Every journal entry tested, every finding defensible

ACIES is a multi-tenant platform for financial audit teams that runs deterministic control tests across an entire general ledger instead of a manual sample. It is live in production at aciessoftware.com, engineered by SolutionsCrafters for ACIES Analytics Ltd.

ACIES is owned by ACIES Analytics Ltd. SolutionsCrafters is the engineering partner behind the platform, responsible for its architecture and development. The product is live at aciessoftware.com.

ACIES risk dashboard showing general ledger control-testing results, with flagged journal entries grouped by control point and a drill-down to individual transactions

Context

Audit work is always performed under review. Every conclusion has to survive a file review, a partner sign-off, and sometimes a regulator. ACIES is built for the teams who carry that weight — external auditors, internal audit functions, and financial controllers who need to know what is in the whole ledger, not only in the part they had time to open.

Challenge

A sample can only ever cover a fraction of the ledger, and the risk that matters may sit in the entries nobody selected. The manual testing that follows is slow, difficult to repeat, and hard to evidence months later when the file is reviewed.

03 · Approach

Approach

The controlling decision was to keep every test deterministic. Each control is an explicit SQL rule, so the same ledger always produces the same findings and every flag traces back to the condition that raised it — no scoring a reviewer cannot explain to a partner. SolutionsCrafters built the platform on .NET 8 and React with TypeScript around a strict organisation to engagement to analysis hierarchy, enforced tenant isolation and role-based access at the data layer rather than in the interface, and deployed it on managed cloud infrastructure with separate liveness and readiness checks so capacity follows demand.

System delivered

Around 40 deterministic control points expressed in SQL: weekend and after-hours postings, duplicate entries, round-dollar amounts, and suspicious keyword detection
Testing across 100% of the journal entries in a ledger rather than a selected sample
Excel and CSV ingest with automatic column detection and account mapping
Multi-tenant hierarchy of organisation, engagement, and analysis, with strict tenant isolation and per-organisation role-based access control
Real-time analysis progress streamed over WebSockets, and interactive risk dashboards that drill down to the individual transaction
Audit-ready export to Excel and PDF, with every finding still linked to the rule that produced it
Engagement workflow beyond the analysis itself: risk-mitigation tracking, annotations, scheduled reports, in-app and email notifications, audit logging, and global search

04 · What changed

What changed

01Coverage stops being a sampling judgement: the control set runs against the whole ledger, so exceptions surface wherever they sit
02Findings hold up under review — the same ledger yields the same result, and every flag names the rule behind it
03Testing effort moves from selecting and tick-marking to the judgement work that genuinely requires an auditor
04Workpapers come out of the platform in the form the file expects, instead of being rebuilt in a spreadsheet
05A firm can open an account and run the control set against its own trial balance before committing to anything

Related services

.NET Applications Database Solutions Hosting & Managed Infrastructure

Technology

.NET 8React + TypeScriptSQL control engineSignalR

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Case study · ACIES