AnalyZ is a browser-based data analysis platform that lets you work with both quantitative and qualitative data — no coding or software installation required. It covers the full research workflow: data cleaning, statistical analysis, visualisation, qualitative coding, and sample size planning.

Yes — all core analysis features are completely free for registered users. No credit card required. The only cap on the free plan is Sense Making, which is limited to 1 interpretation per month. Refining an existing interpretation also counts as one interpretation. Paid plans with higher limits are in development. A Guest mode is also available with limited features (50 rows / 1 MB) if you want to explore without registering.

For quantitative data: CSV, Excel (.xlsx / .xls), Stata (.dta), SPSS (.sav), and JSON. For qualitative data: plain text (.txt), Markdown (.md), and Word documents (.docx). Variable labels and value labels are imported automatically where available.

You can also connect live to survey platforms via API — including KoBo Toolbox, ODK Central, REDCap, Google Sheets, and any custom REST endpoint returning JSON or CSV. Labels, value codes, and multilingual question text are all pulled automatically.

No. Every module includes a plain-English interpretation panel explaining what the results mean and what you should do next. The Guided Analysis feature also recommends the right test for your goal if you are unsure where to start.

Yes. Your data is processed entirely within your browser session and is never stored on our servers. All communication is encrypted with TLS (HTTPS). By default, when you close the tab, the data is cleared from memory. If you'd like to resume a session later on the same device, you can turn on Save on this device in the sidebar — this keeps a copy in your browser's local storage only (never sent to us) until you turn it off or it expires after 24 hours. Saved .analyz.json session files are downloaded directly to your device — no server copy is kept. See the User Guide (Account & Security section) for full details.

Only if you turn it on. By default, nothing survives closing the tab. If you'd rather not re-upload every time, toggle Save on this device in the sidebar — the first time you have data loaded, AnalyZ will ask whether you want to keep it locally. This data is stored only in your browser (never sent to our servers), automatically expires after 24 hours, and you can turn it off anytime from the same toggle, which also clears anything already saved.

Click Sign up free on the landing page or login screen. Enter your email and choose a password. A verification link will be sent to your email — click it to activate your account. You can then log in at analyz.solutions.

Click Forgot password? on the login screen and enter your email address. A password reset link will be sent to you. The link is single-use and expires after one hour. If it expires, request a new one from the same screen.

Absolutely. Results tables are formatted to APA standards where applicable. Download tables as Excel or Word files ready for your manuscript. Charts export as high-resolution PNG or interactive HTML.

Yes. AnalyZ has a built-in API connector that lets you pull data directly from survey platforms — including KoBo Toolbox, ODK Central, REDCap, and Google Sheets — without downloading any files first.

For KoBo-based platforms, you can sign in with your API token (one click from your KoBo account settings) or with your KoBo username and password — AnalyZ exchanges these for a token automatically, so you never need to hunt one down manually. AnalyZ automatically imports variable labels, value codes, and multilingual question text. You can select which variables to load before the data enters the workspace.

For any other platform, use the Custom API option and paste any REST endpoint URL returning JSON or CSV.

Use the 💾 Save Session button in the sidebar after loading data and performing at least one action — this downloads a .analyz.json file. This saves your dataset, variable labels, and action history. Click 📂 Restore Session anytime and select that file to resume exactly where you left off — it works regardless of whether you're currently in Quantitative or Qualitative mode. For qualitative sessions, the saved file also includes your codebook and all coded segments.

Yes. Every quantitative analysis module has a 🔍 Filter data panel at the top. Add one or more conditions (e.g. gender == Female, age > 30) to restrict the analysis to a specific subset without permanently changing the dataset.

AnalyZ is optimised for desktop use. It will load on mobile browsers but the analysis interface — particularly the qualitative coding workspace — is best experienced on a larger screen.

Guest mode lets you explore AnalyZ without an account. It is limited to 50 rows / 1 MB for quantitative data and 1 file / 1 MB for qualitative data. Session saving is disabled. Sign up free to remove all limits.

Go to Data Management → Recode Variable. Select the source variable, define old-value-to-new-value mappings, give the recoded variable a name, and click Apply. The original variable is preserved and the new version is added as a separate column.

Go to Data Management → Create Variable. Choose a method:

• Formula — arithmetic using existing columns (e.g. income / age)
• Constant — a fixed value for every row
• Conditional — if/else logic (e.g. score > 50 → "Pass")
• Sum / Mean of items — combine scale items into a composite score

Yes. Data Management → Merge Data lets you join two datasets on a common key variable. Choose inner, left, right, or outer joins. Append Data stacks two datasets with the same columns on top of each other — useful for combining survey waves.

Go to Data Management → Split Column. Select the text column, choose a delimiter (comma, space, semicolon, pipe, tab, or custom), and click Preview split to verify before applying. Each part becomes a new column named colname_A, colname_B, etc., inserted right after the source column.

Go to Data Management → Reshape Data. Choose a direction:

• Wide → Long — select ID columns to keep and value columns to unpivot (uses pd.melt). Useful for repeated-measures or longitudinal data.
• Long → Wide — select an index column, a category column (becomes column headers), and a values column (uses pd.pivot_table). Useful for creating summary matrices.

Variable and value labels are cleared after reshaping — re-add them in Data View if needed.

Use the ↩ Undo button in the sidebar. AnalyZ keeps a history of up to 10 actions. Each undo step restores the dataset, variable labels, and value labels together.

There are two ways. From Data View: click a column header to open the Column Inspector, then click Change in the Variable type row. From Data Management → Variable Types: select a variable and choose its type from the list. Both routes update the same setting.

AnalyZ auto-detects types — numeric columns with more than 10 unique values are treated as continuous, others as categorical — but you can override any variable. This matters for variables like Likert scales coded 1–5, which should be treated as categorical for most tests.

Type overrides apply across all analytical modules (Descriptive Analysis, Inferential, Visualization) for the rest of the session. They survive a tab refresh if Save on this device is on, and are included in any .analyz.json session file you download.

Expand the ⬇ Download Data section in the sidebar. Choose a format — CSV, Excel (.xlsx), Stata (.dta), or SPSS (.sav) — give it a filename, and click Download. Variable labels and value labels are embedded in the Stata and SPSS exports.

Descriptive Analysis has five analysis types: Frequency tables (counts, %, cumulative %), Cross-Tabulate (contingency tables with chi-square), Table (grouped means and SDs — the academic "Table 1"), Summary Statistics (mean, median, SD, skewness, kurtosis, quartiles), and Data Quality (missing values, duplicates, constants, and outliers for every variable).

Go to Descriptive Analysis → Data Quality. It audits every variable for missing values (count and %), duplicate rows, constant columns (only one unique value), and outliers (values beyond 3 standard deviations, for numeric columns). Results are colour-coded — red for high missing (>30%), orange for moderate (10–30%), and green for clean columns. Run this before any analysis.

Select Cross-Tabulate, choose a row variable and a column variable (both categorical), and click Generate. The output is a contingency table showing counts and row/column percentages. A chi-square test of independence is included automatically — it tells you whether the two variables are statistically associated.

AnalyZ includes 16 tests across four groups:

• Means & Differences: Independent t-test, One-sample t-test, Paired t-test, One-way ANOVA, Two-way ANOVA
• Frequencies & Proportions: Chi-square test, Z-test for Proportion
• Correlation: Pearson, Spearman
• Regression: Linear, Logistic, Probit, Multinomial, Ordinal, Hierarchical, Multilevel

All tests include plain-English interpretation. Regression models also report effect sizes and confidence intervals.

Use 🧭 Guided Analysis in the top navigation bar — it asks two questions and recommends the right test. As a quick guide: comparing two group means → Independent t-test; three or more groups → One-way ANOVA; two categorical variables → Chi-square; predicting a numeric outcome → Linear Regression; predicting a yes/no outcome → Logistic Regression.

Yes. Every inferential test reports the appropriate effect size — Cohen's d for t-tests, eta-squared (η²) for ANOVA, Cramér's V for chi-square, and R² for regression — along with a plain-English interpretation of whether the effect is small, medium, or large.

"ns" stands for "not significant" (p ≥ 0.05). AnalyZ uses standard significance labels: *** (p < .001), ** (p < .01), * (p < .05), ns (p ≥ .05). Statistical significance does not equal practical importance — always report effect sizes alongside p-values.

Hierarchical Regression lets you build a model in blocks, testing how much each additional set of predictors improves R² (ΔR²). Add control variables in Block 1, your variables of interest in Block 2 (and more blocks if needed), then click Run. The output shows each block's model and a change statistics table showing ΔR² and ΔF for each step.

Multilevel Regression — also called mixed models or random effects models — is used when your observations are nested within groups: for example, patients within clinics, students within schools, or survey respondents within villages. Standard regression assumes all observations are independent; when they are clustered, ignoring the grouping inflates Type I error. Multilevel regression models this clustering explicitly.

Use it when your data has a hierarchical structure, when you want to know how much of the outcome variance is between groups versus within groups (the ICC), or when you have both individual-level and group-level predictors.

AnalyZ supports three outcome types in the Multilevel Regression module:

• Continuous — a numeric outcome such as a score, income, or expenditure. Uses EM-REML estimation.
• Binary — a yes/no or 0/1 outcome. Uses Penalised Quasi-Likelihood (PQL) and reports odds ratios (OR) or log-odds coefficients — you toggle between them in the results.
• Count — a count of events or occurrences. Uses PQL with a Poisson log-link and reports incidence rate ratios (IRR).

The Intraclass Correlation (ICC) is the proportion of the total outcome variance that is explained by group membership. An ICC of 0.15 means 15% of the variance is between groups — the rest is within-group individual variation.

As a rule of thumb: ICC > 0.05 justifies a multilevel model. ICC < 0.05 suggests the grouping is not meaningful and a standard regression would give equivalent results. AnalyZ reports the ICC alongside a plain-language interpretation and includes an independent verification panel showing a naive ICC computed directly from group means — so you can confirm the model-based estimate is reliable.

Random Intercept: Each group has its own baseline level (intercept), but the effect of every predictor is assumed to be the same across all groups. This is right for most nested datasets — for example, clinics differ in their baseline vaccination rates but the effect of a predictor like distance-to-clinic is the same everywhere.

Random Slope: One predictor is allowed to have a different effect in different groups. For example, the effect of an intervention may be stronger in urban clinics than rural ones. This adds a variance component for that slope. Use it when you have a specific theoretical reason to expect effect heterogeneity across groups.

Yes. The Multilevel module supports 2-level, 3-level, and 4-level models — for example, patients nested within clinics nested within districts nested within regions. Select the number of levels and assign a grouping variable for each level. A hierarchy check runs automatically: it tells you whether your data is truly nested (each lower-level unit belongs to only one upper-level unit) or crossed/partially crossed, and flags groups with very few observations.

15+ chart types including Bar, Histogram, Scatter, Line, Boxplot, Violin, Heatmap, Bubble, Pie, Donut, Forest plot, and more. All charts are interactive (zoom, pan, hover for values) and can be exported as PNG, JPG, or interactive HTML.

Yes. Each chart has a 🎨 Edit chart panel where you can change the title, axis labels, font size, text colour, bar/marker colour, background colour, and axis ranges. Click Apply changes to update the chart. Changes persist for the session.

Yes. From Data Visualization, select Multi-chart Dashboard from the opening card screen. Build your charts and they assemble into a responsive grid with cross-filtering — clicking any bar, slice, or data point filters all other charts simultaneously. Building and saving a dashboard privately works on every plan, including Free. Publishing it as a live public link is a Starter/Pro feature — see the next question. The dashboard can also be downloaded as an interactive HTML file on any plan.

Yes, on Starter and Pro plans. After building a multi-chart dashboard, click Share dashboard. This publishes it as a live link — anyone can open and interact with it in their browser with no AnalyZ account required. You can revoke or update the link at any time from the dashboard view. For a narrative format, see Data Canvas below — a storytelling tool that also publishes as a public link on Starter/Pro.

Data Canvas is a storytelling module that turns your analysis outputs into a shareable, scrollable data story powered by AnalyZense. You collect findings from any analysis module using Add to Canvas, then ask AnalyZense to generate a story — it reads all your findings, decides the best way to present them, writes plain-language headlines and narratives, and assembles them into a structured slide story. You can refine individual slides, adjust heights, and publish as a public link anyone can read in their browser with no account required. Designed for communicating findings to non-technical audiences such as donors, community members, or board members.

They serve different purposes. The dashboard is an analytical tool — multiple charts side-by-side with cross-filtering, designed for exploring and interacting with data. Data Canvas is a communication tool — a scrollable narrative with insight headlines and plain-language context, designed for telling the story of what the data means to a non-technical audience. You can use both: explore with the dashboard, then add key charts to a Data Canvas story.

Every analysis module has an Add to Canvas button. Supported finding types: Chart (any Plotly chart from any analysis module), Table (frequency tables, crosstabs, test statistics), Infographic (network maps and SVG diagrams from qualitative analysis), and Quote (coded participant excerpts from Thematic Coding). These collected findings form your Findings tab — the evidence repository. AnalyZense then reads them all and generates the story.

AnalyZense does two things in Data Canvas. First, Generate Story — you answer three questions (what the study is about, who the audience is, and any key message you want to convey), choose a presentation duration (5, 15, or 30 minutes), and AnalyZense reads all your collected findings, decides the best grouping and layout for each slide, writes new plain-language headlines and narratives, and assembles the full story. Second, Refine slide — after generation, each slide has a Refine button where you give a plain-language instruction (e.g. "make the headline shorter", "convert the forest plot to a bar chart", "lead with the network diagram"). AnalyZense updates just that slide. Both actions count against your monthly allowance shown in the canvas header.

Yes, on Starter and Pro plans. Canvases are private by default. When your story is ready, click Publish in the canvas header — a dialog opens where you can write an optional note for readers, and the story is published as a public link. After publishing, the header shows Copy link to copy the URL. If you regenerate or refine slides after publishing, click Re-publish to update the public version. Anyone with the link can read the story in their browser — no AnalyZ account needed, no software required. The viewer is mobile-friendly.

Yes. The Thematic Coding module has an Add to Canvas button on every coded segment. Clicking it opens the canvas modal where you choose the destination canvas. The quote is added as a styled slide with the excerpt and transcript source as attribution. Qualitative analysis outputs — co-occurrence tables, comparative charts, saturation curves — can also be added as table or chart slides from their respective modules.

Cronbach's Alpha measures the internal consistency of a set of scale items — i.e. how well they all measure the same underlying construct. Use it when you have a multi-item scale (e.g. a 5-item anxiety questionnaire) before computing a composite score. A value of 0.70 or above is generally considered acceptable; 0.80+ is good; 0.90+ is excellent.

Click "Check items for reverse coding" before running. AnalyZ inspects inter-item correlations and flags any item that correlates negatively with the majority — these are likely reverse-scored items. Pre-ticked items are suggested for reversal; confirm or adjust, then click Run.

PCA (Principal Component Analysis) reduces a large set of variables into fewer uncorrelated components that capture the most variance — useful for data simplification or removing multicollinearity. EFA (Exploratory Factor Analysis) models the latent factors that cause variables to correlate — useful for understanding the construct structure of a scale. PCA is a mathematical transformation; EFA is a theoretical model.

Use Varimax if you expect your factors to be independent (orthogonal rotation). Use Oblimin if you expect your factors to correlate — for example, "stress" and "burnout" are theoretically related. If unsure, run Oblimin first and check the factor correlation matrix: if all correlations are below 0.32, Varimax is equally appropriate.

Sampling brings together three planning tools in one place: Sample Size Estimation (how many participants you need), Power Analysis (the probability your study will detect a real effect, or the sample size needed for a target power), and Draw a Sample (upload a sampling frame and have AnalyZ actually select your sample using your chosen design). All three run before or independently of any loaded dataset.

Calculators are grouped by study design rather than sampling technique. Cross-sectional is one flexible calculator covering a single round of data collection — choose whether you're estimating a proportion or a mean, and how you'll select participants (simple random, stratified, or cluster/multi-stage, which also covers PPS). Measuring change covers Longitudinal Study, Change in Proportion, and Randomised Controlled Trial (RCT) — designs built around detecting a difference. Other study designs covers Case-Control and LQAS.

Open Sampling, choose Sample Size Estimation, and pick the study design that matches your plan. For Cross-sectional, also choose what you're measuring (a proportion or a mean) and how you'll select participants. Enter your confidence level (typically 95%), margin of error or desired power (typically 80%), and any design-specific parameters. Click Calculate sample size. The result shows the required number with an interpretation and the formula used.

For proportion-based calculators, use 50% — this gives the most conservative (largest) sample size estimate. For power-based calculators, use a medium effect size (Cohen's d = 0.50 for t-tests). You can also base your estimate on results from similar published studies.

Yes. In the Cross-sectional calculator, switch to "Estimating a mean (numeric)" for outcomes like income, age, test scores, or expenditure. You'll need an estimate of the standard deviation — ideally from a pilot study, since the result is sensitive to this assumption.

The design effect (DEFF) is a multiplier that accounts for the fact that people within the same cluster (e.g. the same village or school) tend to be more similar to each other than to people in other clusters. A DEFF of 1.5 means you need 50% more participants than a simple random sample would require. It defaults to 1 (no clustering effect) in every calculator and is always editable. When you choose "Cluster / multi-stage" sampling in the Cross-sectional calculator, you can instead calculate DEFF from the intraclass correlation (ICC): DEFF = 1 + (m−1)ρ, where m is your average cluster size. In field surveys, DEFF typically ranges from 1.5 to 3.0.

Choose "Stratified" as your sampling approach in the Cross-sectional calculator, then add each stratum with a name and population size. By default, the total sample is split across strata proportionate to population size. If you need to report or compare results by subgroup, check "I need this precision within each stratum" — every stratum then gets the full sample size independently, since a large national total can still leave individual strata underpowered.

The longitudinal calculator asks for the number of waves and expected attrition per wave (% of participants expected to drop out between each round). It calculates the number of participants you need to enrol at baseline so that enough remain by the final wave for valid analysis. The formula is: enrol = n_required / (1 − attrition)^(waves − 1).

Use two independent groups when comparing two separate samples — for example, a treatment group vs. a control group, or two different groups surveyed at each time point. Use same group, before/after when the same individuals are measured at both time points. The paired design typically needs a smaller sample for the same effect, since each participant acts as their own comparison — you'll also enter an expected correlation between the two measurements (0.5 is a reasonable default), and a higher correlation further reduces the required sample.

Power (1−β) is the probability that your study will detect a true effect if one exists. A power of 0.80 means an 80% chance of getting a significant result when the effect is real. Studies with low power miss real effects and produce unreliable results. Most journals and ethics boards require power justification — typically 80% or 90%.

Two-sample t-test, one-sample t-test, paired t-test, chi-square test, McNemar's test, one-way ANOVA, linear regression, and logistic regression. Each supports two modes: enter a sample size to see the achieved power, or enter a target power to find the required sample size. A power curve chart shows how power changes across a range of sample sizes for small, medium, and large effects.

Use a paired t-test or McNemar's test when the same individuals are measured twice — for example, a knowledge score before and after training. Use a two-sample test or chi-square test when you are comparing two independent groups, such as a treatment group versus a separate control group. Paired designs generally require fewer participants for the same effect size.

EPV is the number of outcome events divided by the number of predictors in your model. A conventional minimum of 10 events per predictor helps avoid an unstable or overfit model. The Power Analysis logistic regression calculator reports the required sample size from both the power calculation and the EPV rule, and uses whichever is larger.

Draw a Sample lets you upload your sampling frame (a CSV or Excel file listing every unit in your population) and have AnalyZ select the sample for you. Choose a design — Simple Random, Systematic, Stratified, Cluster, or Probability Proportional to Size (PPS) — set your parameters, and click Draw sample. The selected units are shown in a preview table and can be downloaded as CSV or Excel, either as the selected rows alone or the full frame with a "selected" flag column.

No. Like all data in AnalyZ, your sampling frame is processed entirely in your browser. It is never uploaded to or stored on our servers.

Yes. Draw a Sample includes an optional random seed. Setting a seed before drawing means the same seed, frame, and design will always reproduce the exact same sample — useful for audit trails or re-generating a lost sample list. Note that a seed is specific to AnalyZ's random number generator and will not reproduce the same draw in other software such as R or Stata.

Plain text (.txt), Markdown (.md), and Word documents (.docx). You can upload up to 10 files at once (up to 250 MB total). Registered users only — Guest mode is limited to 1 file / 1 MB.

Open Qualitative Analysis → Thematic Coding → Assign Codes. The transcript appears on the left. Click and drag to highlight the passage you want to code. With text selected, click any code button in the Coding Panel on the right — the segment is saved immediately with that code and highlighted with a coloured underline. You can assign multiple codes to the same passage and add an optional analyst note before clicking a code.

Go to Thematic Coding → Manage Codes. Click ➕ Add new code, enter a name, choose a colour, and optionally add a description. Codes can be edited or deleted at any time — deleting a code removes it from all coded segments automatically. You can also add new codes on-the-fly from within the Assign Codes workspace.

Explore Themes (6 cards): transcript overview, code frequency bar chart with cross-transcript heatmap, word cloud, word search (KWIC), code saturation curve, and comparative analysis by participant group. Analyze Relationships (6 cards): co-occurrence matrix, code network, mutual exclusivity, code clustering, segment length analysis, and code sequences (Sankey). Every view includes an automated plain-language interpretation.

Open Explore Themes → Comparative. The tab has two modes:

• Per transcript — always available, no setup needed. Shows each code's share of total code assignments per transcript as a heatmap and raw count table. Useful for seeing which themes dominated each individual interview.
• By participant group — requires tag categories. Use Transcript Settings (under View Transcripts) to define categories (e.g. Gender: Male/Female, Location: Rural/Urban) and assign each transcript a value. This mode then shows normalised code frequencies broken down by each group, enabling fair comparison regardless of group size.

Yes. Click Save Session in the sidebar to download a .analyz.json file. This saves all transcripts, your full codebook (codes, colours, memos, and keywords), and every coded segment with its assigned codes and analyst notes. Use Restore Session anytime to resume — all coding work is restored automatically.

Save on this device (the toggle in the sidebar) keeps a working copy in your browser's local storage that survives page refreshes within the same session. It expires after 24 hours and is not restored after fully closing and reopening the browser. It is not a substitute for a downloaded session file.

Sense Making is an AI-assisted feature that synthesises the analyses you have run into a single coherent interpretation. Instead of reading each result separately, the AI connects your findings — identifying patterns, contradictions, and key takeaways across quantitative and qualitative analyses together.

1. Run any analyses across any modules.
2. On each result, click ➕ Add to Sense Making to promote it to a project.
3. Go to Sense Making in the sidebar and open your project.
4. Optionally add context about your research question and audience.
5. Choose an interpretation mode and click ✨ Interpret project.
6. The AI streams its interpretation. You can then refine it with a plain-language instruction — the interpretation is rewritten and replaces the current version.

A project is a named collection of analyses you have chosen to include in a synthesis. You can have multiple projects — for example, one per research study. Each project stores up to 20 analyses, an optional context description, and your full conversation history with the AI. Projects are private to your account.

There are four modes, selectable per project:

• 🗣️ Plain language — simple words, no jargon, suitable for any audience
• 🎓 Academic — formal tone, precise statistical language, suitable for research reports and papers
• 💼 Executive summary — concise, action-oriented, focused on implications for decision-makers
• 📖 Narrative — flowing prose, suitable for donor reports or public-facing outputs

You can switch modes and re-interpret at any time.

No. The AI only ever sees what you have explicitly added to your project — aggregated result tables, key statistics, auto-generated interpretation text, and any notes you wrote. Your raw dataset is never sent to the AI or stored anywhere.

Up to 20 analyses per project. You will see a warning at 15 and a hard block at 20. If you need more, remove less relevant analyses or create a new project. You can also copy an analysis from one project to another.

Yes. After the initial synthesis, a Refine interpretation panel appears. Type a plain-language instruction — for example, "make this more concise", "focus more on the qualitative findings", "include more participant quotes", or "sharpen the conclusion" — and the interpretation is rewritten to incorporate it. The refined version replaces the original. Refining counts as one interpretation from your monthly allowance. You can refine as many times as you like within your limit.

Yes. Click ⬇ Export to Word in the synthesis view. The exported document includes the project name, interpretation mode, context, and the full AI interpretation.

On the free plan: 1 interpretation per calendar month (each refinement also counts as one interpretation). These reset on the 1st of each month. Your remaining allowance is shown at the top of each project workspace. Paid plans (Starter at ₹200/month and Pro at ₹400/month) offer 10 and 50 interpretations per month respectively.

Full quantitative analysis, full qualitative analysis, plain-English interpretations at every step, all export formats, session saving and restoring — all free. Monthly AI limits apply: 1 Sense Making interpretation, 1 dashboard interpretation, 2 Canvas story generations, and 5 Canvas slide refinements per month. Sharing (public links for dashboards and Data Canvas stories) requires a paid plan. No credit card required, no time limit.

Feature	Free	Starter ₹200/mo	Pro ₹400/mo
Sense Making	1/month	10/month	50/month
Dashboard interpretations	1/month	10/month	50/month
Canvas: Generate Story	2/month	5/month	15/month
Canvas: Refine Slide	5/month	15/month	50/month
Canvas: Old AI tools (headlines, refine chart)	Limited	Generous	High
Sharing (Dashboard, Canvas public links)	—	✓	✓

Enterprise plans with team access and custom limits are available — email support@analyz.solutions.

Paid plans are not yet available via self-service checkout. To express interest and be notified when they launch, email support@analyz.solutions with your preferred plan. Early users receive priority access and a founding price.

On the 1st of each calendar month (UTC). Your remaining allowance is always shown in the Sense Making workspace so you know exactly where you stand.

No — unused allowance does not roll over. The counter resets to the full monthly limit on the 1st of each month regardless of how many you used the previous month.

Every analysis module has export buttons below the results. Tables download as Excel (.xlsx) or Word (.docx). Charts export as PNG, JPG, or interactive HTML. Multi-chart dashboards can be published as a shareable live link (viewable by anyone, no login needed) or downloaded as interactive HTML. Data Canvas stories can also be published as a public link — a scrollable narrative with charts, tables, and quotes. The sidebar offers dataset download in CSV, Excel, Stata, or SPSS format. In Sense Making, click ⬇ Export to Word to download the full AI interpretation and context as a formatted Word document.

Email us at support@analyz.solutions. We aim to respond within 48 hours on business days.

The full User Guide covers every module with step-by-step instructions, reference tables, and tips. It is accessible any time from the Help & FAQ section inside the app or from the Support section of the landing page.