Data Audit
Map every data source—emails, Sage,
Google Analytics, machines, POS systems.
Understand formats, volumes, and quality.
From data audit to AI-powered automation—our full data product lifecycle
for building custom solutions tailored to your data sources and workflows.
Most automation projects fail because they don't start with the data.
We start with a data audit, understand your data sources, build proper governance, ensure quality, and then automate workflows with AI and custom logic. The result? Automation that actually works with your data—not generic software that requires you to change your processes.
Off-the-shelf software assumes your data is clean, structured, and standardized. But real data isn't like that. Invoices arrive in different formats. Statements come from multiple sources. Machine data uses proprietary protocols. Google Analytics, Sage, email attachments, POS systems—every data source has its own structure, quality issues, and integration requirements.
That's why we start with the data, not the software.
We build custom automation solutions using a full data product lifecycle—from audit and strategy to governance, quality management, ingestion pipelines, AI-powered processing, and compliance-ready lineage. Every solution is built around your data sources, your workflows, and your business requirements.
You can't automate what you don't understand. We start by auditing your data sources, assessing quality, and designing a strategy that works with your existing systems—not against them.
Map every data source—emails, Sage,
Google Analytics, machines, POS systems.
Understand formats, volumes, and quality.
Design the data architecture, define
ingestion pipelines, plan transformations,
and establish governance frameworks.
Establish data ownership, access controls,
compliance tracking (POPIA, Basel), and
complete data lineage for audit trails.
Before writing a single line of code, we conduct a comprehensive data audit. We map every data source in your workflow—invoices arriving via email, statements from banks and suppliers, transactions from Sage or QuickBooks, machine data from PLCs, analytics from Google Analytics, POS system data, payroll files, and any other sources feeding your business processes.
For each source, we assess: data format (PDF, CSV, API, industrial protocol), volume (transactions per day/month), quality (completeness, accuracy, consistency), and integration complexity (APIs available, authentication requirements, rate limits). This audit reveals the gaps, inconsistencies, and quality issues that would break generic automation.
Example: For invoice automation, we don't just count invoices. We analyze invoice formats (scanned PDFs vs structured invoices), sender patterns (how vendors send invoices), validation requirements (PO matching, GL code rules), approval complexity (multi-level workflows, amount thresholds), and ERP integration constraints (API capabilities, field mappings, batch processing limits).
Once we understand your data sources, we build the ingestion pipelines, transformation logic, and quality checks that turn messy data into automation-ready data.
Monitor email inboxes, pull from APIs,
connect to Sage/ERP systems, capture
machine data, and ingest Google Analytics.
Remove duplicate files (hash checking),
validate data against business rules,
enrich missing fields, and flag anomalies.
Track data from source to destination.
Document transformations. Maintain
compliance trails (POPIA, Basel, IFRS).
Data ingestion isn't about downloading files. It's about building resilient pipelines that handle variability, errors, and scale. We build ingestion pipelines that monitor email inboxes (capturing invoices, statements, and documents as they arrive), connect to APIs (Sage, Xero, QuickBooks, Google Analytics, banking systems), process files (PDFs, CSVs, Excel, XML), and interface with machines (PLC data via OPC UA, Modbus, or custom protocols).
Every ingestion pipeline includes: duplicate detection using content hashing (prevents reprocessing the same invoice or statement), format validation (ensures data matches expected schema), error handling (logs failures, retries with backoff, alerts on persistent issues), and data lineage tracking (records where data came from, when, and what transformations were applied).
Data Quality Controls: We implement validation rules that check for required fields, reasonable value ranges, referential integrity (do vendor codes exist in master data?), and business logic (does invoice date fall within fiscal period?). Quality issues are flagged immediately, not discovered weeks later during month-end close.
With clean data pipelines in place, we implement AI-powered workflows, establish KPIs, build monitoring dashboards, and ensure compliance—all running on your custom data.
Train custom AI models for invoice
reading, receipt classification, transaction
categorization—on your data, your formats.
Track data quality metrics, automation
performance, processing volumes, and
error rates—all visible in real-time.
Complete audit trails, data lineage,
access controls, and compliance tracking
for POPIA, Basel, IFRS, and other regulations.
Here's how our data product lifecycle works in practice, using accounting automation as the example.
We map all financial data sources: invoices arriving via email from 50+ vendors (formats: PDF scans, structured PDFs, Excel), bank statements from 3 banks (downloaded PDFs, API feeds), supplier statements (email PDFs, supplier portals), Sage ERP data (API integration), credit card transactions (CSV exports), and Google Analytics (to track vendor portal usage later).
We assess data quality: 30% of invoices are poor-quality scans requiring advanced OCR, 15% lack purchase order numbers (requiring manual approval routing), vendor names are inconsistent (same vendor, multiple name variations), and Sage GL codes don't always align with invoice line items (requiring mapping rules).
Based on the audit, we design the data architecture: centralized invoice inbox (email monitoring + supplier portal), OCR pipeline with custom models trained on client's invoice formats, validation layer that checks PO matching, GL code rules, and vendor master data, approval workflow engine that routes based on amount thresholds and department rules, and ERP integration layer that syncs approved invoices to Sage via API.
We establish governance frameworks: data ownership (finance owns invoice data, IT owns integration infrastructure), access controls (who can approve invoices, who can modify workflows), data retention policies (invoices retained for 7 years per IFRS requirements), and compliance tracking (POPIA for vendor data privacy, audit trail requirements for external auditors).
We build ingestion pipelines that monitor email inboxes every 5 minutes (capturing invoices as they arrive), calculate file hashes to detect duplicates (prevents reprocessing the same invoice), extract attachments and metadata (sender, subject line, timestamp), and queue files for OCR processing.
For structured data sources (Sage, bank feeds), we implement API integrations with proper authentication, error handling, and rate limiting. For Google Analytics, we connect via API to pull traffic and conversion data (used later for vendor portal optimization).
Every invoice passes through quality checks: OCR confidence scoring (low-confidence fields flagged for manual review), vendor name normalization (maps "ABC Ltd" and "ABC Limited" to same vendor), PO matching validation (checks if PO exists, matches amount, not already fully invoiced), GL code validation (ensures codes exist in chart of accounts), and duplicate invoice detection (checks invoice number + vendor combinations).
We build data quality dashboards that show: daily invoice volume, OCR accuracy rates, validation failure rates by type, duplicate detection statistics, and processing time metrics. Finance teams see data quality in real-time, not weeks later.
We track complete data lineage: source (email, sender, timestamp), transformations (OCR applied, validation rules executed, enrichment performed), approvals (who approved, when, approval reason), and destination (Sage invoice number, posting date, GL accounts affected).
This lineage supports compliance requirements: POPIA (vendor data privacy, consent tracking, data retention), Basel II/III (for financial institutions requiring complete transaction audit trails), IFRS (invoice retention and audit requirements), and internal audit (demonstrating control effectiveness and segregation of duties).
Generic OCR doesn't work well on real invoices—poor scans, handwritten notes, non-standard layouts. We train custom AI models on your actual invoice data: vendor-specific templates (learns the layout of invoices from your top vendors), line item extraction (identifies and extracts itemized charges, quantities, unit prices), field extraction with context (distinguishes invoice total from subtotal, tax amounts, discounts), and confidence scoring (flags uncertain extractions for manual review).
For invoice and receipt classification, we build models that categorize documents by type (invoice vs receipt vs statement vs purchase order), vendor (automatically identifies and tags vendor from document content), GL code prediction (suggests correct GL codes based on line item descriptions and historical patterns), and priority classification (flags urgent invoices, high-value transactions, or regulatory-sensitive items).
These models are trained on your custom data—not generic datasets. They learn your vendor patterns, your GL code structure, and your business rules. As they process more invoices, accuracy improves continuously.
We establish KPIs that measure data product performance: Data Quality KPIs — OCR accuracy rate, validation failure rate, duplicate detection rate, missing field percentage. Automation KPIs — Invoices processed automatically (vs manually), average processing time per invoice, exception rate (invoices requiring manual intervention), approval cycle time. Business Impact KPIs — Time saved per month, cost per invoice processed, month-end close time reduction, error rate reduction.
These KPIs feed into real-time dashboards that show: current processing queue status, data quality trends over time, automation performance vs manual baseline, exception volumes by category, and cost savings realized. Finance leadership sees the impact immediately, not in quarterly reports.
Data products aren't "deploy and forget." We continuously optimize: retrain AI models as new invoice formats appear, tune validation rules based on exception patterns, optimize ingestion pipelines for performance, and expand to additional data sources (new vendors, additional bank accounts, new Sage modules).
The data product evolves with your business. As you add vendors, change processes, or expand to new entities, the automation adapts—because it's built on a flexible data architecture, not rigid software.
Robust data ingestion handles the reality of enterprise data: invoices arrive via email (from Gmail, Outlook, vendor portals), files come in multiple formats (PDF, Excel, CSV, XML, images), data sources use different protocols (REST APIs for Sage, OPC UA for machines, SMTP for email, OAuth for Google Analytics), and volumes vary (100 invoices one day, 500 the next).
We build ingestion pipelines with: content-based deduplication using SHA-256 hashing (prevents processing the same file twice even if renamed), retry logic with exponential backoff (handles temporary failures gracefully), monitoring and alerting (notifies when ingestion fails or performance degrades), and scalable architecture (handles volume spikes without performance degradation).
Automation is only as good as the data feeding it. We implement multi-layer data quality controls: schema validation (ensures data matches expected structure), business rule validation (checks domain-specific requirements like "invoice date cannot be future-dated"), referential integrity checks (validates foreign keys like vendor codes, GL codes, cost centers), statistical outlier detection (flags unusual amounts, unexpected vendors, anomalous patterns), and completeness checks (ensures required fields are populated).
Quality metrics are tracked and visualized: percentage of records passing validation, most common validation failures, data completeness scores by source, and quality trends over time. Poor data quality triggers alerts before it impacts month-end close.
Every data transformation is tracked: source system and timestamp (where did this data come from and when?), transformations applied (OCR extraction, validation rules, enrichment logic), data quality results (did validation pass? what issues were flagged?), approvals and decisions (who approved this invoice? when? why?), and destination (where did this data end up in the ERP?).
This lineage supports regulatory compliance: POPIA requires demonstrating lawful data processing and retention, Basel II/III requires complete audit trails for financial transactions, IFRS requires documented invoice processing and retention, and internal audit requires segregation of duties and control effectiveness evidence.
We train custom AI models for invoice reading: document classification models that distinguish invoices from receipts, statements, and other documents, OCR models fine-tuned on client's specific vendor invoice formats, field extraction models that identify and extract invoice number, date, vendor, amounts, line items, and tax, and validation models that flag suspect data (unlikely amounts, missing fields, format inconsistencies).
For invoice classification, we build models that: predict correct GL codes based on line item descriptions and historical patterns, identify cost center allocations based on invoice content and business rules, flag exceptions requiring manual review (new vendors, PO mismatches, amount thresholds), and route to appropriate approvers based on learned approval patterns.
These models are trained on your custom data—your invoices, your GL codes, your approval patterns. They learn what "normal" looks like for your business and flag deviations automatically.
The data product lifecycle we use for finance automation applies to any data source. Manufacturing companies use it for PLC-to-ERP integration. Retail businesses use it for POS-to-inventory automation. Marketing teams use it for multi-channel analytics consolidation.
The process is the same: audit data sources, design integration architecture, build quality controls, establish governance, implement AI-powered automation, track KPIs, and maintain compliance-ready lineage.
We're starting with accounting automation because finance teams feel the pain most acutely. But the methodology scales to marketing automation (Google Analytics, ad platform data, CRM integration), operations automation (machine data, supply chain visibility, inventory optimization), and cross-functional data products (combining finance, marketing, operations, and supply chain data for executive dashboards and decision support).
Off-the-shelf software assumes your data arrives structured, complete, and consistent. When it doesn't—and real data never does—the software breaks, requires manual workarounds, or forces you to change your processes to fit the software's limitations.
Data products start with data audit and quality assessment. They're designed around your actual data sources—messy PDFs, inconsistent vendor formats, multiple ERPs, fragmented systems. The automation is built to handle your data reality, not an idealized version of it.
By starting with data—not software—we build automation that handles your vendor invoice variations, works with your Sage customizations, respects your approval workflows, integrates with your existing systems (not replaces them), and adapts as your data sources and business requirements change.
This is why our solutions deliver measurable results: 70-90% time reduction in invoice processing and reconciliation, 95%+ automation rates with low exception volumes, month-end close time reduced by 3-5 days, and complete compliance-ready audit trails.
Every engagement follows the same proven methodology—whether we're automating finance workflows or building cross-functional data products.
Map your data sources, assess quality, identify integration requirements, and design the data strategy. Typical duration: 1-2 weeks.
Build ingestion pipelines, implement quality controls, train AI models, and run parallel processing to validate accuracy. Typical duration: 3-4 weeks.
Go live, monitor KPIs, optimize performance, expand to additional sources, and continuously improve AI models. Ongoing optimization and support.
