Platforms · FIN fabric

Close the books on what actually happened.

A financial intelligence platform built for finance leaders — AI that reads what arrives, reconciliation engines that match it line by line, and a route straight into Tally.

01 — Who it is for

Built for the finance leader, not the IT roadmap

FIN fabric was designed around the questions a CFO actually gets asked at short notice — what is our exposure, why does this account not tie out, when will we close — and around the fact that answering them usually means someone rebuilding a reconciliation by hand.

Month-end as it usually runs

Reconstruction, under time pressure

Statements downloaded, spreadsheets built, ledgers compared by eye. The exceptions surface in the last two days, and the explanation is written from memory rather than from a record.

Reconciliations rebuilt from scratch each cycle
Errors found late, when there is least time to fix them
No trace of what was checked or by whom
Insight arrives after the decision needed it

FIN fabric

Continuous, with the exceptions surfaced early

Documents are read as they arrive, matched against your registers automatically, and only the genuine breaks reach a person. Close becomes a review rather than an investigation.

Reconciliations run continuously, not at cut-off
Exceptions raised the day they occur
Full trace of what matched, what did not, and why
Analytics drawn from the same data, not a separate extract

02 — Reconciliation engines

Proven engines, now with AI reading the inputs

Our reconciliation engines have been running in production for years. What changed is the front of the process: an AI layer now ingests the documents — scanned statements, PDF reports, vendor formats that vary by counterparty — so reconciliation no longer waits for someone to key the data in.

1Documents arrivestatements, reports, returns, in any format

2AI reads and classifiesfields extracted, document type identified

3Engine reconcilesline by line against your register

4Exceptions raisedonly the genuine breaks reach a person

Engines available today

Marketplace reconciliation — Amazon, FlipkartPayment gateway — PayU, RazorpayBank reconciliationGSTR2B against purchase registerTDS receivables against Form 26ASESI & EPF for contract staffVendor statement reconciliationStock and inventory

A few examples. Where your reconciliation is not on this list, the engine is configured to it rather than rebuilt.

Intelligent matching

Partial matches, split settlements and timing differences handled as rules rather than as manual judgement each cycle.

Charges verified

Commissions, fees and deductions levied by aggregators and gateways checked against what was agreed, order by order.

Audit-ready output

Every match, break and resolution retained with its evidence, so the reconciliation is the audit trail.

03 — Tally integration

Data entry that does not need a person

For most Indian finance teams the bottleneck is not analysis, it is entry. Bank statements and supplier invoices arrive faster than anyone can key them. FIN fabric reads both and posts them into Tally directly.

Bank statementsStatements read line by line — narration, value date, amount, instrument — and posted as vouchers with the ledger already identified.

Supplier invoicesHeader and line-item extraction across vendor formats, with tax, PO reference and cost allocation captured for posting.

Ledger intelligenceRecurring narrations learned over time, so the same counterparty maps to the same ledger without being told twice.

Exceptions, not silenceAnything ambiguous is held for review rather than posted on a guess. The team checks a short queue instead of everything.

04 — Analytics and insight

The numbers, and what they mean

Once the data is clean and current, analysis stops being a monthly exercise. FIN fabric reads the same reconciled ledger it maintains.

Financial statement analytics

Ratio, trend and variance analysis across the statements, with the movement explained against the underlying transactions rather than described in the abstract.

Risk assessment

Concentration, ageing, exposure and covenant positions surfaced continuously, so a deterioration is visible while it is still small.

AI-powered CFO insights

Ask a question in plain language and get the answer from your own ledger, with the transactions behind it attached.

Audit intelligence

Sampling, exception testing and completeness checks run against the full population rather than a selection.

05 — What changes

For the person who signs the accounts

A shorter closeReconciliations are already done when the period ends, because they ran while it was open.

Fewer surprisesBreaks appear the day they happen rather than in the final week of the cycle.

Less keyingStatements and invoices reach the ledger without passing through a keyboard.

Evidence on demandEvery figure traceable to the document behind it, for auditors and for anyone who asks.

Capacity releasedThe team spends its month on judgement and analysis rather than on matching rows.

Scale without headcountVolume growth handled by configuration, not by hiring another pair of hands each year.

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