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EraDigital

AI Automation

Automate the work your team repeats every week

We design AI automations that take the repetitive steps out of operations — with retries, logs and human review on anything that should not run unsupervised.

Definition. AI automation uses models plus traditional workflow logic to classify, extract, draft or route work. It is still software: queues, failures, ownership and audit trails included.

Manual operations do not fail loudly — they just consume the team

Copying data between tools, triaging tickets and rewriting the same messages are expensive because they hide in headcount.

Hidden queue work

High-skill people spend time on formatting, filing and first-pass sorting.

Fragile zaps

Point-to-point automations break silently when a field changes.

No exception path

When automation cannot decide, work should land with a person — not disappear.

Capabilities

What we build

Specific delivery, not a vague capability list.

Document and email pipelines

Extract, classify and draft follow-ups from inbound operational content.

Ticket and order routing

Send work to the right queue with a recommended action attached.

Catalog and content jobs

Batch enrichment with approval before anything is published.

Solutions

Common implementation scenarios

Support operations

First-pass triage and draft replies, with agents handling send and exceptions.

Ecommerce back office

Reduce manual catalog and order exception handling across Shopify and internal tools.

Internal admin work

Turn recurring spreadsheet processes into a monitored pipeline.

How we work

Development process

  1. 01

    Discovery

    We clarify the business problem, current systems, constraints, success metrics and decision-makers before recommending a build.

  2. 02

    Strategy

    We define the product scope, delivery sequence and commercial trade-offs so engineering work maps to a measurable outcome.

  3. 03

    Architecture

    We design the application, data, integration and security model before implementation starts, reducing rework later.

  4. 04

    UI/UX

    We design the interfaces operators and customers will actually use — with clarity, accessibility and conversion in mind.

  5. 05

    Development

    We implement in short, reviewable increments with staging environments and visible progress against the agreed scope.

  6. 06

    QA

    We test functional flows, edge cases, integrations, performance and release readiness before anything reaches production.

  7. 07

    Launch

    We ship with a controlled cutover, monitoring and a clear rollback plan so go-live is an operational event, not a gamble.

  8. 08

    Support & Optimization

    After launch we stabilize, measure and improve — fixing issues quickly and iterating on the features that affect the business.

Stack

Technologies

  • AI APIs
  • OpenAI
  • Node.js
  • PostgreSQL
  • Redis

Industries

Where this service is typically used

Ecommerce

We help ecommerce businesses build Shopify experiences, operational AI, mobile clients and the integrations that keep catalog, orders and inventory honest.

SaaS

We help software companies ship AI capabilities, integration surfaces and mobile clients that respect tenancy, metering and a roadmap you can maintain.

Retail

We help retailers connect commerce, operations apps and back-office systems so availability, orders and exceptions are not a manual sport.

Read the industry pages: Ecommerce, SaaS, Retail.

Engagement

How we work commercially

Fixed scope

A defined outcome, timeline and budget after discovery. Best when requirements are clear enough to estimate with confidence.

Dedicated team

A stable product squad that works as an extension of your team across a roadmap, not a one-off ticket list.

Ongoing development

Retained engineering capacity for iteration, integrations, maintenance and new capabilities after the first release.

Consulting

Architecture, vendor selection, AI feasibility, Shopify Plus planning or integration design without a full build engagement.

FAQ

Questions about ai automation

Ordinary automation follows exact rules. AI automation can interpret messy input — then still hand off to rules, APIs and people.

Next step

Identify an automation candidate

Describe a repetitive workflow and the systems it touches. We will tell you what can be automated safely and what should stay manual.

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