Conversational Analytics
We’re implementing an intelligent conversational layer for your data warehouse. Ask business questions in natural language and get answers in seconds.
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We empower leaders:
Key benefits of implementing conversational analytics
Traditional BI reporting often becomes a bottleneck. We’re changing the game by putting analytics in the hands of the people who make day-to-day decisions, rather than just a handful of experts.
The Democratization of Data
We’re breaking down the technological barrier. Every employee from sales reps to executives—can independently draw insights from the data warehouse without needing to know SQL or use BI tools. No more waiting for an analyst’s help.
Real-time decisions
Time is money. Instead of waiting days for a report from IT, you get an accurate answer the moment you ask the question. Respond to market changes immediately, before your competitors do.
Contextual understanding
Our systems understand industry jargon and the unique definitions of your KPIs. The AI knows what “margin” or “active customer” means for your business, combining data exactly according to your standards.
Multi-channel support
Your data warehouse is accessible anywhere. We integrate the assistant with Slack, Microsoft Teams, or your company portal. You have access to key metrics at your fingertips whether you’re in a meeting, at the office, or on your smartphone.
What does a conversation with your data look like?
Forget about complicated SQL queries and waiting for an analyst’s availability. This solution acts like a smart assistant that knows your business inside and out. We’ve streamlined the entire process into three simple steps that cut the time from question to decision from days to seconds.
For the Board of Directors
Example: “What was the margin last quarter compared to the previous year?”
For Sales
Example: “Which Enterprise customers did not renew their subscriptions this month?”
For Marketing
Example: “Which LinkedIn campaign had the lowest cost per lead in March?”
For HR/Internal Use
Example: “How many vacation days has the development team taken on average this year?”
Ready for a new era of analytics?
Contact usA smart bridge between questions and data
Behind this simple chat lies an advanced architecture that ensures precision and security. We don’t let AI guess – we build a system that translates business language into database language, relying solely on facts.
A Solid Foundation: Your Data Platform
We use your existing environment (BigQuery, Snowflake, Redshift) as your single source of truth. The system does not copy data—it analyzes it right where it is most secure.
Semantic Layer: Business Translator
We map the technical structure of the database to business concepts. This allows the AI to recognize that a column’s abbreviated name in the system actually corresponds to your key metric, such as “Revenue.”
LLM Engine: Precise Queries
We integrate state-of-the-art models (such as Gemini), which, thanks to advanced prompt engineering, do not “make up” answers but generate error-free SQL code that is ready to be executed in the data warehouse.
Security: Full access control
Your data is protected by strict role-based access control (RBAC) mechanisms. The AI will only provide responses based on the information to which a given user has official access within your organization.
Reduce the time from inquiry to decision
Ask about implementationSee conversational analytics in action
Your data holds great potential. Ask us how to make the most of it
Why should choose Alterdata?
We combine expert experience, extensive technical knowledge, and a flexible approach to collaboration to create data solutions that are truly tailored to your organization’s needs.
Comprehensive End-to-End Implementation
We manage the entire process: from consulting and technology selection, through data warehouse construction, to the development, maintenance, and optimization of solutions. This ensures that our clients receive consistent support at every stage of their data-related work, without having to coordinate multiple independent vendors.
Data Expert Team
We bring together the expertise of data engineers, analysts, data scientists, IT architects, and business consultants to address both technological and business needs. Our team helps translate an organization’s goals into concrete solutions that effectively support decision-making and business growth.
Technology Neutrality
We choose tools based on the goal, not the other way around. We work with popular cloud and analytics technologies, including Google Cloud, Azure, AWS, Snowflake, Databricks, Power BI, Tableau, and Looker. Thanks to our extensive knowledge of these tools, we recommend the solutions best suited to the client’s situation, rather than pushing a single technology.
Flexible Model of Collaboration
We offer support exactly when you need it, ranging from individual specialists to a Data Team as a Service model, without the need to build a full in-house team. This allows you to quickly expand your organization’s capabilities and leverage expert knowledge in a way that aligns with your current needs.
Business-Specific Solutions
We design services and architecture tailored to specific requirements, budgets, industries, company sizes, and business objectives. We treat each implementation as a unique case to ensure that the technology supports the processes, workflows, and priorities of the organization in question.
Secure Architecture
We create scalable, secure solutions designed to support organizational growth, handle increasing data volumes, and facilitate migration to modern cloud environments. We ensure access control, stability, and scalability so that the data platform can grow alongside your business.
Tech stack: the foundation of
our work
Discover the tools and technologies that power the solutions created by Alterdata.
Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.
Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.
Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.
Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.
Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.
Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.
Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.
Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.
Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.
Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).
Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.
Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.
AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.
Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).
Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.
Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.
BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.
Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.
Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.
Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.
Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.
PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.
Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and interactive dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.
Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.
Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive dashboards and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.
Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.
Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.
GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.
Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.
Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.
Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.
BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.
R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.
Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.
FAQ
What exactly is conversational analytics, and how will it impact my business?
Conversational analytics is a technology that connects your data warehouse with large language models (LLMs), allowing you to interact with data through simple conversation. Implementing this solution in your company will democratize access to knowledge – every employee will be able to instantly obtain the necessary analyses without needing to know SQL or use BI tools, which will significantly speed up decision-making processes.
Is my data secure, and does AI learn from our confidential information?
Data security is our top priority. At Alterdata, we deploy solutions within your private cloud infrastructure (e.g., Google Cloud or Azure). We use AI models in a closed environment, which means your business data never leaves your environment and is not used to train public models from third-party providers.
Can conversational analytics provide incorrect data (so-called “AI hallucinations”)?
We minimize this risk by using a fact-based architecture (e.g., RAG or Text-to-SQL technology). The AI doesn’t “make up” answers; instead, it translates your question into a precise database query. If your data warehouse doesn’t contain an answer to a specific question, the system will let you know instead of providing incorrect information.
With which systems can the conversational interface be integrated?
Our solution is flexible and can be integrated with the tools your team uses every day, such as Microsoft Teams, Slack, or internal company portals. We can also develop a custom web application tailored to your organization’s specific needs.
Do I need to have a modern data warehouse to implement this solution?
Conversational analytics works best when based on a structured data model (e.g., BigQuery, Snowflake). If your data is currently scattered or needs to be modernized, our team will help you lay the groundwork (Modern Data Warehouse) first so that the AI engine can use it effectively.
How can I measure the results of implementing conversational analytics?
You’ll measure the results primarily by the dramatic reduction in the time it takes to get business answers – from hours or days to just a few seconds. You’ll also see a decrease in the number of simple queries coming into the IT/BI department, allowing your analysts to focus on more complex tasks, as well as an increase in the use of data in your team’s day-to-day operations.
How long does it take to implement conversational analytics in an organization?
The implementation time depends on how well-organized your current data architecture is and the number of systems being integrated. At Alterdata, we work in the spirit of agile implementation—preparing a minimum viable product (MVP) for a selected business area (e.g., sales data only) typically takes 4 to 6 weeks. Full production implementation, including LLM model calibration, usually takes a few months.
How does the system handle unique KPI definitions and industry jargon?
Before launching the conversational interface, our engineers build what is known as a dedicated semantic layer. This is a dictionary of business terms that maps your company’s language to the database structure. As a result, the AI solution precisely “knows” how your organization defines terms such as “customer acquisition cost (CAC)” or “active customer,” eliminating the risk of misunderstandings and discrepancies in reports.
In which languages can I ask questions, and can the system support international teams?
The system is fully multilingual and supports languages from around the world. Thanks to advanced models such as Gemini, the platform seamlessly recognizes context and country-specific business jargon. An employee can ask a question in their own language (e.g., English), and the system will automatically analyze the data in the data warehouse and generate a response in the same language. This facilitates day-to-day work in organizations with an international structure.
What is the cost of maintaining such a solution (cloud costs and AI licenses)?
The cost of maintaining the system (OpEx) depends on the intensity of its use and the volume of data processed; however, by properly optimizing the architecture, we ensure maximum cost-effectiveness. We select AI models and queries to the data warehouse in such a way that you pay only for the cloud resources you actually use (pay-per-use system), without having to incur fixed, expensive license fees for each user.