Practical AI automationFinance · Documents · Operations
We turn repetitive finance and operational work into controlled AI workflows—with human review, source-level traceability and a clear handoff.
Start with one source, one decision path and one reviewable output.
Nothing consequential happens silently. Every exception remains visible.
Synthetic-data proofSafe before live integration
Human approvalAt consequential decisions
Source traceabilityOutputs link back to inputs
Duplicate controlRepeatable, idempotent imports
What we build
Tool choice follows the process. The standard stays the same: controlled inputs, explicit rules, visible exceptions and evidence behind every output.
CURRENT VIEW / 01
Bring bank exports, sales, receivables, payables, budgets and project data into one reviewable management view.
Working proof / 01
An interactive demonstrator showing how management reporting can stay explainable—from executive KPI to the underlying source row.
Synthetic company data · No client information
The system uses a fictional company and synthetic six-month dataset. It proves interaction design, control logic and traceability—not a production deployment or promised customer outcome.
Anatomy of a dependable workflow
Files, forms, exports, email or API events enter through a defined boundary.
Input loggedData is extracted, normalised and mapped without hiding the original source.
Source preservedRules catch duplicates, missing values, mismatches and low-confidence results.
Controls appliedA person reviews consequential or ambiguous cases instead of letting AI guess.
Human authorityApproved output reaches the report, system or next workflow with an audit trail.
Result recordedHow an engagement starts
We begin with a narrow operational slice. That makes accuracy, edge cases and maintenance visible before anyone commits to a wider implementation.
Build a process brief ↘One process, current tools, inputs, decisions and desired output.
A bounded proof using synthetic, test or properly anonymised data.
You test the workflow, exceptions and evidence before production scope.
Only then: integrations, deployment, documentation and support boundaries.
Operating principles
Low-confidence or ambiguous items go to review with the reason visible.
Repeatable imports and stable identifiers protect the workflow from retries.
Money, records and external communication stay behind explicit approval rules.
Logs, error paths and update instructions are part of the deliverable.
Why Row & Rule
This practice is led by Viktar, combining more than twenty years of financial and management work with hands-on AI-assisted system building.
The goal is not to add AI everywhere. It is to understand where work repeats, where decisions carry risk and where a small controlled system can produce a useful result.
Start with one process
Create a structured starting brief in your browser. Nothing is transmitted unless you choose to email it.
Your draft brief
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Questions before the first proof
A small, bounded proof covering one source, one decision path and one reviewable output. A no-cost slice may be offered for a suitable process; integrations and production work are scoped separately.
No. The first proof should use synthetic, test or properly anonymised data whenever possible. Access to live systems comes only after the workflow and safeguards are agreed.
Not where the consequence is material. The workflow can prepare, classify and recommend, while approval remains with an authorised person.
Yes. The default collaboration model is written and asynchronous: structured questions, review links, decision logs and documented handoff.
The stack follows the process, existing systems, security boundary and maintenance needs. The service is not tied to one automation platform.