AI for Data
By connecting LLMs to both operational systems and analytical platforms, Reply enables conversational data exploration and optimises data governance, accelerating time-to-insight across the enterprise.
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Data for an AI World
Data for an AI World
From traditional analytics to conversational business intelligence
Enterprise data architectures have traditionally relied on centralised data lakes and enterprise data warehouses, imposing a structural dependency on business users through static dashboards, predefined filters and periodic reports. This model creates a significant operational bottleneck between data generation and the extraction of actionable business intelligence, slowing decision-making and reducing the ability to respond promptly to market changes.
Reply's architectural approach addresses this friction by introducing an AI-driven interaction layer that overlays existing data ecosystems, shifting the enterprise from a report-centric model to a conversational, question-driven approach. Generative AI acts as an interaction layer on top of enterprise analytical infrastructures, enabling business users to query data in natural language and obtain dynamic, contextualised insights, making data access more direct, self-service and less dependent on technical teams.
The role of the Model Context Protocol
The Model Context Protocol (MCP) establishes a standardised and secure integration layer between large language models and underlying enterprise data environments. Instead of building bespoke data ingestion pipelines or moving large volumes of transactional data into a central analytical repository, data engineering teams can deploy protocol servers to expose specific operational data and metadata directly to AI agents.
This infrastructure facilitates a "validate first, industrialise later" methodology for data architecture. Users can query live legacy systems to test business hypotheses and explore emerging trends. If a specific analytical use case demonstrates clear and measurable value, the organisation can then justify the capital expenditure required to integrate that data flow into the permanent enterprise data warehouse.
An organisation can implement dual protocol connections. One connection links to the central data platform to retrieve aggregated historical data. The other connection engages the legacy transactional system to retrieve real-time, highly granular records. This architecture enables the AI agent to seamlessly cross-reference broad historical trends with the most up-to-date operational insights.
Furthermore, the protocol facilitates the creation of governed conversational spaces. In these secure analytical environments, the data scope is rigorously curated. Data architects can explicitly define fact-to-dimension logic, expected join paths and filtering rules for active records. This semantic framing ensures that the AI uses only reliable, analysis-ready data to formulate responses.
Agile data governance and architecture optimisation
Chief Information Officers constantly face the challenge of managing data infrastructure costs whilst meeting a growing backlog of enterprise reporting requests. Implementing direct AI connections on legacy systems enables data departments to rapidly deliver lightweight analytical capabilities. This approach significantly reduces the structural effort associated with traditional business intelligence delivery.
The integration provides value in technical data governance and data lifecycle automation. By pointing the protocol at a central metadata platform, the AI can read the entire enterprise data catalogue, including the lineage of extraction, transformation and loading (ETL) processes. Data architects can perform natural language impact analyses to understand how modifying a specific database table or metric logic will affect downstream reporting layers.
AI plays a direct operational role in data management. Practical applications include data catalogue enrichment, automatic metadata generation, semantic mapping across heterogeneous data sources, structured dataset documentation, query optimisation, data quality control and support for semantic model maintenance. These capabilities reduce the manual workload on data engineering teams and improve consistency across the entire enterprise data ecosystem.
In addition, IT departments can deploy specialised AI agents to accelerate data project delivery:
Functional data analyst
These AI assistants help gather business requirements for new data models by generating targeted questions based on internal data governance guidelines. This capability facilitates the rapid onboarding of junior data analysts and improves the quality of data dictionaries.Technical BI agent
These tools automatically translate enterprise reporting requirements into data pipeline logic and semantic rules. They connect directly to semantic models during the data modelling phase to identify duplicate records, highlight underperforming analytical measures and resolve complex relationship issues within the data architecture. This reduces data troubleshooting activities from days to hours.
Use cases from Reply's experience
Marketing and sales
Natural language BI access
In organisations where business intelligence reporting routinely requires users to navigate dashboards, apply filters and request analyst support for non-standard queries, natural language-mediated access changes the interaction model. Users formulate questions in business language and the system returns governed, auditable answers drawn from certified data sources.Generative BI for retail and e-commerce
In the retail and e-commerce sectors, generative BI applications enable commercial managers to query data marts securely, instantly retrieving customer segmentation charts and sales performance metrics without writing SQL."Push" model with AI agents
AI agents connected to enterprise data can be configured to analyse specific datasets on a defined schedule, detect anomalies or performance deviations from expected ranges or historical benchmarks, and deliver structured summaries to the relevant stakeholders. This "push" model is particularly applicable for recurring operational monitoring: weekly performance summaries, KPI deviation alerts, cross-market anomaly detection and root-cause analysis on known performance issues. If a severe anomaly is detected, the agent can autonomously trigger alerts to third-party operational systems.Decision intelligence layer
The combination of both models, the conversational "pull" query and the automated "push" analysis, constitutes a "decision intelligence layer": a persistent AI capability that responds to user questions and proactively surfaces information relevant to operational and strategic decisions.
Finance and corporate governance
Financial governance and KPI harmonisation
Financial and administrative functions require rigorous data governance, absolute numerical accuracy and harmonised reporting. Multinational organisations frequently contend with fragmented calculation logic, where KPIs are defined differently across local databases.Verification and comparison of calculation rules
This protocol-driven architecture enables the AI to verify and compare divergent calculation rules across countries. Financial controllers can query the system to understand exactly how a specific metric is calculated in different local data warehouses, automatically merge the documentation and select a unified master calculation rule.Automatic metadata enrichment
The AI can automatically enrich the metadata descriptions of each KPI based on the underlying table structures, providing agents with deep semantic context without requiring manual data entry from the data stewardship team.Building enterprise ontologies
This standardisation forms the foundation for building robust enterprise ontologies, representing a new approach to master data management. A well-defined ontology ensures that business terms maintain a single, consistent definition across all systems. The status of a physical asset, for example, must have exactly the same meaning for the supply chain analytics team and the financial audit team.Corporate governance and access control
The protocol layer is also essential for keeping corporate governance under control. Multi-agent assistants act as secure data custodians, analysing enterprise databases and internal documentation comprehensively. The protocol intentionally restricts AI access to approved tables and dedicated database views only. This rigorous access control ensures that all extracted financial information is auditable, secure and compliant with corporate reporting standards.
Challenges and lessons from real-world implementations
Whilst an MCP-based approach accelerates time-to-insight, Reply's experience in implementing these architectures has highlighted several key factors that require careful management.
Protecting operational systems
The volume of queries directed at live operational systems must be governed to avoid degrading source performance. Legacy systems are natively designed for transactional workloads, which means that complex analytical aggregations can encounter severe performance limitations if not managed correctly.Dependency on upstream data
Generative AI does not replace data quality. Response quality remains entirely dependent on the accuracy and completeness of upstream data.
Explicit query governance
Whilst the protocol enables secure connections, the governance policies themselves (defining exactly which agents can access which data) must be explicitly configured as a fundamental part of the protocol server setup to prevent an indiscriminate access model.The semantic layer as the key element
Simply exposing raw tables is rarely sufficient. The semantic layer represents the key element for ensuring that business terms are interpreted correctly and consistently across different departments. Through the definition of a robust ontology and explicit semantic rules, the semantic layer eliminates terminological ambiguities, ensures shared KPI definitions, improves the accuracy of AI-generated responses and reduces the risk of misinterpretation by language models.
The value of Generative AI applied to enterprise data
Direct integration of large language models decouples analytical capability from data platform completion. Reply supports enterprises across various sectors in implementing MCP layers on both legacy operational systems and analytical repositories, enabling the construction of a dual-speed data strategy. Organisations can thus obtain insights more rapidly, identify hidden profitability drivers and drastically reduce the difficulties associated with delivering data projects.
Frequently asked questions
Target Reply is the company within the Reply group specializing in advanced solutions for Analytics, Big Data, Data Science, Cloud Architectures, and Artificial Intelligence. Target Reply supports companies in the process from identifying needs to designing and implementing data integration solutions, data modeling, predictive and prescriptive analytics, using the most innovative tools and approaches in the fields of Fast Data and Machine Learning. Target Reply has gained significant experience with leading Italian and international companies and is capable of operating competently across all major markets (Telco, Finance, Manufacturing, etc.).
Cluster Reply is the Reply group's company specialising in consulting and system integration services on Microsoft technologies. Focused on innovation, Cluster Reply has always offered on-premise and cloud-based Microsoft solutions in areas such as Modern Workplace, Business Applications, Applications and Infrastructure, Data and AI. With over 24 years of collaboration with Microsoft in Italy, Cluster Reply helps its clients choose the best Microsoft solutions quickly and effectively, bringing certified Microsoft expertise and a distinctive market knowledge to its work.
Data Reply is the Reply Group company specialized in Big Data Analytics and Artificial Intelligence. Through multidisciplinary teams of specialists in Big Data Engineering, Data Science and Intelligent Process Automation, Data Reply supports companies from different industries in process optimization through the design and implementation of solutions, quantum algorithms and Machine Learning and Artificial Intelligence models.
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