Every number. Every trend. Every anomaly.
108 analytics endpoints across procurement, finance, suppliers, budgets, cash flow and accounting - with AI-powered transaction insights, processing intelligence and live dashboards. From raw data to actionable intelligence.
108 endpoints. Ten domains. One coherent platform.
Every metric, trend, breakdown and drill-down accessible via API - computed from live data on every request. The intelligence layer that sits above every other module.
Ten modules. One filter model. Every angle covered.
From the operations command-centre to the AI-powered transaction insights - every domain has its own deep-dive. All filterable by site, area, supplier, category, date range and time frame, all returning the same time-series shape your charts already speak.
Backlog, position, volume by dimension, processing pipeline, AP sync, procurement metrics, supplier overview, payments. The command centre in one call.
- Invoice + credit backlog with totals due
- Volume sliced by site, supplier, category, intake source
- Processing funnel: upload → validated → failed
AP aging by bucket, three-way match rate, unmatched invoices, spend trend, budget variance, GL spend, tax summary, DPO, duplicate detection.
- Three-way match: PO vs GRN vs Invoice with match-rate %
- DPO with industry benchmark comparison band
- Duplicate-invoice detection with grouped value at risk
Working-capital KPIs, AP aging, weekly outflow forecast (7–365 days), payment-run preview, outflow trend, top payables with creditor concentration.
- Outflow forecast for next 7–365 days from due dates
- Payment run with configurable horizon (1–90 days)
- Daily burn rate + DPO summary
Summary, utilisation distribution, budget-vs-actual trend, cumulative trend, category spend trend, utilisation rate trend, hierarchical variance breakdown.
- Variance breakdown table - category → site with sparklines
- Four time-series chart endpoints with consistent shape
- Best / worst category surfacing per period
HHI concentration index, credit ratio, credit frequency, status counts, 4-dimension performance scorecard, supplier comparison vs org average.
- HHI risk bands: Low → Moderate → High → Critical
- Credit ratio: Excellent <2% → Unacceptable >12%
- Single-supplier 4-dim composite score (0–100)
Category-level concentration, spend mix, risk distribution, spend trend, single-source-trend (categories with one supplier), per-category HHI, volatility trend.
- Single-source risk surfacing - categories with one supplier
- Spend mix as pie / treemap-ready data
- Per-category volatility % over time
Monthly avg / min / max unit price per product from purchase line items. Multi-product comparison for benchmarking. Order-count and total-quantity included.
- Price-trend window from 12 to 36 months
- Multi-product comparison side by side
- Source: purchase line-item history
Document count, processing time, time saved, cost savings, model accuracy with method breakdown. Donut breakdowns + 6 time-series trends.
- Verified-score, confidence-score, quality-score
- Touch-free / single-touch / multi-touch breakdown
- Anomaly + fraud detection rate trends
Pattern detection across every transaction. Subscription, recurring, seasonal and one-off classification. New-pattern alerts, price-drift detection, frequency-change tracking.
- Subscription & recurring detection per supplier
- Price-drift alerts with threshold & confidence
- New-supplier-recurring + frequency-change alerts
P&L, balance sheet, cash position, AR aging, DSO, top customers, financial ratios, KPI scorecard with traffic-light status - direct from Xero, QuickBooks or Sage.
- Auto-detects provider - Xero → QB → Sage fallback
- Historical snapshots + trend extraction (30–365 days)
- Bank transactions with reconciliation status
What's due. What's overdue. What's coming next.
Every payable in the business mapped to a date. AP aging buckets show what's outstanding. The outflow forecast extends from 7 to 365 days. The payment-run endpoint surfaces invoices due in a configurable window - the working list for next Tuesday's BACS.
- "What do we owe in the next 14 days?" · payment-run endpoint with supplier, amount, due date, days-until-due
- "Is our burn rate increasing?" · monthly outflow trend from 1 to 36 months of paid invoice data
- "Which suppliers are we most exposed to?" · top payables with creditor concentration
- "What's the cash outflow projection for Q3?" · weekly outflow forecast extending up to 365 days
Pattern detection. Price drift. New-supplier alerts.
Every transaction classified. Every recurring pattern identified. Every price drift flagged. Pattern-matching algorithms scan the full transaction history and surface what would otherwise stay buried in the line-by-line detail.
SaaS · Datadog Inc
Fixed-amount, fixed-frequency transaction detected. £2,400/month for 14 consecutive months. Trend: stable. Last invoice posted 28 April 2026.
Semi-skimmed milk · Bidfood
Unit price has drifted +14% in 60 days. From £0.92/L (Mar avg) to £1.05/L (May avg). Drift exceeds the configured 8% threshold. Volume order pattern unchanged.
Fresh Direct Ltd · weekly orders
New supplier with recurring pattern detected. Weekly invoices averaging £680 over the last 4 weeks. Was a one-off relationship in Q1; now established weekly cadence.
Brakes Foodservice · cadence shift
Existing pattern changed from monthly to weekly in the last 30 days. Combined order value has grown 4× while per-invoice value has fallen - split-billing pattern. Worth a conversation.
Detection runs across the full transaction history. Alerts raised for new patterns in the last N days (default 30). Confidence scores reflect the pattern's regularity, not a probability - the detection is rule-based.
P&L. Balance sheet. Direct from your books.
32 endpoints pulling live data from Xero, QuickBooks or Sage. Auto-detects which provider you're connected to and falls back to cached data when no live connection. The KPI scorecard rolls everything into a green/amber/red traffic-light view your finance team can action.
- P&L summary - multi-period revenue, costs, margins, net income
- Cash position - bank balances across all connected accounts
- Financial ratios - current, quick, debt-to-equity, gross + net margins
- KPI scorecard - traffic-light status across 12 key indicators
- Tiered cache - 4h reports, 1h invoices, 30min cash for performance
From "export to Excel" to live, drilled-down intelligence.
Most multi-site operators keep their analytics in spreadsheets - exported monthly from the accounting system, manually pivoted, always out of date by the time the meeting starts.
- Spend data locked in accounting software - export to Excel to analyse
- No real-time visibility - reports run monthly, always out of date
- No pattern detection - subscriptions, price drift and anomalies stay invisible
- Supplier risk unknown - concentration, credit quality, performance not measured
- Budget tracking manual - spreadsheet updated at month-end
- Cash flow guesswork - no forward projection of payables
- Three-way matching done manually - or not at all
- Processing performance unmeasured - no accuracy, speed or cost tracking
- 108 endpoints - every metric, trend and drill-down via API
- Live computation - figures pulled fresh on every request
- AI transaction insights - patterns, price drift, new-supplier alerts surfaced automatically
- Supplier intelligence - HHI concentration, credit quality, 4-dimension scorecard
- Budget tracking live - spent + committed with threshold alerts
- Cash flow forecast - weekly outflow projection with payment-run planning
- Three-way match dashboard - PO vs GRN vs Invoice with match-rate %
- Processing intelligence - model accuracy, time saved, anomaly trend
Ten domains. One question box.
Every analytics endpoint is in scope. LEDGE picks the right module, applies the right filters and explains the answer with the underlying data behind it. Cross-module queries - "compare suppliers by performance and cash exposure" - handled in a single call.
The practical details.
When you say "real-time" - how live is it really?
/analytics/dashboard, the SQL runs against the live transactional database and returns the current numbers. That's "real-time" in the practical sense - the figures reflect everything that was committed up to the moment of the call. We don't run a streaming push channel - it's request-response, not WebSocket. The accounting analytics use a tiered cache (4h for reports, 1h for invoices, 30min for cash) because those calls hit external APIs and we don't want to hammer Xero on every refresh.Is the AI in "Transaction Insights" actually machine learning?
Can I customise the dashboard widgets?
/analytics/dashboard/backlog, /analytics/dashboard/processing-pipeline) and compose your own dashboard against the API. All endpoints share the same filter model and time-series shape so a custom dashboard is a few hours of frontend work, not weeks. A drag-and-drop dashboard builder is something we've considered but haven't built yet.Can I get scheduled email reports?
format=csv or format=xlsx) so you can pull data on demand into Excel. For scheduled distribution, customers typically point a BI tool (Looker, Metabase, Power BI) at the API and use that tool's scheduling. We'd be happy to discuss native scheduled reports if there's clear demand.Are the benchmarks ("DPO industry avg") sourced from external data?
Multi-currency - how is FX handled?
currency_conversion_rate set at the moment the invoice is posted. All analytics aggregations use that posting-rate to convert to your base currency - so the totals in the dashboards reflect what the invoice was worth on the day it landed, not today's rate. This matches standard accounting practice and avoids the "your reports change every time the rate moves" problem. Cash position figures from accounting providers are pulled at the provider's reporting rate.Which accounting providers are supported?
How does the platform perform at large scale?
func.date() conversions in WHERE clauses) and most endpoints respond in well under a second on typical multi-site datasets. For very large datasets - millions of invoices, hundreds of sites - some of the trend endpoints can take a few seconds. If your scale would benefit from pre-aggregation or materialised views, that's something we'd build to your specific data shape rather than ship as a generic feature.See your data come alive.
Bring three of your real data questions to a 30-minute demo and we'll run them live against the platform. Pattern detection, supplier risk, cash forecast - pick your angle. No card needed.