Finnoto AI

Meet DAI.
Money in. Money out.
One brain.

One AI works your entire money pipeline in shifts - reconciling every rupee your channels owe you through the morning, keeping the books continuous at noon, governing every rupee you owe your vendors through the afternoon. Humans own the exceptions. Guards own the money.

money in the books money out
The Revenue Reconcilertying every order to its bank credit
What that does to a business

Not features. A finance
department that compounds.

Revenue stops leaking.

Every settlement reconciled to the order across every channel. Every deduction challenged with evidence instead of resignation.

Spend can't go wrong.

No invoice moves without the contract, the GRN, GSTR-2B, TDS and policy agreeing. Duplicates and fraud die at the gate.

The close stops being an event.

Both sides post themselves, failed ERP entries self-heal, and the books stay audit-ready every single day.

The team compounds.

People do judgment, not data entry - and every correction they make trains DAI to need them less tomorrow.

09:02 - first hat on

Every rupee in,
accounted for.

Marketplace settlements, gateway captures, COD remittances - tied to the exact order and the exact bank credit.

DAI as - The Revenue Reconciler order → cash · every channel matching
Channel payouts - money in
Amazon settlement₹8,41,220
Blinkit payout₹2,10,480
COD remittance₹5,63,900
Razorpay capture₹1,94,315
Zepto payout₹96,410
4,118 orders tied today
99%+ GMV → bank credit
Tied to the business
2,204 orders · fees split
commissions vs rate card
962 UTRs → orders
gateway fees verified
short vs rate card₹18,340 ✗
⚠ Zepto payout short ₹18,340 against the rate card - claim drafted with line-level evidence, filed on your click.
Settlement recon every marketplace, gateway & COD Order recon engine order → cash · inventory → cash · claims Self-improving sharper with every cycle it runs
10:15 - the detective hat

Leakage found.
Fought. Recovered.

Deductions, penalties and chargebacks don't get absorbed - they get investigated.

DAI as - The Recovery Agent deduction desk · claims & disputes investigating
88% invalidWeight claim · ₹42,180our POD says 212kg, not 190
disputingFill-rate penalty · ₹1,08,300appointment data contradicts
validDamage claim · ₹12,540photos check out - posted
judgmentPromo chargeback · ₹64,220contract ambiguous - to a human
Valid - accepted & postedno time wasted fighting real claims
Invalid - disputed with evidencePO, GRN, POD, photos - assembled automatically
Ambiguous - a human decidesfull context · the call trains DAI
hrs

Disputes filed in hours, not quarters. Every evidence packet - the PO, the GRN, the POD, the photographs - is assembled the moment the deduction lands, tracked from filing to credit note.

Deduction intelligence classified, scored, contested Evidence assembly automatic, line-level Recovery tracking dispute → credit note
12:00 - the bow tie

One truth
in the middle.

Both sides of the pipeline meet in the ledger - and the ledger stays current on its own.

DAI as - The Bookkeeper continuous close · both sides posting balancing
Revenue side posts
Sales JE · 4,118 orders
Settlement JE · fees split
Gateway JE · captures
trial balance - current
close-ready, every day
Payables side posts
Purchase JE · INV-2291
TDS JE · 194C ₹8,364
Payment JE · run #221
✗ SAP posting E-1187 failed - GL 400310 missing on line 2 ⟳ DAI diagnosing… mapping corrected, tax split recomputed ✓ re-posted - SAP · Oracle · Tally · Zoho, all in sync
13:15 - under every hat, one rule

It judges with confidence.
Literally.

Whatever hat DAI is wearing, every decision carries a score - and the score decides who decides.

≥95%

DAI acts

Clean documents and exact matches flow straight through - logged, reversible, never silent.

INV-2291 · policy ✓ · 2B ✓ - 99.2% · posted
70–95%

DAI drafts, you click

Disputes, follow-ups and adjustments arrive pre-assembled with evidence - approval is one decision, not one hour.

weight-claim dispute · evidence attached - 86% · one click
<70%

Humans decide

Ambiguity and fraud-sensitive changes route to people with full context - and every call teaches DAI.

vendor bank change · 3.1× utility bill - held for review
14:30 - the inspector's cap

Nothing wrong
gets through.

Every invoice - email, WhatsApp, portal, any language - is read, prefilled and run through the gauntlet before it can touch money.

DAI as - The Gatekeeper channel intake · portal · email · WhatsApp · Slack · Teams · Claude · ChatGPT validating
Email + 3 attachmentsvendor@kova.in · parsed & routed
Invoice · हिंदीDevanagari template · read fine
WhatsApp forwardphoto of a bill · deskewed, read
Rent contract · 14 ppclauses + escalation extracted
MIS workbooksupporting data, tied to invoice
Vendor KYC packGST · PAN · MSME · bank proof
GSTIN 29AAACK…1Z5 ✓ TDS → 194C · fields 98% prefilled ₹4,18,200 · HSN 8471 escalation: 5% yearly MIS ↔ invoice tied ✓ MSME ✓ · bank ✓
Contractterms & rates agree
GRN3-way · goods received
GSTR-2BITC secured · IMS
TDSsection & rate right
Policybudget & approvals
Anomalyduplicates · drift · risk
⚠ Vendor bank change on Kova Packaging - fraud-sensitive, held for human review with full context.
16:00 - the banker's hat

Money moves only
through guards.

DAI stages everything. People release everything. That line never blurs.

DAI as - The Paymaster payment run #221 · guarded execution staging
The run - staged by DAI
64 payouts · ₹1.18Cr - UPI 41 · NEFT 19 · RTGS 4, beneficiaries locked to the vendor master ✓
Payment advice → vendor@kova.in - sent on release, UTR attached
WhatsApp · "When's payment?" → "Released today - UTR 2216…884" ✓
The guards - humans & hard rules
DAI prepares - validated, scheduled, annotated
👤Maker approves - reviews the staged run
👤Checker approves - second pair of eyes
OTP verified - at the moment of release
Released - audit entry written, UTR tracked
DAI never touches the release button. Deterministic where money moves - learned models inform, hard rules and humans execute. No stochastic payments, ever.
Every rail UPI · IMPS · NEFT · RTGS Maker-checker + OTP beneficiary lock to vendor master Auto vendor comms advice, UTRs, queries - answered
17:30 - the scholar's cap

The whole pipeline,
one question away.

DAI speaks MCP - ask it from Claude, ChatGPT, the CLI or plain chat, and it answers with your numbers.

finnoto - mcp · cli · chat
$
MCP & CLI reports + key actions from Claude, ChatGPT, terminal CXO slice & dice by channel, entity, vendor, ageing Auto summarization what changed, what needs a decision
62%

19:00 - same brain, sharper tomorrow
Every correction is training data. Invoice entry is ~98% prepopulated and climbing. Disputes get sharper with every ruling. The recon engine improves with every cycle. The human queue shrinks instead of growing - the longer DAI runs your pipeline, the harder your operation is to compete with.

House rules

AI an auditor
can love.

Deterministic where money moves. Learned models inform; hard rules and human controls execute. No stochastic payments, ever.
Every decision explains itself. Confidence, evidence and the exact rule or pattern behind each action - visible in the trail, not buried in a model.
Humans own judgment. Exceptions, overrides and fraud-sensitive changes always reach a person - and their corrections make the system better.
The trail is the product. Every read, match, judgment and release writes an immutable audit entry - compliance isn't a report, it's a property.
Where this goes

From roles
to autonomy.

Today · live

Role-based agents

DAI already works the pipeline in shifts - reconciler, recovery, bookkeeper, gatekeeper, paymaster, analyst - humans on exceptions, guards on execution.

Next

Agent-run workflows

Whole workflows carried end to end - the dispute from evidence to credit note, the vendor chase from detection to closure - with finance approving outcomes, not steps.

Beyond

Autonomous finance

Finance agents operating on trusted, real-time truth across entities and geographies - the operating layer the vision describes.

FAQ

The questions diligence asks.

Does AI move money on its own?

No. Money movement is deterministic and guarded - maker-checker, OTP release and beneficiary lock to a verified vendor master. AI computes, matches, flags and drafts; execution runs through hard controls, and every step is audit-logged.

Is DAI one model or many agents?

One intelligence layer wearing role-specialised hats - reconciler, recovery agent, bookkeeper, gatekeeper, paymaster, analyst - all operating on the same transaction truth graph. Every role inherits everything the layer has already learned about your business.

What happens to low-confidence decisions?

They route to a human with full context - the document, the match evidence and the reason for doubt, side by side. Corrections feed back into the models, so the human queue shrinks over time instead of growing.

Is our financial data used to train shared models?

Customer data is used to run and improve your own workflows. Cross-customer intelligence is limited to aggregate patterns - never your documents, counterparties or amounts exposed to anyone else.

How accurate is the document AI on Indian invoices?

Field-level extraction runs 96%+ on typical Indian formats - including GST fields, HSN codes, regional templates and multilingual documents - with low-confidence fields flagged for human confirmation rather than silently guessed. Reinforcement learning from corrections pushes auto-prepopulation of invoice data, including TDS and GST, toward 98%.

Put DAI
on your pipeline.

Bring one messy month - settlements, deductions, WhatsApp invoices. Watch both sides come back reconciled.

Talk to us