Teams with large product datasets do not only need more charts. They need a way to understand what is changing, which products are performing, how demand may develop, and what actions deserve attention.
Adapt Insights was designed as an AI-integrated analytics product for exploring product forecasts, seasonality, trends, and performance data. Desisle created the product design and interactive prototype in 15 days, with one designer responsible for the design work.
Project Overview
- Client: Adapt Insights
- Industry: Analytics software and data intelligence
- Product: AI-integrated analytics and forecasting platform
- Platform: Web application
- Primary Users: Teams working with product and demand data
- Services: UI/UX design and interactive prototyping
- Team: One designer
- Duration: 15 days
- Deliverables: Product design and prototype
What Did Desisle Do?
Desisle designed an AI-powered analytics platform for Adapt Insights and delivered an interactive prototype within 15 days. The product design focused on helping teams explore demand forecasts, monthly and yearly data, product seasonality, emerging trends, product rankings, recommendations, and AI-assisted report generation.
The Product Challenge
Analytics products often contain extensive data but leave users to interpret it across separate dashboards, spreadsheets, reports, and presentation tools. Adapt Insights needed a more integrated experience: one place where teams could examine product-demand patterns, move between time periods, identify leading products and trends, and generate a useful summary for stakeholders.
The design challenge was to make a complex data product feel purposeful rather than overwhelming. Users needed a clear path from a high-level forecast to product-specific information, recommendations, and reporting outputs.
From Forecast Data to Product Decisions
The product was structured around questions teams may ask about their data:
- What is expected to happen to product demand?
- Which patterns appear across monthly or yearly time periods?
- Is a product affected by seasonality?
- Which products are the leading performers?
- What trends should the team investigate?
- What conclusions or recommendations can be generated from selected data?
- How can insights be shared in a presentation-ready format?
The interface needed to support both exploration and communication. A user should be able to move from a visual forecast to a more detailed product view, then use the findings to create a useful report or presentation.
Recommended Visual: A product-flow diagram showing the journey from selecting a period, reviewing forecasts, analysing trends, generating recommendations, and creating a presentation.
Why It Matters: It demonstrates that the product was designed as an end-to-end analytics workflow rather than a collection of disconnected charts.
Designing AI Into the Analytics Workflow
AI was designed as part of the analytics experience, not as a separate feature. The AI helper was intended to support interpretation of forecast graphs, charts, monthly and yearly forecasts, product-demand patterns, seasonality, and trends.
The product also included the concept of AI-generated presentation output. Users could select a relevant period or date range, then generate a structured PPT-style report containing timelines, product summaries, trend insights, top-performing products, areas for improvement, recommendations, and conclusions.
This approach required the interface to make the relationship between selected data, AI output, and user decisions understandable. The AI needed context from the analytics workspace rather than asking users to explain the same dataset again in a separate conversation.
Key UX and Product Decisions
1. Keep Forecasting and Interpretation Connected
Problem: Forecast data can be difficult to use when charts and conclusions live in separate places.
Decision: Design AI assistance around the product’s forecast graphs, charts, monthly and yearly data views.
Why: Users should be able to move from a visual pattern to a clearer explanation without losing the context of the data they are reviewing.
Trade-off: AI output needs to remain connected to the selected product, time range, and chart context so it does not feel generic or unexplained.
2. Support Both Broad and Product-Level Analysis
Problem: Teams need to understand overall demand patterns while also investigating individual product performance.
Decision: Include views for wider forecasting and trend analysis alongside product-level summaries, top-product rankings, and seasonal patterns.
Why: A high-level view helps users identify changes, while product-level detail helps them investigate what may be driving those changes.
Trade-off: The interface must avoid showing every data point at once. Clear hierarchy is needed to guide users from summary to detail.
3. Make Reporting a Natural Final Step
Problem: Insight is often lost when teams must manually recreate charts and conclusions in separate presentation software.
Decision: Include AI-assisted PPT generation within the product concept.
Why: The selected time period, data summaries, trends, recommendations, and conclusions can remain connected to the analysis already completed in the workspace.
Trade-off: Generated reports need transparent inputs and review opportunities so users can understand what data informed the output before sharing it.
The Designed Experience
The completed product design and prototype brought together the core parts of an AI-integrated analytics workflow:
- Demand-forecast visualisation.
- Monthly and yearly forecast views.
- Product seasonality insights.
- Trend analysis.
- Product-level summaries.
- Top-product and top-five product views.
- Recommendations and conclusion states.
- AI support for interpreting graphs, charts, and forecast data.
- AI-assisted PPT generation based on selected data and time periods.
The prototype was designed to communicate how users could move through the platform, from choosing a date range to reviewing forecasts, investigating product insights, and preparing an output for stakeholder discussion.
Recommended Visuals:
- A main analytics dashboard showing forecast charts and product performance signals.
- A seasonality or trend-analysis view showing a selected product and time range.
- An AI helper interface connected to a chart or forecast view.
- A report-generation flow showing selected insights prepared for a PPT-style output.
Outcome
Desisle delivered the UI/UX design and interactive prototype for Adapt Insights in 15 days with one designer. The work established a concept for an AI-integrated analytics product that brings forecasting, product trends, seasonality, summaries, recommendations, and presentation generation into one workflow.
No post-launch metrics or live implementation results were supplied. This case study does not claim improvements in forecast accuracy, analysis speed, data adoption, product demand, user retention, reporting time, revenue, or return on investment.
What We Learned
AI analytics products need to make their reasoning context visible. An AI-generated summary becomes more useful when users can see the selected date range, relevant product data, charts, and forecast signals that informed it.
The project also reinforced a core dashboard-design principle: not every insight should compete for attention at the same time. Users need a clear route from overview to investigation, then from evidence to a shareable conclusion.
Building a Similar AI Data Product?
Founders and teams building analytics, forecasting, planning, or AI reporting products often face the same design challenge: the data is valuable, but the experience does not yet make that value easy to access.
Desisle’s data-product offering covers dashboard, analytics, and data-visualisation design for complex digital products.
Frequently Asked Questions
What Is Adapt Insights?
Adapt Insights is an AI-integrated analytics product designed to help teams explore product-demand forecasting, seasonality, trends, product performance, recommendations, and reporting outputs.
What Did Desisle Deliver?
The verified deliverables were the product design and an interactive prototype, completed by one designer in 15 days.
Which Analytics Functions Were Included in the Design?
The supplied project scope supports forecast charts, monthly and yearly views, seasonality analysis, trend insights, product summaries, top-product views, recommendations, conclusions, and AI-assisted presentation generation.
How Was AI Used in the Product Concept?
AI was designed to help users interpret forecast graphs, charts, product-demand patterns, seasonality, and trend data. It was also intended to generate presentation-style reports from selected time periods and data.
Did Desisle Build the Live Product?
The supplied information confirms design and prototype delivery. It does not confirm production development, live launch, or the final implemented technology stack.
Are Business or Product-Performance Metrics Available?
No verified quantitative outcomes were supplied. The case study should not claim forecast improvements, revenue impact, time savings, reporting efficiency, adoption, or user-engagement results.