Skip to content
LedgerLens

Financial reconciliation · Early access

Every naira on the statement, accounted for.

LedgerLens reads your bank statements, invoices and receipts, matches each transaction to its record, and shows you the duplicate payments, missing entries and wrong amounts — with a plain-language reason for each — before month-end close.

Request Early Access Explore the Demo

In development — onboarding early-access businesses and accounting firms.

Operating account ••4471 · Oct 2026

Sample data
Bank statementStatusYour records

  • POS · Shoprite Ikeja

    ₦48,200

    Matched

    RCPT-1182

    ₦48,200

  • TRF · Brightline Logistics

    ₦1,250,000

    Amount differs

    INV-0419

    ₦1,205,000

  • TRF · Kola Distribution

    ₦310,000

    Matched

    INV-0422

    ₦310,000

  • TRF · Kola Distribution

    ₦310,000

    Duplicate

    Already paid

    INV-0422

  • SMS / maintenance

    ₦2,150

    Missing entry

    No ledger entry

    —

Explanation

The Brightline transfer is ₦45,000 above INV-0419. That equals the waybill surcharge on the delivery note, which was never invoiced. Ask the supplier for a revised invoice before posting.

Matches
Bank lines to invoices, receipts and payment records
Detects
Duplicates, missing entries, wrong amounts, odd patterns
Extracts
Totals, dates, VAT and references from documents
Explains
Why each item was flagged, in plain language

§01  The problem

Reconciliation still runs on spreadsheets, eyesight and the last week of the month.

Today

  • Line-by-line ticking. A bookkeeper exports a 200-line statement, opens the invoice folder and matches by amount and memory.
  • Errors hide in volume. A ₦310,000 supplier invoice paid twice, a ₦275,500 transfer against a ₦257,500 receipt, a bank charge never posted.
  • Found too late. Mistakes surface at audit, in a tax filing, or when a supplier refuses to refund — weeks after the money left.
  • It doesn’t scale. Every new account, client or sales channel adds hours of manual matching to the same small team.

With LedgerLens

  • Upload once. Statements, invoices and receipts go into one workspace; fields are extracted automatically.
  • Matching happens first. Clean matches clear themselves, so people only look at what doesn’t add up.
  • Every flag has a reason. “Same payee, same invoice, four days apart” — not just a red cell.
  • Close with evidence. Approvals, disputes and notes are kept with each transaction for your accountant or auditor.

§02  Core features

Six capabilities, one reconciliation workflow.

From the document upload to the reviewer’s sign-off, each feature hands its output to the next.

01

Intelligent Transaction Matching

Pairs each bank line with the invoice, receipt or payment record it belongs to using amount, date, payee and reference — including partial and split payments.

Clean matches clear automatically, removing most of the manual ticking.

TRF Kola Dist.₦310,000
INV-0422 ✓99% match
02

AI Discrepancy Detection

Flags duplicate payments, missing transactions, incorrect amounts and payment patterns that break from a business’s normal behaviour.

Catch overpayments while there is still time to recover them.

Duplicate payment
Amount differs ₦18,000
Unusual merchant
03

Automated Document Extraction

Reads invoices, receipts and bank statements — PDFs or photos — and pulls out totals, dates, VAT, line items and reference numbers.

No retyping; every figure links back to its source document.

Invoice
INV-0419
Date
01 Oct
VAT
₦84,070
Total
₦1,205,000
04

Reconciliation Dashboard

One view of matched, unmatched and disputed transactions per account and period, with progress toward close.

Know exactly what is left before month-end.

78% matched14% open8% disputed
05

Financial Anomaly Reports

Periodic reports summarising unusual transactions, risk patterns and unresolved items, with explanations a non-accountant can read.

Give owners and auditors a clear record of what was found and done.

October anomaly summary

06

Review Queue

Flagged items are routed to a reviewer who can approve, dispute or annotate them; every decision is logged against the transaction.

Accountability without email threads.

TX-10240Approve
TX-10255Disputed
TX-10274Ask cardholder

§03  How it works

From a folder of PDFs to a closed month in four steps.

  1. Step 1

    Submit records

    Upload bank statements, invoices and receipts, or connect them through the API from your accounting system.

  2. Step 2

    Extract and validate

    Document extraction reads every field; totals, dates and references are checked for completeness.

  3. Step 3

    Match and detect

    Transactions are matched to records. What doesn’t match is classified: duplicate, missing, wrong amount or unusual.

  4. Step 4

    Review and close

    Reviewers approve or dispute each flag. The period closes with a full audit trail and an anomaly report.

§04  Interactive demo

Close a month in the sample workspace.

Search transactions, open a record, approve or dispute flagged items and inspect extracted document fields. Everything below is fictional sample data.

Harmattan Print Co.
Sample data

Matched

8

Needs review

3

Unmatched

2

Value under review

₦1,117,150

Transactions processed by week

MatchedOpen

Insights

  • Two payments to Kola Distribution reference the same invoice (₦310,000). Recoverable if confirmed.
  • Paperworks NG transfer exceeds its receipt by ₦18,000 — digits 57/75 look transposed.
  • One POS charge from an unrecognised merchant on a Sunday with no receipt.

Activity

  • 09:42LedgerLens Extracted 31 fields from INV-0419 (Brightline Logistics)
  • 09:44LedgerLens Flagged TX-10240 as a possible duplicate payment
  • 10:05Tolu A. (reviewer) Disputed TX-10255 — awaiting supplier response
  • 10:21LedgerLens Matched 12 transactions to receipts batch #18
  • 11:02Tolu A. (reviewer) Approved bank fee posting for September

§05  Who it’s for

Built for the people who sign off the books.

SMEs & retail / e-commerce

ProblemCard, POS and transfer payments arrive in volume and rarely carry the invoice number.

How they use itUpload the month’s statement and receipts; LedgerLens matches POS and transfer lines to sales and supplier records.

BenefitDuplicate supplier payments and unposted bank charges are caught before they compound.

Accounting & bookkeeping firms

ProblemEach client means another statement, another folder of receipts and another evening of ticking.

How they use itOne workspace per client; reviewers work a single queue of flagged items across all of them.

BenefitMore clients closed per month with the same team, and a decision trail for every adjustment.

Financial consultants

ProblemEngagements start with messy, incomplete records and a short deadline.

How they use itIngest historical statements and documents to surface gaps, duplicates and unusual patterns quickly.

BenefitStart the engagement with an anomaly report rather than a spreadsheet.

Finance teams in growing companies

ProblemMultiple accounts, approvers and payment runs outgrow manual reconciliation.

How they use itConnect accounting systems through the API and route exceptions to the right approver.

BenefitFaster month-end close with fewer surprises in audit.

§06  Technology

How We Use Amazon Web Services

LedgerLens is being built on a serverless AWS architecture: documents land in Amazon S3, Amazon Textract turns them into structured fields, AWS Lambda runs matching and discrepancy checks, Amazon DynamoDB keeps the reconciliation record, and Amazon Bedrock explains what was found — all exposed through Amazon API Gateway.

  1. 01

    Data Collection

    Amazon API Gateway

    Receives uploads and accounting-system data

  2. 02

    Secure Storage

    Amazon S3

    Encrypted document storage

  3. 03

    Processing

    Amazon Textract · AWS Lambda

    Extraction and transaction matching

  4. 04

    AI Intelligence

    Amazon Bedrock

    Anomaly explanations and reports

  5. 05

    Application Experience

    Amazon DynamoDB

    Dashboard, review queue and audit trail

  • Amazon S3: Stores uploaded bank statements, invoices and receipts in encrypted, access-controlled buckets.
  • Amazon Textract: Extracts totals, dates, VAT, line items and references from invoices, receipts and bank statements.
  • AWS Lambda: Runs the matching engine and reconciliation workflows each time new documents or transactions arrive.
  • Amazon DynamoDB: Holds transaction records, match results, review decisions and customer configurations.
  • Amazon Bedrock: Generates plain-language explanations for flagged anomalies and drafts the period anomaly report.
  • Amazon API Gateway: Exposes secure APIs between the LedgerLens web app, customers and external accounting systems.

§07  Early access

Help shape LedgerLens before launch.

LedgerLens is in active development. We are working with a small group of businesses and accounting firms to test matching rules against real reconciliation workflows.

  • Guided walkthrough of the product with the founding team
  • Input on matching rules, flags and reports for your industry
  • Early-access onboarding when the platform opens

§08  Pricing

Simple monthly plans in naira.

Plans apply when the platform opens to early-access customers.

Starter

₦5,000/ month

For owner-run businesses reconciling one bank account each month.

  • Bank statement, invoice and receipt upload
  • Automatic transaction matching
  • Discrepancy flags with explanations
  • Monthly reconciliation summary
Request Early Access

Growth

₦10,000/ month

For growing finance teams handling several accounts and higher document volume.

  • Everything in Starter
  • Multiple bank accounts
  • Review queue with approve / dispute workflow
  • Financial anomaly reports
Request Early Access

Enterprise

₦30,000/ month

For accounting firms and finance departments reconciling across many entities.

  • Everything in Growth
  • Multi-client / multi-entity workspaces
  • API access for accounting systems
  • Priority onboarding with the founding team
Request Early Access

§09  Team

The Team Behind LedgerLens

Peace Lasisi

Founder

Peace founded LedgerLens in 2026 to give small businesses and the accountants who serve them a faster, evidence-backed way to reconcile their records. As founder, Peace leads the company and the development of the LedgerLens platform.

§10  Contact

Talk to the team.

Questions about the product, partnerships or the early-access program.

peace@ledgerlens.siteLedgerLens on LinkedIn