Finance analytics and AI workflow automation for CFO teams
We unify data from your ERP, billing, banking and spreadsheets into a governed model, then use AI to automate month-end close, reconciliations and forecasting - delivering trusted metrics, audit-proof reporting and board-level insights.

Services
From daily execution to board-level planning, our solutions work as one connected finance system. Each module shares consistent KPI logic, workflow orchestration, and control visibility - so analytics, AI automation, and apps reinforce each other and compound in value over time.
Data Engineering & Analytics
Finance Data Foundation
- Data pipelines
- Data modeling
- Data governance
GL & Reporting
- Cashflow mapping
- Flux analysis
- Segment reporting
Planning, Budgeting & Forecasting
- Driver planning
- Rolling forecast
- Scenario modeling
Working Capital
- Cash conversion cycle
- Receivables & payables aging
- Inventory turns
Cost & Profitability
- Unit Economics
- Margin Waterfall
- Price-Mix-Volume
Capital Investments
- NPV & IRR
- Scenarios & simulation
- Portfolio optimization
AI-Powered Workflow Automation
Procure-to-Pay (P2P)
- Invoice capture
- 3-way match
- Vendor onboarding
Order-to-Cash (O2C)
- E-invoicing
- Cash application
- Collections
Acquire-to-Retire (A2R)
- Asset capitalization
- Depreciation runs
- Transfers & retirements
Hire-to-Retire (H2R)
- Time & attendance
- Expense management
- Payroll postings
Record-to-Report (R2R)
- Close orchestration
- Account reconciliations
- Consolidation & reporting
Low-Code Apps
Bespoke Finance Apps
- Schema-driven forms
- API & webhooks
- Routing & rules
Harness the power of unified dataHarness thepower ofunified data
We build bespoke financial data architectures that integrate disparate platforms into a single, authoritative record, enabling consolidated reporting across the business.
Finance analytics compatibility code examples
Scrollable code sample. Scroll horizontally to view long lines.
Scrollable code sample. Scroll horizontally to view long lines.
Scrollable code sample. Scroll horizontally to view long lines.
Scrollable code sample. Scroll horizontally to view long lines.
Scrollable code sample. Scroll horizontally to view long lines.
Scrollable code sample. Scroll horizontally to view long lines.
Finance analytics dashboard snapshot
Liquidity — Current Assets Mix
- Current ratioLatest
- 1.8×
- Cash runwayAt burn
- 7.2 mo
- Free cash flowL6M avg
- $0.4M
| Asset class | Mix value |
|---|---|
| Cash & Eq | 275 |
| A/R | 200 |
| ST Inv | 187 |
| Prepaids | 173 |
| Other CA | 90 |
P&L Trend - Net Revenue vs OpEx
| Month | Net Revenue | OpEx |
|---|---|---|
| Jan '24 | 40 | 30 |
| Feb '24 | 60 | 30 |
| Mar '24 | 70 | 35 |
| Apr '24 | 50 | 25 |
| May '24 | 40 | 20 |
| Jun '24 | 30 | 15 |
| Jul '24 | 70 | 35 |
| Aug '24 | 50 | 25 |
| Sep '24 | 40 | 20 |
| Oct '24 | 30 | 15 |
| Nov '24 | 45 | 22 |
| Dec '24 | 55 | 28 |
Finance Ops KPIs
- 2.5K
- AP Invoices Processed
- $8.1K
- Net Revenue
- 92%
- Forecast Accuracy
Governed AI automation for finance workflows
We build bespoke AI agents for autonomous finance teams. These agents operate inside your existing systems and control framework: they can interpret invoices, emails, and contracts; query databases and APIs; initiate approvals and postings; and return edge cases to human reviewers with full context. Applied across P2P, O2C, R2R, and FP&A, they convert long chains of manual handoffs into governed, observable automations-so the finance function sets direction and exercises oversight, rather than pushing every transaction forward by hand.
- OpenAI
- Claude
- Cohere
- Mistral
Finance automation capabilities
Journal entry & accrual assistant.
AI drafts accruals, provisions, and allocations; your team just reviews and approves.
Source documents, prior period history, and drivers or schedules flow into AI-drafted accrual, provision, and allocation entries, ending in a reviewed and posted draft journal entry. Source documents, prior period history, and drivers or schedules flow into AI-drafted accrual, provision, and allocation entries, ending in a reviewed and posted draft journal entry.Bank & GL reconciliation.
AI reconciles bank feeds to GL and subledgers, surfacing only true breaks for your team.
Bank logos for bank and general ledger reconciliation source coverage. - JPMorgan Chase logo
- Citi logo
- HSBC logo
- Barclays logo
- Bank of America logo
- Deutsche Bank logo
- BNP Paribas logo
- Societe Generale logo
- JPMorgan Chase logo
Close checklist & anomaly monitor.
AI monitors close tasks, reconciliations, and trial-balance outliers, flagging problems before they show up in the audit.
Close tasks, reconciliations, and trial balance feed an AI scan that surfaces overdue tasks, reconciliation breaks, and unusual variances. Close tasks, reconciliations, and trial balance feed an AI scan that surfaces overdue tasks, reconciliation breaks, and unusual variances.Card & expense policy auditor.
AI reviews card and T&E spend against policy and history, flagging missing receipts, out-of-policy items, and potential fraud.
Card swipes, expense claims, and receipts are evaluated by AI against rules and history, producing exceptions for missing receipts, out-of-policy spend, and potential fraud, followed by notifications. Card swipes, expense claims, and receipts are evaluated by AI against rules and history, producing exceptions for missing receipts, out-of-policy spend, and potential fraud, followed by notifications.Cash application.
AI matches bank lines and remittances to open items, keeping unapplied cash and suspense balances to a minimum.
Bank lines, payment references, invoices, and remittance advice are matched by AI into applied cash and unmatched cash outputs. Bank lines, payment references, invoices, and remittance advice are matched by AI into applied cash and unmatched cash outputs.
Platforms and tools in our stack
We architect solutions on a modern mix of analytics and automation platforms, selected for their proven reliability, interoperability, and governance characteristics. For each client, we assemble a tailored stack-spanning warehouses, ELT pipelines, BI, workflow engines, and low-code frameworks-that integrates cleanly with existing systems, preserves clear ownership of data and controls, and can be sustained at scale over time.
- Snowflake
- BigQuery
- Databricks
- Azure Synapse Analytics
- PostgreSQL
- Microsoft SQL Server
- Fivetran
- dbt
- Azure Data Factory
- Microsoft Power BI
- Tableau
- Looker
- Microsoft Power Automate
- UiPath
- Zapier
- Microsoft Power Apps
- Retool
- Apache Airflow
Delivery model
Every engagement follows a disciplined three-phase delivery model. We begin with your finance brief, sharpen objectives and constraints, and map the current data and process landscape. We then architect and implement an integrated operating model across systems, controls, and teams, culminating in a live stack of models, reports, and workflows that deliver measurable business value.
From brief to business value
Request a demo- 01 Ideation
Clarify and stress-test your brief
Work with your finance and business leaders to sharpen the question we are solving, surface constraints, and align on success metrics. We map your current data, systems, and processes to identify quick wins and the right scope for the first release.
~2 weeks - Problem framed and roadmap agreed
- 02 Development
Design and build the operating stack
Translate the brief into a working finance operating model: data structures, calculation logic, controls, reports, and workflows. We build in short, review-driven cycles using your real data, so stakeholders can see, test, and refine the solution as it takes shape.
~6 weeks - Models, reports, and workflows live in pilot
- 03 Launch & Maintain
Embed, support, and continuously improve
Move from pilot to business-as-usual, with us alongside your team. We harden the stack for production, train users, and put in place governance, documentation, and KPIs. After launch, we stay involved to monitor performance, resolve issues, and evolve the model as your business and data change.
Launch complete - Ongoing optimization and support
FAQ
The questions below reflect the themes clients most often raise about our Finance Analytics work-its scope, operating model and governance. For a mandate-specific view, we typically work through your context in a structured discovery session.
What kinds of finance teams do you typically work with?
We work primarily with CFOs, finance leaders, controllers, and FP&A teams who already have a functioning finance operation, but lack the data infrastructure and automation to support the next stage of growth. Typical clients are multi-entity or multi-division organisations, often with several source systems (ERP, billing, banking, payroll, CRM) and a proliferation of spreadsheets. In short, we are most useful where the accounting is under control, but reporting, planning, and operational workflows are creaking under the weight of manual effort and fragmented data.
Do you only build dashboards, or do you also redesign finance processes and workflows?
Dashboards are usually the visible tip of the work, not the work itself. Our engagements typically combine data engineering, finance-specific data modelling, and the redesign and automation of key workflows across P2P, O2C, A2R, H2R, and R2R. That can mean anything from standardising how the GL and subledgers feed a management P&L, to automating approvals, reconciliations, or cash-application steps that previously lived in email and Excel. The objective is not "prettier charts," but a more reliable, auditable, and less manual finance operation that happens to surface its outputs in well-designed reports.
How does a typical engagement with you work from brief to go-live?
Every engagement follows the same underlying structure, adapted to the client's context. We begin by clarifying the brief: agreeing on the questions we are trying to answer, the decisions they will inform, the constraints we must respect, and the current landscape of systems, data, and processes. We then design and build the operating stack-data structures, calculation logic, controls, reports, and workflows-working in short, review-heavy cycles using real data rather than idealised samples. Finally, we harden the solution for production use, support rollout and training, and put in place governance and documentation for its ongoing operation and evolution.
Can you work with our existing tools and data stack, and what platforms do you typically use?
Yes. As a matter of principle, we start from your existing estate-ERP, CRM, billing, banking, payroll, data warehouse, and BI tools-and only introduce additional platforms where there is a clear gap or economic case. In practice, we most often work with mainstream databases and warehouses, ELT and integration tools, BI platforms such as Power BI, and low-code environments including the Microsoft Power Platform for apps, automations, and portals. The specific combination is tailored to your environment, support model, and risk posture; our role is to assemble a coherent finance data and workflow architecture rather than to impose a particular vendor catalogue.
What do you need from our internal team to make a project successful?
We do not require a large internal task force, but we do need a small, stable group of counterparts. Typically this includes a finance lead (CFO, controller, or FP&A head), a business stakeholder who feels the pain of the current process, and someone with access to systems and data (often from finance ops or IT). We handle the design and build, but we rely on your team for three things: authoritative definitions of metrics and edge cases, timely feedback during reviews, and the internal sponsorship needed to ensure that new tools and workflows are actually adopted.
How long does it take to see tangible value from a project?
Timeframes depend on scope and complexity, but we design engagements so that clients see concrete value early rather than waiting for an all-or-nothing "go-live." For many projects, a first wave of usable outputs-such as a critical management report, a working capital or margin view, or an automated workflow that removes a painful manual step-emerges within roughly 6-8 weeks. Larger programmes may extend beyond that, but the intent is always to deliver a sequence of increments that stand on their own, rather than a single, monolithic deliverable at the end.
How do you handle data security, access control, and confidentiality?
We work within the security perimeter you define, typically using your own cloud tenant and approved tools so that sensitive data remains under your organisation's control. Access is granted on a least-privilege basis, with clear separation between production and non-production environments, and we design solutions with role-based access, logging, and auditability as first-order concerns rather than afterthoughts. Confidentiality obligations are governed by contract and NDA, and are reflected in our day-to-day working practices, including how we structure environments, manage permissions, and handle project artefacts and documentation.
Put AI to work across your finance workflows
In a short discovery call, we'll identify the highest-impact AI-powered automations and outline a governed roadmap to audit-ready reporting and a faster close—within your existing controls.
30-minute video call | No cost, no obligation.



