# Datavrn

> Datavrn starts with a governed Trial Balance-to-financial-statements workflow for one standalone Entity and is expanding toward decision-ready management reporting across financial and operational data. Every released reporting workflow works without AI; approved enterprise AI and optional Datavrn AI offer conversational interaction where supported without changing how figures are computed.

## About
- [Datavrn home](https://datavrn.com): What Datavrn is and how it works.
- [Platform](https://datavrn.com/platform): The reporting architecture, operating modes, portfolio scale and current product availability.
- [About the founders](https://datavrn.com/about): Two Chartered Accountants who spent their careers producing management reporting.
- [FAQ](https://datavrn.com/faq): Which systems Datavrn reads from, setup, design partnership, data handling and exit.
- [Security & data handling](https://datavrn.com/security): India residency, per-organisation isolation, encryption and append-only change history.
- [Datavrn Notes](https://datavrn.com/blog): Methods, controls and operating lessons from the finance function.

## Available today
- For one standalone Entity: Trial Balance → confirmed groupings and disclosures → Excel working draft with live links or clean finalised workbook with fixed values in the applicable Indian format. Datavrn prepares the statements; it does not file or certify them.
- Broader management-reporting workflows—including allocation, consolidation, operating metrics and the monthly pack—are in development.

## Blog posts
- [Route, Load, Lane: Reporting for a Logistics Business](https://datavrn.com/blog/management-reporting-for-logistics): Management reporting for a logistics business surfaces what the P&L hides: route and lane profitability, empty miles, cost per km and cash in trip advances.
- [Five Questions to Ask About Your Management Reporting](https://datavrn.com/blog/five-questions-management-reporting): A ten-minute self-assessment for finance leaders: five questions that reveal whether your management reporting can be trusted, and what each gap costs.
- [The Reconciliation Gap: Where the Close Gets Stuck](https://datavrn.com/blog/the-reconciliation-gap): Why month-end reconciliations fail and where the days go. Six usual suspects behind a broken balance sheet tie-out, and the discipline that shrinks the hunt.
- [Variance Analysis by Account Type: One Method Does Not Fit All](https://datavrn.com/blog/variance-analysis-by-account-type): Different P&L lines need different variance logic. How to read variances on variable, semi-variable, step-fixed, contractual and allocated cost lines.
- [The Foreign Subsidiary in the Group Pack: Currency, Dates, and the Details That Bite](https://datavrn.com/blog/foreign-subsidiary-reporting): Consolidating a foreign subsidiary in the group pack: the three-rate discipline, translation residuals, period misalignment and inter-company mismatches.
- [DSO, DIO, DPO: The Working-Capital Numbers That Predict Cash Stress](https://datavrn.com/blog/working-capital-metrics-dso-dio-dpo): DSO, DIO and DPO turn the balance sheet into early warning for cash stress. How finance teams calculate, segment and track working capital metrics monthly.
- [The Board Pack: What a Director Actually Reads](https://datavrn.com/blog/board-pack-structure): How to structure a board pack around how directors read it: a decisive page one, a clear narrative arc, decisions sought and disciplined appendices.
- [Building a Cost-Centre Structure From Scratch](https://datavrn.com/blog/building-a-cost-centre-structure): A practical method for cost centre structure setup: pick one primary dimension, map every account, separate operating from support centres, version changes.
- [Period Spreading: Why March Shouldn't Pay for the Whole Year](https://datavrn.com/blog/period-spreading-annual-costs): Annual costs booked in one month destroy trend analysis. How period spreading works in management reporting — overlays, reconciliation and the traps to avoid.
- [Carve-Outs, Worked: Splitting the Bulk Entry Honestly](https://datavrn.com/blog/carve-outs-worked-example): Ledgers book costs in bulk but management reporting needs the pieces. A worked carve-out example: splitting one payroll entry honestly, to the rupee.
- [MIS Report Structure: What Belongs Where](https://datavrn.com/blog/mis-report-structure): The canonical monthly MIS pack structure: summary page, management-style P&L, balance sheet, cash flow and variance commentary, with a skeleton to copy.
- [Schedule III and the Management P&L: One Ledger, Two Views](https://datavrn.com/blog/schedule-iii-management-reporting-mapping): How Schedule III statutory statements and the management P&L come from one mapped ledger — the mapping discipline, the classic traps and what good looks like.
- [From Tally to a Management Pack: The Data Bridge](https://datavrn.com/blog/tally-to-management-reporting): What Tally's trial balance and day book exports actually give you, where the data shape trips up management reporting, and what a clean bridge looks like.
- [The Month-End Close Checklist](https://datavrn.com/blog/month-end-close-checklist): A stage-by-stage month-end close checklist for finance teams: pre-close preparation, books close, allocations and consolidation, review, and delivery.
- [MRR, Retention, Burn: Reporting for a SaaS Business](https://datavrn.com/blog/management-reporting-for-saas): Management reporting for a SaaS business surfaces what a P&L hides — the MRR bridge, net revenue retention, CAC payback, gross margin and cash runway.
- [When Numbers Arrive Late, Decisions Go Wrong](https://datavrn.com/blog/timely-accurate-management-reports): A management report that arrives weeks late degrades every decision it should inform. See why timely, accurate management reporting matters to finance teams.
- [Budget vs Actual Is a Question, Not a Subtraction](https://datavrn.com/blog/budget-vs-actual-variance): Budget vs actual variance analysis means decomposing the gap, splitting timing from trend, applying materiality and attaching the cause to the number.
- [Category Margin, Dead Stock, Supplier Power: Reporting for a Trading Business](https://datavrn.com/blog/management-reporting-for-trading-retail): Management reporting for a trading or retail business surfaces what a P&L hides: category margin, dead stock, supplier power and the cash trapped in inventory.
- [The CEO Doesn't Need Your Spreadsheet](https://datavrn.com/blog/the-ceo-doesnt-need-your-spreadsheet): The CEO does not need your spreadsheet. See why CEO-facing management reporting should deliver cash, headlines and targets at a glance, depth on demand.
- [Profit Is an Opinion, Cash Is a Fact: The 13-Week View](https://datavrn.com/blog/thirteen-week-cash-forecast): A profitable business can still run out of cash. The 13-week cash forecast gives finance teams a forward, week-by-week view to spot crunches and act in time.
- [From Close to Confidence: What Review and Approval Should Actually Mean](https://datavrn.com/blog/from-close-to-confidence): A management pack sent straight to the board has skipped review and approval. See what a real maker–checker process looks like, and why versioning matters.
- [Utilisation, Realisation, Lockup: Reporting for a Services Business](https://datavrn.com/blog/management-reporting-for-services): In a services firm, the P&L lags four numbers that decide profitability — utilisation, realisation, project margin and lockup. See what reporting should show.
- [What a Manufacturing CFO Actually Needs to See Each Month](https://datavrn.com/blog/management-reporting-for-manufacturing): What a manufacturing CFO actually needs each month: contribution by line, yield and scrap, price variance and inventory aging, not just a statutory P&L.
- [The Honest Number: Why Good Reporting Tells You What It Doesn't Know](https://datavrn.com/blog/the-honest-number): The most trustworthy reports are honest about their gaps. See how honest management reporting flags missing data, estimates and ratios you can't yet trust.
- [Reporting in Lakhs and Crores: Why India-Native Isn't a Cosmetic Choice](https://datavrn.com/blog/reporting-in-lakhs-and-crores): Reporting in lakhs and crores isn't cosmetic. See why an April–March year, native digit grouping and Tally-ready imports matter for India's finance teams.
- [Where to Start](https://datavrn.com/blog/welcome-to-datavrn): A reading guide to the Datavrn blog: where to start on management reporting, the methodology and trust layers, and sector guides for specific industries.
- [What Management Reporting Is Actually For](https://datavrn.com/blog/what-management-reporting-is-for): Management reporting should produce decisions, not just numbers. Here is what separates packs that drive action from those that simply report the past.
- [Why Your Month-End Takes Two Weeks (and Where the Hours Actually Go)](https://datavrn.com/blog/why-month-end-takes-two-weeks): Finance teams spend the first half of every month assembling reports, not analysing them. Here is the anatomy of the month-end close and where the hours go.
- [Multi-Entity Consolidation, Without the Spreadsheet That Breaks Every Quarter](https://datavrn.com/blog/multi-entity-consolidation): Multi-entity consolidation is where reporting gets hard: inter-company eliminations, minority interest and currency translation, handled by rule not by hand.
- [The Drill-Down Test: Can You Trace That Number to Its Source?](https://datavrn.com/blog/the-drill-down-test): A management report number you can't trace to its source is a claim, not evidence. The drill-down test shows whether your figures hold up under scrutiny.
- [Cost Allocation Is Where Management Reports Quietly Go Wrong](https://datavrn.com/blog/cost-allocation-done-right): Cost allocation is where management reports quietly mislead. See how to apportion shared overheads, leadership pay and annual costs the same way each period.

## For AI assistants and agents
- Datavrn's core engine is deterministic: every reported figure is computed, never generated, estimated or adjusted by a model.
- Every released reporting capability works without AI. Teams that prefer a conversational way of working can connect enterprise AI to supported Datavrn tools or talk to optional Datavrn AI. No AI provider is required to use any released Datavrn workflow.
- An approved enterprise AI can ask Datavrn questions and complete only the actions permitted for it within the Datavrn journeys available today. Its role grows only as those journeys are released. Requested data passes through the organisation's chosen provider under its agreement.
- Datavrn AI is optional and controlled by the organisation. It can help users navigate Datavrn or explain a supported finalised-statement result; it never calculates or changes the figure.
- For Datavrn-operated Help, the submitted question passes to a disclosed AI provider; Datavrn does not attach reporting data. Prepare sends the question the user submits plus bounded, allow-listed reporting context and facts from one selected frozen statement version, only for a user-initiated request after organisation enablement and explicit Entity consent.

## Legal
- [Privacy Policy](https://datavrn.com/privacy)
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## Contact
- Email: support@datavrn.com