A forecast nobody acts on is just another chart. Azati builds forecasting, customer segmentation, and business intelligence solutions on data we’ve checked first to place the output where decisions get made: a lease review, a reorder point, a campaign plan, a production schedule, or a maintenance plan.
We connect data engineering, statistical modeling, and analytics delivery so teams can evaluate likely outcomes, understand the factors behind them, and use the results in business planning.
Data science services use statistics and machine learning to analyze business data, explain patterns, and predict likely outcomes. Typical outputs include revenue and demand forecasts, customer segments, risk estimates, decision-support models, and dashboards that help people act on the findings.
At Azati, data science is part of our data engineering practice. We assess the data behind a model, develop the analytical approach, validate the results, and integrate the output into the workflow where it will be used. If the data is incomplete or unreliable, we address that limitation before presenting a model as decision-ready.
These disciplines answer different questions, but useful business solutions often combine them.
| Capability | Business question | Typical output |
|---|---|---|
| Business intelligence (BI) | What happened, and where? | Dashboards, reports, KPI monitoring and self-service analytics |
| Predictive analytics | What is likely to happen next? | Revenue, demand, inventory and risk forecasts |
| Data science | Why is it happening, and what drives the outcome? | Statistical analysis, segments, driver analysis and predictive models |
| Decision support | What action should we consider? | Prioritization, probability scores, planning recommendations and operational signals |
A dashboard may show that demand is changing. A forecasting model estimates what comes next. Driver analysis explains the forecast, while a decision-support tool brings the result into the planning process. The objective is to better inform existing business decisions.
Azati develops BI, reporting, and predictive analytics solutions around defined business needs. The scope may range from a targeted forecasting tool to an analytical application built on a wider enterprise data platform.
Forecasting models estimate future outcomes from historical data and relevant business drivers. Depending on the use case, results can include a point estimate, a prediction range, or a probability score that helps decision-makers understand uncertainty.
Typical applications include:
The appropriate method depends on the data, forecast horizon, business constraints, and cost of an incorrect prediction. We evaluate model performance against held-out data and establish a baseline before claiming an improvement over an existing process.
Segmentation helps businesses understand groups of customers, accounts or audiences based on observed behavior and relevant attributes. It can support marketing planning, customer analysis, and more informed allocation of commercial resources.
Examples include:
Segmentation is useful when the groups reveal meaningful differences for a business decision. We work with stakeholders to establish what those differences should help them understand or do, rather than treating the number of segments as a success metric in itself.
Some business questions require more than a forecast or dashboard. They require an estimate, a ranking, or a signal that can inform a specific action.
Azati builds analytical tools that can support:
The model or calculation should make the decision easier to evaluate, not conceal uncertainty behind a single number. Where relevant, we expose the factors behind a result and test it against the business or physical limits of the use case.
Business intelligence solutions consolidate data into dashboards and analytical applications that help teams monitor performance and investigate changes.
Azati has experience with:
The foundation matters as much as the interface. If departments define the same metric differently or rely on inconsistent source data, a new dashboard can make disagreements more visible without resolving them. We address data preparation and business definitions as part of the analytical solution.
Conversational analytics gives analysts a natural-language interface for exploring business data. It can make existing data warehouses easier to use, but only when questions are interpreted correctly, and answers can be checked against the underlying data.
For enterprise use, the important considerations include business terminology, retrieval quality, SQL execution, source-system access, and the client’s security requirements. A conversational interface should make analytical work more accessible without hiding how an answer was produced.
Azati has applied this approach to a retail analytics environment, rebuilding an earlier prototype to support verifiable answers from the client’s own data. See the analytics copilot case study for the implementation and adoption results.
For enterprise knowledge retrieval beyond analytical data, see Azati’s Knowledge Intelligence capabilities.
An accuracy figure has limited business meaning without a reference point. We establish what the existing process can measure, define a suitable baseline, and distinguish model performance from proven business improvement. See the retail forecasting case study for an example.
Showing the factors that contribute to a result gives decision-makers a basis for questioning and validating it. Explanations support review, but do not independently prove accuracy or causation.
Testing should include the business rules, edge cases, and physical constraints that apply to the intended use. A model or calculation that works on typical inputs may still produce implausible results under less common conditions. See the reservoir analytics case study for an example.
A model or interface is unlikely to be useful if users cannot verify its output or incorporate it into their existing process. Adoption depends on reliable data access, relevant business definitions, and a clear way to check results. See the analytics copilot case study for an example.
Some decisions depend on statistical analysis or calculations grounded in scientific and physical relationships rather than conventional business reporting.
Azati has worked on analytical solutions for complex insurance data, including reserve estimation, and on reservoir analytics where calculations inform decisions about physical extraction processes.
These use cases require domain-specific validation. A result can be computationally plausible yet operationally wrong if the model violates the assumptions or constraints of the underlying system.
In a retail forecasting engagement, the client had not previously measured the accuracy of its own expert forecasts for store-opening decisions.
The measured figure established a baseline for the model. It did not prove that the model outperformed the client’s previous process. The next step was business validation during a test period.
A forecast is more useful when users can investigate why it changed. In the retail forecasting calculator, each forecast included a breakdown of its 15 leading drivers and the direction in which they influenced the result.
This gave development managers a way to question and validate the model’s reasoning rather than treating the prediction as an unexplained output.
The right explanation method depends on the model and use case. Explanations can support review, but they do not by themselves establish that a prediction is correct or that a factor causes the outcome.
On an oil and gas reservoir analytics project, QA identified calculation coefficients exceeding physical limits by 8–12 times under extreme salinity conditions. Azati redesigned the computational core in 10 days, maintained the delivery date, and passed user acceptance testing on the first attempt.
The lesson applies beyond industrial modeling: test data and edge cases can reveal failures that a successful demonstration does not. Validation needs to reflect the conditions under which a model or calculation will actually be used.
An earlier AI-built analytics prototype at a retail client had no users. Its search failed when questions were phrased differently, and analysts could not reliably check its answers against actual data.
The broader principle is simple: analytical tools need to fit the way people work, and users need a way to verify the results.
Azati works with midmarket and enterprise organizations that already collect business or operational data but need to use it more effectively for forecasting, analysis, and planning.
Commercial, finance, and development teams evaluating new locations, forecasting revenue, or improving demand and inventory planning.
Analytics and marketing teams combining audience data, investigating engagement, and enabling self-service reporting.
Operations and engineering teams using production forecasts and analytical calculations that must reflect physical constraints.
Need more reliable forecasts for investment, expansion, and resource-allocation decisions.
Teams are constrained by fragmented reporting, manual analysis, or underused analytical tools.
Need models and calculations validated against operational conditions.
Responsible for making existing data platforms more useful to business teams.
Predictive analytics becomes relevant when historical reporting is no longer sufficient for the decisions a business needs to make.
Common signals include:
The right starting point is to define the decision, assess the data available, and establish how the result will be evaluated. Not every problem requires a complex machine learning model; in some cases, better data preparation, statistical analysis, or a simpler forecasting method is more appropriate.
We start with the business decision, not a preferred algorithm. That keeps the project focused on a result that can be evaluated and used.
Actual delivery time depends on data readiness, scope, access, and validation requirements. The retail forecasting example above illustrates a focused engagement from kickoff through deployment.
We identify the decision the solution should support, who makes it, and how often it occurs. This determines the relevant forecast horizon, required level of detail, and acceptable consequences of error.
We examine source availability, quality, history depth, gaps, consistency, and potential data leakage. If the data foundation is not ready, we identify what needs to be fixed before modeling.
We measure the current process where possible. If the business has never measured its existing approach, we make that limitation explicit and define an initial reference point.
We compare suitable statistical or machine learning approaches using held-out data. Depending on the use case, validation may include forecast error, classification trade-offs, driver explanations, edge cases, and business constraints.
We deliver the result through an analytical application, calculator, dashboard, API, or operational signal, not just a notebook or a presentation.
We agree on a test period and assess how the solution performs against observed outcomes. If the model is intended to remain in use, we also define how accuracy, changing patterns, and maintenance responsibilities will be handled.
A focused engagement built around one defined business decision and a limited analytical scope. It establishes data suitability, tests the selected approach, and delivers an output that can be evaluated by the intended users.
A broader solution combining forecasting, segmentation, BI, or decision support with the data integration and application components needed for business use. Delivery can be phased around business priorities.
Continued evaluation and improvement of analytical solutions, with monitoring, retraining, and maintenance responsibilities agreed according to the solution’s operational requirements.
You’re ready if: your decision data sits in a structured environment (for example, PostgreSQL or Greenplum) and the history is long enough for the pattern you need to model. For seasonal demand, 12–24 months is a useful starting point; we assess sufficiency case by case.
You need data readiness if: your department metrics conflict across systems, data is trapped in unstructured silos, or historical records have major gaps.
Before developing a model, Azati assesses whether the available data can support the business decision you want to improve.
The assessment examines source availability, historical coverage, data gaps, consistency, and potential data leakage. It also considers how the existing decision process can be measured and what validation would be required.
You receive a view of the data’s suitability for the proposed use case, the limitations that may affect the results, and the recommended next steps. Where the data is not ready, the assessment identifies what should be addressed before model development begins.
If the data is not ready, we will say so. In some projects, improving the data pipeline or clarifying business definitions is the right first step.
A large grocery retail chain was making new-store investment decisions primarily through expert judgment, without a measurable baseline for forecast accuracy. With 50–100 new locations planned across its core metro region, the risks were significant: 21% of new stores closed as unprofitable in 2025, while another 16% were operating at a loss. Azati was tasked with developing a machine learning calculator to estimate average monthly revenue for prospective locations, helping development managers make more informed decisions before signing a lease.
Azati developed a web-based, two-circuit ML calculator combining a revenue regression model with a weak-store classifier. Using location coordinates, store area, and internal store-cluster information, the system estimates average monthly revenue, provides a prediction interval, and assesses the probability of a location underperforming. SHAP-based explanations show the main predictors behind each forecast.
The client is conducting a three-month test period to compare calculator-assisted store-opening decisions with its previous expert-judgment approach. The business impact has not yet been quantified. The reported model metrics are technical evaluation results, not evidence of improved store profitability or reduced closure rates.
A major oil and gas enterprise needed a petroleum analytics platform to calculate well interaction coefficients, target compensation, production rates and losses, reservoir pressure dependencies, and gas-oil ratio forecasts – processing high-volume production data across well coordinates, lithology, salinity, and historical production datasets. Requirements changed mid-sprint throughout the build, mathematical formulas needed constant validation, and because the calculations directly influence physical extraction decisions, precision and stability were non-negotiable.
Azati built a modular suite of Python-based calculation services, each independently deployable, covering well compensation, gas-oil ratio forecasting (with ML-assisted clustering and regression), and production rate calculation. Two weeks before launch, Azati's QA team caught a critical defect through property-based and stress testing on real historical geological data – impact coefficients were exceeding physical limits by 8–12x under extreme salinity conditions – and the engineering team redesigned the computational core in 10 days without slipping the delivery date.
A major entertainment company managing cable channels, streaming services, and digital platforms faced overwhelming data volume – terabytes daily from online and offline sources. They needed deep insight into audiences (age, gender, hobbies, social connections, behavior) and a way to optimize content release timing, ad-targeting, and audience retention that manual, historical-data analysis couldn't deliver.
Azati built a unified BI and data warehouse solution combining 30+ sources into a central platform, enabling advanced audience segmentation across 100+ attributes and fast, self-service access for marketing and analytics teams – streamlining content planning and ad-targeting across TV and digital platforms. Azati has run and extended this platform for the client since 2017.
A retail analytics team needed one system to search corporate knowledge by meaning, connect that knowledge to its Cursor code assistant, and verify analytical hypotheses against live data. Its previous AI-built prototype had no users: literal keyword matching failed across different terminology, responses were slow, and the absence of instrumentation made retrieval quality difficult to assess. The replacement also had to operate entirely within the client's proprietary infrastructure and existing data environment.
Azati rebuilt the prototype as an AI analytics copilot combining custom hybrid retrieval, an MCP server for Cursor integration, an SQL execution layer, and a benchmark repository. The system searches corporate analytics knowledge using both semantic meaning and exact terms, gives the code assistant access to the same knowledge base, and lets analysts and agents check hypotheses against the client's own data.
One embedded ML Engineer / Backend Developer owned the rebuild end to end, covering retrieval architecture, MCP integration, SQL execution, benchmarking, and the project roadmap. The engagement has run on Kanban since March 2026 and remains ongoing.
The platform runs entirely inside the client's proprietary infrastructure, with no external cloud dependency. SQL execution takes place within the client's own data environment and uses its existing access paths.
A large chemical enterprise required scalable mechanisms to collect, transform, and deliver trusted analytical data across dozens of interconnected business systems. Growing analytical requirements, a complex data ecosystem with hundreds of flows and thousands of tables, and the need for reliable reporting data created ongoing engineering challenges.
Azati embedded specialists into the client's delivery organization, developing analytical pipelines, data marts, and orchestration workflows using SQL, Python, dbt, Apache Airflow, Apache NiFi, Greenplum, PostgreSQL, and ClickHouse. The engagement focused on continuous platform evolution over 24+ months rather than a one-time implementation.
We select technologies based on the data environment, analytical requirements, delivery constraints, and the way the output will be used. The following tools have been used in Azati projects. The stack for a new engagement depends on its requirements.
| Layer | Technologies used in delivered projects |
|---|---|
| Modeling and statistical analysis | Python, pandas, NumPy, scikit-learn, XGBoost, LightGBM, Optuna, SHAP, Keras / TensorFlow, PyTorch |
| Time-series and statistical methods | SARIMAX, piecewise and regularized regression, clustering |
| Data platforms and compute | PostgreSQL, Greenplum, ClickHouse, Microsoft SQL Server, IBM Db2 Warehouse, Netezza, AWS Athena, AWS SageMaker |
| Application delivery | Flask, FastAPI, Docker, web calculators and analytical applications |
| Business intelligence | IBM Cognos, custom web dashboards |
The technology list is evidence of experience, not a commitment to use every tool on every project. We assess the existing platform and integration constraints before recommending a stack.
Analytics projects often overlap with data engineering and AI. The right starting point depends on the problem you need to solve.
Start with Data Engineering & AI Data Readiness. We assess and improve the pipelines, transformations, and analytical datasets needed for reliable downstream use.
Explore Generative AI & Advanced ML Engineering for use cases such as recommendations, anomaly detection, computer vision, NLP, and generative AI.
Explore Managed AI & Process Re-engineering for ongoing monitoring, optimization, and operational support.
Explore Knowledge Intelligence for search and retrieval across business documents and other knowledge sources.
These capabilities can be combined where the project requires them. The scope should follow the business problem rather than expanding automatically into a larger AI initiative.
Tell us which decision you need to support, who makes it, and where the relevant data lives. We’ll review the use case and outline the first steps, including whether your data can support the analysis.
Share the details of your decisionData science services can include forecasting, segmentation, statistical analysis, driver explanations, decision-support models, and the dashboards or applications that present the results. At Azati, we also assess the data foundation and integrate analytical outputs into the workflow where they will be used.
Business intelligence helps teams understand what has happened through reports, dashboards, and KPI analysis. Data science uses statistical methods and machine learning to investigate patterns, explain drivers, and estimate future outcomes. Predictive analytics focuses specifically on likely future outcomes. Many business solutions combine all three.
Forecast accuracy depends on the use case, data quality, history depth, forecast horizon, and the variability of the outcome. We cannot responsibly promise an accuracy figure before assessing the data. We evaluate the model on held-out data and compare it with a suitable baseline. In a recent retail forecasting proof of concept, the model achieved 30.1% WAPE (weighted absolute percentage error) on held-out stores; that was the client's first measured baseline, not a demonstrated improvement over its previous process.
It depends on data readiness, how many decisions the model must support, integration scope, and validation needs, so we don’t publish a single figure. A proof of concept scoped to one decision and one model pair is the reference shape (our most recent took 10 weeks), and the data readiness assessment tells you first whether that scope is realistic.
For simple series, you may get far with one. The harder parts are rarely the model: judging whether the history is sufficient, preventing data leakage, setting a baseline against the current process, validating against business and physical limits, and getting the output into the workflow. An assistant can produce a forecast in minutes; it can’t tell you whether the data supports trusting it. Azati’s work concentrates on those surrounding steps.
The timeline depends on data readiness, the complexity of the decision, integration requirements, and validation scope. Azati's retail forecasting example demonstrates that a deployed analytical application can be delivered within a focused proof-of-concept engagement. See the case study and project metrics above for the specific delivery timeline.
There is no universal minimum. The required history depends on how frequently the event occurs, seasonal patterns, business changes, and the forecast horizon. We assess whether the available data supports a meaningful model before selecting an approach. If it does not, improving data collection or starting with a simpler method may be more appropriate.
We can use model-specific explanation methods to show which factors contributed to an individual prediction. In Azati’s retail forecasting calculator, each forecast included a breakdown of its 15 leading drivers and their direction of influence. Explanations help users review a result, but they do not guarantee accuracy or establish causation.
We build both. Our work includes BI and video-on-demand analytics, self-service dashboards, forecasting calculators, and analytical applications. We aim to deliver the output in the format that fits the decision, whether that is a dashboard, a web tool, an API, or a signal used by another system.
A model needs to be evaluated against real outcomes after deployment. Data distributions and business conditions can change, so projects intended for ongoing use should define monitoring, review thresholds, maintenance responsibilities, and retraining requirements where applicable. These responsibilities should be agreed as part of the delivery scope.
Yes. Azati has delivered a retail forecasting calculator and an analytics copilot within client-controlled environments where data sources and credentials were provisioned by the client. The deployment model for a new project depends on the client’s infrastructure, security requirements, and available technologies.
This service focuses on analytics built around business data: forecasts, segmentation, BI, and decision support. Generative AI & Advanced ML Engineering covers machine learning and AI capabilities embedded into products and operational processes, including recommendations, anomaly detection, computer vision, NLP, and generative AI. Some projects require both capabilities.
Azati’s data scientists and engineers are based in Europe and work Central European hours, aligning with the working day in DACH and Benelux. For US engagements, collaboration arrangements depend on the client’s location, delivery needs, and project scope.
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