Descripción del servicio

Este servicio está diseñado para organizaciones que buscan resultados prácticos, como una mayor claridad en el seguimiento del rendimiento, un acceso más rápido al conocimiento interno y métodos fiables para analizar los mercados. Se hace hincapié en la calidad de la implementación, la documentación y la adopción, de modo que la solución pueda utilizarse con confianza tras su entrega.

1) Implementación de herramientas de análisis

Implementamos una base analítica que sirve de apoyo para la elaboración de informes y la toma de decisiones. Esto incluye definir métricas, armonizar las definiciones de los datos entre los distintos equipos, diseñar estructuras de información y establecer controles de calidad y documentación para que las cifras se mantengan coherentes a lo largo del tiempo.

Entre los resultados habituales se incluyen una guía de métricas y definiciones, una estructura de presentación de informes, paneles de control e informes de referencia, controles de calidad de los datos y un calendario operativo práctico para la revisión y el mantenimiento.

2) RAG-GRAPH: facilitación del conocimiento

Implementamos patrones de conocimiento basados en la recuperación de información y en grafos para facilitar la búsqueda y la reutilización de la información interna. Esta iniciativa ayuda a los equipos a reducir el tiempo dedicado a la búsqueda, a evitar la duplicación de esfuerzos y a mejorar la coherencia a la hora de responder a preguntas que abarcan distintos documentos y registros operativos.

Entre los resultados habituales se incluyen un inventario de contenidos y fuentes, un modelo de entidades y relaciones para los ámbitos clave, el ajuste y la evaluación de la recuperación de información, normas de acceso según el rol y directrices para un uso seguro.

3) AI Agents for Business Workflows

We design and implement AI agents that support specific workflows, such as summarizing, drafting, categorizing, routing, preparing briefs, or checking items against internal rules. The focus is on predictable behavior, clear inputs and outputs, and a review process that keeps accountability with the team.

Typical outputs include agent specifications, prompt and evaluation patterns, escalation rules, monitoring and logging guidelines, and training for owners and users.

4) Market Intelligence Enablement

We implement a repeatable approach to collecting and analyzing market signals, such as competitor changes, opportunity signals, customer feedback themes, pricing references, and regulatory updates. The objective is to create a maintainable internal system, not one time research.

Typical outputs include a tagging taxonomy, review cadences, alert and summary formats, and a knowledge base structure that supports sales and leadership.

Use Cases

• Weekly and monthly executive performance reviews.
• Sales reporting that ties activity and pipeline to measurable outcomes.
• Internal knowledge assistants for operations, delivery, and customer success.
• Opportunity and competitor monitoring with structured summaries and change logs.
• Automated preparation of briefs, proposals, and internal updates.

Working Approach

Discovery and Alignment

We start by clarifying the decisions that need better support, the workflows that need less friction, and the constraints that matter most, such as data quality, access control, and compliance. We then define a realistic scope and prioritize the highest value use cases.

Implementation and Configuration

We implement in increments, validate early with stakeholders, and document as we go. The goal is to avoid fragile setups, and to produce a system that is understandable, maintainable, and aligned with how the team works.

Enablement and Handover

We deliver training and practical playbooks for owners, admins, analysts, and business users. We also define ownership and a backlog for iteration, so the work continues smoothly after the engagement.

Overview

This service delivers an implemented analytics and AI capability set, plus the operating model needed to run it. It combines technical setup with process design and training, so teams can rely on the outputs in routine decision making.

Common outcomes include clearer metrics and reporting, faster knowledge retrieval, AI supported workflows with review controls, and a structured method to capture and summarize market signals.

Modelo de entrega

Timelines vary depending on scope, readiness of data and documentation, and stakeholder availability. Below is a typical delivery model in phases, each phase can be adjusted to fit priorities.

1. Phase 1, Assessment and roadmap, 1 to 2 weeks
   • Stakeholder interviews and use case definition
   • Inventory of data sources and knowledge repositories
   • Current state review for reporting, access control, and data quality
   • Target state outline and prioritized backlog
2. Phase 2, Analytics foundation setup, 2 to 4 weeks
   • Metric definitions and ownership
   • Data modeling approach and reporting structure
   • Baseline dashboards and reports
   • Documentation and data quality checks baseline
3. Phase 3, RAG-GRAPH knowledge implementation, 3 to 6 weeks
   • Content preparation and source mapping
   • Entity and relationship model for key domains
   • Retrieval tuning, evaluation, and access rules
   • User experience patterns for search and answers
4. Phase 4, AI Agents implementation, 3 to 6 weeks
   • Agent selection and workflow design
   • Guardrails, review steps, and escalation rules
   • Evaluation criteria and quality checks
   • Monitoring and improvement loop
5. Phase 5, Market Intelligence operating model, 2 to 4 weeks
   • Signal taxonomy and tagging rules
   • Collection and review cadence
   • Alerting and summary templates
   • Knowledge base structure and ownership
6. Phase 6, Enablement and handover, 1 to 2 weeks
   • Training sessions and role based onboarding
   • Playbooks and documentation handover
   • Backlog for next iterations and support plan