Typical automation copies the manual process. Ours brings deep business acumen to how you operate today — and keeps an eye on what you'll need tomorrow.
New pricing rules. New customer requirements. New integrations you haven't thought of yet. The work that used to require your intervention starts to run on its own — and the platform adapts as your business changes. Here's how it happens.
Every app your business runs silos critical information — your clients, your invoices, your payments, your performance. The Operational Data Layer brings it home into a single source of truth that you own, you control, and your automation can rely on.
A near-real-time data layer that syncs, consolidates, transforms, and serves the state of every app that runs your business — managed by us and owned by you.
Your accounting system, payment processor, CRM, and billing app each hold a piece of your business — and each one silos it behind their own terms, their own API, their own limits. The Operational Data Layer consolidates everything into one place, transforms it into clean business objects, and gives your automation and agents a single, reliable source of truth to work from.
We've been building data replication and transformation systems for decades, and running this pattern in production for years — well before the wider automation community started discussing the need for it. The name is new. The practice isn't.
Every app your business runs holds a fragment of the truth. The ODL brings them together into a single, current picture — so reconciliation, reporting, and automation all read from the same source instead of querying multiple systems and hoping it works.
Right now, you're renting access to your own data — subject to each vendor's terms of service, their privacy practices, and whatever they decide to change next quarter. Your business data is your intellectual property, and the ODL treats it that way. A complete, continuously updated copy that exists independently of any single vendor. You own the data. We own the logic that keeps it current.
When your data is already consolidated and normalized, you're never locked in. Outgrow your CRM? Switch billing platforms? The same transformation infrastructure that builds the ODL can be repurposed to push clean data into the new system — and run both platforms in parallel while you transition. A capability most businesses assume costs five figures and several months.
Every technology has a specific purpose. Every stack is customized per client — private and dedicated, never shared.
Every client runs on isolated infrastructure — a private ODL, private n8n instance, private networking. Not a shared tenant. Not a multi-user platform.
We chose Microsoft Azure since most of our clients already run Microsoft 365. Their identity, their email, their file storage — it's already there. Dedicated containers mean VPN integration with on-premise systems when you need it, and a clear path to local AI models when the time comes.
Visual, code-extensible, self-hosted. When we build an automation, we visualize and execute your workflow step by step — the workflow itself is the living specification.
Self-hosted means the engine runs on infrastructure we control, not a vendor's cloud. No execution limits. No feature gates. No dependency on a third party's uptime or pricing changes.
We integrate with your cloud apps and services through their native APIs — the secure connections that let authorized services like ours read and update your data. Not pre-built connectors. Not community plugins. This is a deliberate choice, learned the hard way.
Pre-built nodes abstract away the details. That's convenient right up until something breaks, the node doesn't support the field you need, or your app vendor changes their API. Native integrations mean we understand exactly what's happening, we can fix it ourselves, and we're never waiting on a third party to update a connector.
Every dollar your business earns touches one of these stages — if not all of them. Some are already automated for our clients. Others are where we're building next.
Quotes, estimates, proposals, and order confirmations.
In development — converting client purchase orders to sales orders, eliminating manual re-entry across systems.
One-time, recurring, subscription, and milestone billing.
In production — automated invoicing for MAT settlement processing and Remergant's own service billing.
Cards, ACH, pre-authorized debit, wires, partial payments.
In production — automated payment collection and settlement for MAT. Extending to Remergant's own billing.
Aging, follow-up, past-due notices, payment plans.
In production — automated past-due notices with fee assessment for MAT.
Tax remittance, regulatory filings, documentation.
In production — full remittance automation for MAT including tax handling and compliance documentation.
Payment-to-invoice matching, bank reconciliation, settlement balancing.
In production — cross-system reconciliation for MAT settlement processing.
Refunds, credit memos, billing corrections.
In production — automated credit memo generation for Remergant's own billing.
Point-in-time, over-time, deferred revenue tracking.
On the roadmap — building toward automated recognition rules as the client base grows.
Revenue reports, DSO tracking, cash flow projections.
On the roadmap — the ODL is designed to serve as the reporting source once recognition rules are in place.
Engagement monitoring, reactivation, relationship health.
On the roadmap — the data layer captures the signals; the automation to act on them comes next.
Every platform engagement follows the same three-beat rhythm. We build it once, we evolve it continuously, and the fee is tied to the value it creates.
Design and stand up the automation against your existing systems. Mapped to your business rules — not a vendor's template, not a copy of your manual process.
The system gets better as your business does. New rules, new integrations, better exception handling — refined continuously, not just maintained. The difference between automation that runs and automation that evolves.
You pay for the value the system creates — not the hours we put in. If volume grows, we grow with it. If it stalls, we have skin in the game.
Each component covers something specific. Nothing is bundled to obscure what you're paying for.
Build fee · One-time. Architecture, development, deployment, and provisioning the ODL with its initial connections. Scoped and quoted upfront against a service block — a single payment so it never competes with your monthly costs.
Base fee · Monthly. The cost of running your infrastructure — Azure, SurrealDB, networking, vendor subscriptions — marked up for our management of those services. Plus a monthly allocation of included support hours. Every line item is visible.
Upside share · Percentage of value created. We continuously invest in making the system better. When those improvements produce results, we share in the value they create. Our incentive is aligned with yours: improve the system, not just maintain it.
Event fees · Per occurrence. High-value automations that run on a non-predictable basis — late payment recovery, NSF charges, dispute handling. The event fee recovers the investment of building automations you benefit from and didn't know you needed.
Every engagement is quoted from scratch. There's no rate card here because the right mix for a $2M services business and a $20M distributor looks completely different.
What shifts each variable: transaction volume, average ticket size, integration count, infrastructure complexity, and how much of the revenue cycle the platform covers. We model each before quoting.
Enough transaction volume that the automation pays for itself. We say no when a spreadsheet would do.
Your existing systems can be read from and written to via API. If not, we scope adapters as part of the build.
The business rules are knowable — even if no one's written them down yet. We extract them; you confirm them.
You're comfortable with us holding the keys to the revenue cycle. We're comfortable being the team that fixes it at 2am.
Let's map what yours should look like — then quote it specifically.
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