Problem Statement

Across India, the same workflow repeats every month in lakhs of business offices and CA firms - internal MIS and statutory workings. Over one million business entities are estimated to spend 25 to 40 hours every month preparing MIS, and the country's 50,000-plus CA firms are estimated to spend 120 to 300 hours every month collating data and preparing statutory workings. Numbers change month to month; the working logic stays substantially the same.

The bottleneck is not the working logic - it is the input. Books of Account are rarely complete enough to feed directly into any output, MIS or statutory. Party-wise breakups, cost-centre tagging, due-date fields and narrations are routinely missing or buried in free-text. AccountingIQ is built to surface this gap before the working starts, not after.

Technology Used

AccountingIQ is a client-side web app on React and TypeScript, hosted within the firm’s monorepo. It consumes Tally XML exports directly (DayBook, Trial Balance, P&L, Balance Sheet, Group Summary) and parses them in the browser - no client data leaves the user's machine for the core scoring engine. The engine is rule-based and deterministic: 58 codified checks across 8 weighted dimensions, producing a transparent score out of 100. An LLM-powered analysis layer sits on top, turning the dimension-level results into plain-English diagnostic narrative and prioritised remediation steps.

Solution / Key Features

1. Layer 1 - Health Score (shipped). Runs all 58 checks on the Tally XML bundle and produces a weighted 0-100 score with grade, broken down by 8 dimensions. Each failed check is mapped to a specific remediation step. An AI layer translates the numeric output into partner-ready narrative.
2. Layer 2 - Gap Highlighter (in build). Takes the desired deliverable as input - currently the monthly MIS pack - and maps the data demanded by that output against what the books actually contain. Every missing data point is flagged at a line-item level, with a clear pointer to the voucher or ledger where the gap exists.
3. Layer 3 - Output Generator (MIS first). Generates the final working in client-share format. The current shipped output is the monthly MIS workbook - a formatted Excel covering P&L summary, balance-sheet snapshot, key ratios and variance commentary, ready to hand to management.
4. Tally-native input. Direct XML ingestion. No mapping templates, no re-keying.
5. Deterministic, traceable scoring. Every score reverse-traces to specific checks; nothing is a black box.
6. Audit-grade flag surfacing. TB mismatches, suspense balances, near-duplicate ledgers, missing PAN/GSTIN, and dozens more.
7. AI diagnostic narrative. LLM-powered interpretation with effort-vs-impact ranked recommendations.
8. Privacy by default. Core parsing and scoring run client-side; the firm controls every byte of client data.

Roadmap. The same architecture is being extended beyond MIS. The check engine and AI layer are decoupled from the output format, so adding a deliverable is a matter of plugging in a new gap-map and generator. Three statutory tracks are in the pipeline: Schedule III financial statements, GST workings (GSTR-1, 3B, 9, 9C) sourced from the same Tally XML, TDS returns with NSDL FVU-compatible output and many more statutory compliance workings.

Impact. Pre-working data clean-up time per client drops by 60-80%. A scored, partner-ready health-and-gap report is produced in under a minute from a Tally export. The CA firm reclaims senior bandwidth that today goes into chasing accountants; the client business gets a structured, repeatable feedback loop on the quality of its own books; and the accountant gets specific, prioritised corrections instead of vague "please clean up Tally" instructions.

Conclusion. The working logic in Indian accounting is largely standardised; the inputs are not. AccountingIQ scores the books first, surfaces the gaps next, and generates the deliverable. Better books make better workings - measurable, visible, fixable