What we do
Digital Gateway is a boutique technology team, not a consulting firm that drops a report and leaves. We embed with our own operators, engineers, and cloud team and run the whole arc — diagnose, build, and keep it running in production.
AI readiness
Map the process, classify the data, and score each workflow for automation readiness before a line of code is written.
AI implementation
Agents, automations, and integrations built by senior engineers — with evaluation, human-in-the-loop, and audit trails, not a demo that breaks in week two.
Fractional AI team
Ongoing monitoring, validation, and new initiatives — a team that scales with the work instead of a one-time project.
The entry point — our AI Workflow Readiness Scorecard
Before automating anything, we answer one question: is this process actually ready for AI? Every workflow is scored across five blocks, each broken into sub-criteria rated 1–5. It's industry-agnostic and built for organizations, not just individuals — so it accounts for ownership, maintenance, and the cost of getting it wrong.
Volume & frequency, repeatability, cost of error, what the process actually produces.
Clarity of inputs/outputs and the defined artifact the workflow must deliver.
Data availability and quality, integration surface, feasibility of the AI approach.
Roles & responsibilities — who owns, maintains, and uses the automation.
Fit with priorities and the case for doing this workflow before the next one.
A simple gate before you invest design and build effort.
Built for regulated, data-sensitive work
Production AI in a regulated setting is a governance problem as much as a modeling one. We staff engagements with senior engineers experienced in compliance-grade domains and design for it from day one.
- Engineering depth in regulated systems — banking/fintech and healthcare — where correctness, testing, and auditability are non-negotiable.
- Certified Azure AI & data engineering: Cognitive Services, Databricks/Spark, MLOps pipelines.
- Validation, versioning, logging, and human-in-the-loop on high cost-of-error steps — the difference between a POC and a system you can defend in an audit.
Local vs. cloud LLMs — our view
It's rarely a binary. The real question is: what's the smallest model that clears the accuracy bar for this task, and where is it allowed to run given the data's sensitivity?
Sensitive/PHI-touching steps run on private-cloud with enterprise controls (BAA, region-locking, no-training guarantees) or on local open-weight models. De-identified, reasoning-heavy steps can use frontier models. Most regulated agents end up hybrid.
Open-weight models are now good enough to make local a real third option — this isn't pharma "reverting," it's maturing to a data-tiered architecture. We help design which tier each workflow belongs in.
Examples of our work
One integration layer across a fragmented insurance stack
A US commercial P&C insurer was bleeding underwriter capacity to manual re-keying — the policy admin system, rating engine, CRM, and document inbox didn't talk to each other, so every submission was typed in three times. Industry-wide, underwriters spend about 70% of their time on non-underwriting work (Accenture).
We built the missing middle: an event-driven integration layer that ingests each ACORD submission and email attachment, uses AI document extraction to read and structure it, clears and validates the data, and syncs it across the policy admin system, rating APIs, and CRM — so a submission arrives as clean structured data instead of a PDF someone has to retype.
Result: submissions flow from inbox to underwriter-ready automatically, and underwriters get their hours back for risk selection instead of data entry.
Where it runs · Cloud. Entirely inside the insurer's private Azure tenant — VNet-isolated and region-locked, with models called in-region so no data leaves their environment.
Delivered as an embedded build; client name withheld.
AI voice agents that take patient calls off nurses
A US healthcare provider was burning nurse hours on pre-op intake, post-procedure follow-up, and scheduling calls. We built AI voice agents that place the call, transcribe it, decide the next action, and write it straight back to the EMR — so clinical staff get their time back for care.
Built on Azure end to end, in production inside HIPAA / SOC 2 controls with human-in-the-loop on anything clinical:
Result: routine patient calls run themselves, and nurses get hours back at the bedside instead of on the phone.
Where it runs · Hybrid / local. PHI-touching steps (transcription, patient identifiers) stay on local open-weight models via ONNX Runtime inside the network; only de-identified reasoning uses Azure OpenAI in the HIPAA-covered tenant. No PHI leaves the perimeter.
Delivered as an embedded build; client name withheld.