RoboRecon · Works With Your ERP
300 transactions in minutes, not a week of clicking.
The bot extracts your ledgers, the engine matches them, and your finance team sees only the exceptions, with the reason already written.
The Cost Of Manual Reconciliation
For one bank account with 300 transactions, built-in ERP reconciliation is a full week of manual work.
25+ hrs
Per reconciliation cycle
5–10 min
Per transaction in the ERP UI
1 week
Of manual work, every cycle
– With RoboRecon –
REDUCTION
Reduction in reconciliation time
TRANSACTIONS
Reduction in reconciliation time
ALGORITHM · 4 STAGES
Reduction in reconciliation time
How RoboRecon Works · Architecture
Your ERP stays the system of record.
Five steps. The first and the last are your ERP, untouched.
source
Your ERP, system of record
Everything starts and ends in your ERP. RoboRecon never replaces it.
Extract
Auto-extract
Pulls GL and Cash Ledger data and reads bank statements field by field.
match
RoboRecon engine
Each unmatched items escalate through 4 stages, reaching AI only when rules cannot resolve it.
Verify
AI verification
Every match carries a confidence score and a plain-language audit explanation.
Post
Post back to your ERP
High-confidence matches post. Anything below threshold goes to a named finance owner.
Step 03 · the matching engine, 4 stages
Stage 1 · Rules
Deterministic rules: exact, amount/date tolerance, reference, 1:N batch
Stage 2 · Composite
Multi-field, batch receipts, consolidated payments, N:1
Stage 3 · AI Semantic
LLM embedding, abbreviations, reordered references, inconsistent descriptions
Stage 4 · LLM Verify
Confidence scoring on every match
Output Reports
Four sheets, and every row says why.
|
Sheet |
What a row carries |
Disposition |
|
Matched |
Every successfully matched transaction, complete with GL references. |
Auto-posted |
|
Unmatched |
Items that fail to match carry into the next session for review. Nothing is lost. |
Carried forward |
|
Justification |
The reason for each match, written in plain language, on every row. |
Audit trail |
|
Confidence |
A confidence score on every match, so auditors can run risk-based review. |
Below threshold → a person |
Built For Regulated Malaysian Enterprises
On-premise deployment. Local inference. Nothing leaves your environment.
On-premise deployment
Runs inside your own infrastructure, built for government and GLC data-sovereignty requirements.
Local LLM engine
AI inference runs on-site. No external API calls, no data leaving your environment.
Malaysian bank formats
Built-in logic for all major Malaysian bank formats and payment channels.
50+ configurable parameters
Adapts to any bank format, naming convention, or date tolerance, without code changes.
Before / With RoboRecon
Same ledger. Same ERP. One week becomes one run.
BEFORE ROBORECON
Navigate the ERP page-by-page per transaction
5–10 minutes each · 25+ hours per cycle
Key bank-statement fields lost on import
Matching logic fixed
WITH ROBORECON
Extracts and matches automatically
~300 transactions in minutes
All bank-statement fields preserved
Client-specific rules configurable without code
Client-specific rules configurable without code
Confidence score on every row
Plain-language match reason
Unmatched items carry forward, never dropped
Start with a pilot. See RoboRecon on your own data.
Book a 30-minute session and we will run RoboRecon against a sample of your reconciliation, your data, your ERP, your environment.