Cross-system workflow design
Intake, validation, approvals, and hand-offs mapped end to end across your ERP, CRM, document stores, and back office, with the exception paths and approval chain built in from the start, not bolted on after launch.
Automation
Enterprise automation fails for a specific reason: the workflow crosses five systems, three departments, and a compliance requirement, and no low-code tool owns all of that. DPI builds automation that spans your systems of record, with audit logs, role-based access, and exception handling a controller or auditor can review, deployed as a managed service rather than a platform you have to run yourself.
Who This Is For
What the Service Includes
Intake, validation, approvals, and hand-offs mapped end to end across your ERP, CRM, document stores, and back office, with the exception paths and approval chain built in from the start, not bolted on after launch.
Every automated action, approval, and exception logged with who or what triggered it, viewable by role, and exportable for an audit. Access to sensitive steps is scoped, and sensitive changes require a person to confirm.
Invoices, contracts, purchase orders, and forms extracted, validated against your business rules, and routed, with confidence scoring and a human review queue for anything below your threshold.
Built on your APIs rather than screen-scraping: ERPs such as NetSuite and SAP, CRMs such as Salesforce and HubSpot, ticketing systems, and internal databases, connected directly so the automation survives a vendor's UI update.
Every workflow has a defined path for what happens when a rule does not match: escalate to a person, queue for review, or retry with a different rule, so an edge case is a ticket, not a broken process.
Volume processed, exceptions and their reasons, cycle time before and after, and cost avoided, reported monthly against the baseline measured before launch.
A single-team automation is a script: one system, one team, one owner who can fix it when it breaks. Enterprise automation is different because the workflow does not respect team boundaries. An invoice starts in email, gets approved by someone in a different department, updates the ERP, and triggers a task in a CRM. A low-code platform can usually automate one hop of that chain well and the rest badly, because it was built around its own connectors rather than your process. The result is either a brittle chain of point tools that breaks every time a vendor changes an API, or a platform license sized for enterprise workloads that still needs a specialist to configure every workflow.
The second failure mode is governance. Once an automation touches money, customer data, or a regulated process, someone asks for an audit trail, a way to see who approved what, and a plan for when the automation is wrong. Retrofitting that onto a workflow built for speed is expensive; designing it in from the first workflow is not.
We start by mapping the workflow as it actually runs, across every system it touches, and find where the process needs a rule versus where it needs a person. The automation is then built on your APIs, not a vendor's connector catalog, so it does what your specific ERP, CRM, and document systems actually support rather than the lowest common denominator a platform offers. Where a platform such as ServiceNow, Workato, or UiPath is the right fit for part of the chain, we build on it; where it is not, we build directly, and either way you get one system that spans the whole workflow instead of several that each own a piece of it.
Every automated action is logged: what triggered it, what data it touched, what rule or model decision produced the outcome, and what happened next. Access to configure or override the automation is scoped by role through Okta or Microsoft Entra. Actions above a threshold you set require a person to confirm before they execute. This is not a feature we add when someone asks; it is how the system is built from the first workflow, because retrofitting audit logging after an incident is the expensive way to learn you needed it.
The workflows that make automation worth building are the ones with real volume, which means real edge cases. Every automation we build has a defined exception path from day one: cases that do not match a rule go to a review queue with full context, escalate to the right person, or retry under a different rule, and we track why. Over months, the exception rate becomes a source of new rules rather than a permanent manual workaround, which is where the second and third year of value usually comes from.
Enterprises rarely automate one workflow in one place; they need the same logic in three regions or five brands, each with slightly different systems and rules. We build the automation once, with unit-specific configuration and data scoping, so a rollout to a new business unit is a configuration exercise rather than a new build. This is also what makes managed operations sustainable at scale: one team supports the shared architecture instead of a different codebase per unit.
How It Works
We document the workflow as it runs today across every system it touches, measure volume and cycle time, and identify where rules are clear versus where judgment is required. Output: a scoped automation plan and the metric it is judged on.
Integration design, data flows, access model, and audit requirements documented for your IT and compliance teams before a line of production code ships, the same review a security team would run on any enterprise system.
The automation is built against your actual systems and tested on historical volume before it touches a live transaction, with exception rates measured before go-live.
A phased rollout by business unit or workflow, then managed operation: monitoring, rule updates as policy changes, new exception types added, and monthly reporting against the baseline.
Stack & Integrations
Engagement Models
Baseline measurement, cross-system design, and a controls document your IT and compliance teams can approve. Fixed scope, useful even if the build happens elsewhere.
Fixed-scope delivery of the automation and its integrations, phased by business unit, with the exception rate and cycle-time target agreed up front.
Monthly operation: monitoring, rule updates, new exception types, audit support, and reporting against the baseline.
FAQ
Platforms give you a canvas and license fees per workflow or per bot; you still design, build, test, and maintain each automation yourself, and audit logging and role-based access are often add-ons. We build on whichever platform fits, including an open stack when a license would cost more than the workflow saves, and we design, build, test, and operate it, so your team gets the outcome rather than another tool to run.
During the architecture review, yes, when it is relevant. Those platforms suit certain patterns well, particularly ticket-driven ITSM work and scheduled batch jobs. Where the workflow is document-heavy, judgment-heavy, or needs a language model to read and decide, a purpose-built automation usually costs less and adapts faster than configuring a general orchestration platform to do something it was not designed for.
Every automated action is logged with the trigger, the data it acted on, the rule or model decision behind it, and the outcome, viewable by role and exportable. Sensitive actions require a person to confirm before they execute. This is designed to survive a SOX, SOC 2, or internal audit, not just to exist.
Yes. The automation logic is built once and configured per unit, with data and access scoped per unit, so each business runs its own instance without a separate build. This is how we keep enterprise rollouts from becoming a series of one-off projects.
It escalates rather than guessing. Every workflow has a defined exception path: a review queue, an escalation to the right person, or a retry under a different rule. Exception rates are measured from day one, and recurring exceptions become new rules over time instead of permanent manual work.
Yes. Where data residency or vendor policy requires it, the language and extraction models run self-hosted in your environment, including open-source models such as Qwen. Hosted models are used under enterprise terms that exclude training on your data where that is acceptable instead.
The process map and architecture review is typically two to four weeks. The first workflow usually goes live within six to ten weeks depending on the number of systems and the approval complexity; later workflows on the same architecture move faster because the integrations and controls already exist.
Related
Next Step
Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.