. With human-agent working agreements.
ent/06Enterprise AI PlatformsMicroservice architecture with multimodal chat, voice, video, RAG and ML analytics in production.
ent/07Enterprise IntegrationsERP (Odoo, SAP), ITSM (Freshservice), CRM (Salesforce), M365, WhatsApp Business.
ent/08Agentic AIAutonomous agents that carry out complex tasks and decide in real time on data you can trust.
ent/09Agentic AutomationOperational processes run by AI agents: sales, onboarding, reporting.
ent/10Advanced Analytics and VisualizationPredictive ML and dashboards in Power BI, Looker and Tableau on certified metrics — not on loose extracts.
ent/11Cloud ComputingGCP, Azure and AWS sized for data and AI workloads with costs under control.
ent/12Data and AI ConsultingA data maturity assessment, a roadmap prioritized by ROI, and support in management committees.
IoT sensors, multispectral drones and computer vision over your crops and shrimp ponds. Ask about any plot in plain language and decide on data — GlobalGAP, BAP, ASC and SENASA compliance automated.
agro/01IoT sensorsLoRaWAN network: humidity, temperature, pH, dissolved oxygen, salinity. Data in real time.
agro/02Agricultural dronesMultispectral mapping (NDVI, RGB, thermal): plant stress, fruit counting, pond inspection.
agro/03Computer VisionVertex AI / Gemini Vision: pest detection, yield estimation, biomass counting.
agro/04AI reportingLooker/Power BI dashboards plus AI-generated reports with automated compliance.
agro/05Conversational agentAsk about any lot, plot or pond. Agronomic decisions grounded in data.
agro/06Measurable resultsLess water, more yield, and health problems caught before they escalate.
You can deploy the best platform and find out six months later that the teams went back to their spreadsheets and the licenses are asleep. Technological transformation without cultural transformation is spending, not investment. That's why every program we lead includes the work with people, led by Irene Pastor, our Partner for Cultural Transformation and AI Adoption.
Who wins, who loses and who decides in each area. Resistance can be managed once it's known; by the time it surfaces in committee it's late and it's expensive.
Visible executive sponsorship and a go-to person per area with a mandate and time assigned — not willing volunteers with a full calendar.
Different paths for leadership, middle management and operating teams. The board needs judgment to decide; the analyst needs hands on the tool.
Indicators of real usage and process impact, not of licenses purchased. An adoption committee reviewing quarterly, able to change course.
In-company programs taught by active practitioners: we teach what we use to run an agentic company.
Fundamentals, professional prompt engineering and productive use of ChatGPT, Claude and Gemini. Cases by area: marketing, sales, operations, HR.
Strategy, ROI cases and AI governance for management committees and boards. From theory to an actionable roadmap in one session.
Describe your stack and your processes to the enterprise assistant, or book 30 minutes with César. The first session costs nothing.
There are also demos for legal —contracts, comparison and risks— and for procurement —quotes, suppliers and RFPs.