Finance Automation Built on Your ERP and Microsoft Stack

ERP finance automation is the practice of building automated finance workflows -- collections, invoice matching, job costing, reporting -- on top of the ERP you already run, rather than replacing it or squeezing your process into an off-the-shelf tool. The ERP stays the clean source of record. The automation layer does the work the ERP was never designed to do.

That distinction matters because ERPs are systems of record, not systems of work. Dynamics 365 Business Central knows which invoices are open; it does not know who called which customer, what was promised, or which dispute is blocking a six-figure payment. Sage 50 holds your nominal ledger; it cannot tell you whether last week's job to Milan made money. The gap between the record and the work is where finance teams burn their hours -- in inboxes, spreadsheets and memory.

Fintask closes that gap with custom-built platforms that run inside your own Microsoft environment: reading ERP data through Azure Synapse or Microsoft Fabric, sending email through Microsoft Graph from your own mailboxes, authenticating with Entra ID, and deploying to Azure Container Apps in your tenant. Nothing leaves your estate. Nothing writes back to the ERP.

Sometimes you should. If your process is standard -- one AP contact per customer, simple dunning ladders, a common ERP connector -- a subscription AR or AP product will be faster to switch on, and we will tell you so in an assessment.

Mid-market finance teams come to us when that stops being true:

• The process doesn't fit the template. One of our clients chases ~2,500 individual insurance claims adjusters spread across 1,200 carrier customers. No off-the-shelf AR tool models a payer structure like that.
• The data can't leave. A platform running in your own Azure tenant, behind your own Entra ID, passes security review in a way a third-party SaaS holding a copy of your ledger often doesn't.
• The economics invert at scale. Enterprise AR and AP suites are priced per user, per year, forever. A custom build on infrastructure you already pay for is a one-time project with running costs measured in tens of euros a month of Azure spend.
• You already own the plumbing. If you have Business Central, Fabric or Synapse, Microsoft 365 and Azure, most of the hard infrastructure exists. The missing piece is the workflow layer -- and that's a build, not a licence.

These are real production systems, not demos. Each links to a full case study and a deeper guide to the pattern behind it.

A US insurance services firm had $11.8M of open receivables across 7,700+ invoices and no system of record for collections -- the work lived in personal inboxes. We built a collections workspace that mirrors Business Central data daily through Azure Synapse, re-scores every payer's risk each morning with a balance-weighted algorithm, generates and routes follow-up tasks across four escalation tiers, tracks promises to pay, and sends dunning emails through Microsoft Graph -- with the ERP untouched.

Read the full case study, or the guide to AR automation for Business Central.

A Norwegian equipment rental group (~€30M revenue) ran a €7.6M receivables ledger with a one-person AR team. We built an AI agent that reads open invoices from their Microsoft Fabric Gold layer every morning, drafts the correct chase email for each account in Norwegian, classifies every customer reply into one of twelve intents, and routes everything through a human approval queue. A single agent run drafted 162 correctly-staged chase emails. No email is ever sent without sign-off.

Read the full case study, or the guide to Microsoft Fabric for finance teams.

An Irish pan-European freight operator had five disconnected systems -- TMS, telematics, fuel cards, toll feeds, ferry PDFs -- and reconstructed job costs by hand in Excel. We built a platform that allocates 101,000+ toll transactions to the exact trip legs they belong to, reconciles ~9,500 fuel-card records a year against telemetry, reads ferry invoices with AI extraction, and maps every cost to Sage nominal codes. In its first months it surfaced a €41,000 cost gap nobody knew existed.

Read the full case study, or the guide to job costing automation with Sage.

Every build follows the same rule: the ERP is read-only. Data flows out -- through a Synapse view, a Fabric Gold SQL endpoint, or an API layer -- into the platform's own Azure PostgreSQL database. Everything the automation creates (tasks, call logs, promises, emails, cost allocations, approval decisions) lives in the platform, never in the ERP.

This is what makes finance teams comfortable automating: there is zero risk of a bug corrupting the books, auditors get a clean ledger plus a complete separate activity trail, and if you ever switch ERPs, the workflow layer survives.

The standard stack we deploy on:

• Azure Synapse / Microsoft Fabric -- consolidated, read-only views of ERP data
• Azure PostgreSQL -- the platform's own operational database
• Azure Functions / Container Apps jobs -- daily syncs, scoring runs, agent schedules
• Microsoft Graph -- email from your own mailboxes, SharePoint document access
• Microsoft Entra ID -- single sign-on with role-based access; no new passwords
• Managed identities throughout -- no connection-string secrets to rotate

We put AI where it earns its keep and keep it away from where it doesn't. The core of every platform is deterministic and auditable: risk scores a finance team can explain to an auditor, escalation rules they can edit in a settings screen, cost allocation logic that produces the same answer twice.

AI handles the unstructured edges: reading supplier PDFs, drafting collection emails grounded strictly in ledger data, classifying customer replies, surfacing anomalies. And it always ships with guardrails -- human approval queues for anything customer-facing, hard stops for legal threats and bankruptcy mentions, and validation harnesses before go-live. In the Norwegian build, the agent passed a 23-scenario test covering every outbound stage and inbound intent before it touched a real customer.

Every project starts with an AI readiness assessment: we map your systems, gauge how clean the data actually is (it's never clean -- that affects sequencing, not feasibility), and identify the highest-leverage automation first. Builds typically go from kickoff to production in weeks, not months, because we reuse the Microsoft infrastructure you already have instead of introducing new systems.

What you get is configurable without a code deploy: templates, cadences, risk thresholds, nominal-code mappings and even AI prompts are editable by your finance team in an admin screen. Deployment is automated -- a push to the main branch builds a container and ships it to your Azure environment.

If your receivables, payables or job costing still run on spreadsheets next to a perfectly good ERP, tell us how the process works today. We'll show you what the automation layer would look like on your stack.