AI Workflow Automation
Hire AI Workflow Developers — Automation That Ships
Every business runs on processes that someone repeats by hand. ISEMI builds AI workflow automation that takes those processes off your team's plate: we audit how work actually flows through your company, then engineer pipelines where AI handles the repetitive middle and people approve the edges. Built and operated by a team that ships its own AI products, available by the project or by the month across US, EU, and APAC hours. The result is not a science project but a working pipeline your team trusts — one that quietly does the repetitive work every day while your people focus on the judgement calls only they can make.
Automation Audit & Process Mapping
We sit with your operators, map the real process (not the org-chart version), and rank automation candidates by hours saved and risk.
AI Pipeline Engineering
Document intake, classification, extraction, summarization, generation — chained into pipelines that run on schedule or on trigger, with retries and logging.
Orchestration Across Your Stack
CRMs, ERPs, spreadsheets, e-commerce backends, messaging platforms — we connect them through their APIs so data moves itself instead of being re-typed.
Human-in-the-Loop Controls
Approval steps, confidence thresholds, and escalation paths keep people in charge of decisions while AI does the preparation.
Operational Dashboards
Live visibility into what your automations did, what they cost, and where they need attention — no black boxes.
Run & Improve
We operate what we build: monitoring, failure recovery, and monthly improvement cycles as your volumes and edge cases grow.
How an automation goes from audit to autopilot
We start where the pain is, ship something that works fast, and expand from proof — not from a big-bang rollout that stalls before it earns anything back.
Process audit
We sit with the people who actually do the work and map the real process step by step, including the exceptions nobody wrote down. Then we rank the candidates by hours saved and risk, and pick the first automation to build.
Build the first pipeline
We engineer the highest-value workflow first: intake, classification, extraction, and generation, plus the approval steps that keep a human in charge. It runs on a schedule or a trigger, with retries, logging, and a dashboard from day one.
Operate & expand
We measure the hours the first pipeline gives back, then extend into the neighbouring processes it touches. We operate what we build — monitoring failures, handling new edge cases, and improving on a monthly cycle.
Ways to work with us
Automation can start small and grow. Pick the model that matches your appetite; each one wraps around the tools you already run rather than replacing them.
Automation audit
A fixed-scope engagement to find and rank your best automation opportunities. You come away with a prioritised map of what to automate, the hours each item could save, and a recommended first build.
Fixed-scope build
One clearly defined pipeline, delivered to a set scope and timeline with acceptance criteria you sign off on. Ideal for taking a single painful process off your team's plate with a predictable commitment.
Monthly team
An embedded team that keeps automating across your operation month after month as new processes qualify and volumes grow. Best when automation becomes an ongoing program rather than a one-off project.
The stack we build on
We connect the tools you already use rather than replace them, and we keep humans in control of the decisions that matter. A typical automation draws on:
Orchestration
n8n-class workflow orchestration for pipelines that run on a schedule or a trigger, with retries, idempotency, and full logging — so a failed step recovers instead of silently dropping work.
Business-system integrations
We connect Google Workspace, Jira, GitLab, Notion, Confluence, CRMs, ERPs, spreadsheets, and e-commerce backends through their APIs, so data moves itself between systems instead of being re-typed by hand.
AI processing steps
Document intake, classification, extraction, summarisation, and generation powered by the model best suited to each step — grounded in your data wherever accuracy matters.
Human-in-the-loop controls
Approval steps, confidence thresholds, and escalation paths, so people review the edges while the AI handles the repetitive middle. Nothing high-stakes ships without the sign-off you configure.
Scheduling & monitoring
Crons and event triggers drive the pipelines; dashboards show what ran, what it cost, and what needs attention. No black boxes — you can always see what the automation did and why.
Flow Kit
Open-source proof of how we think about workflow: Flow Kit chains reference creation, image and video generation, narration, and YouTube publishing into one command-driven pipeline — 34 pre-built skills orchestrated end to end.
View ProjectFlowboard
Workflow design made visual: Flowboard composes model, product, scene, and video nodes on a canvas, and the prompts and execution order generate themselves from the graph — the same orchestration discipline we apply to business pipelines.
View ProjectSee it in production
We build automation the same way we build our own. Read how these pipelines came together:
AI Agent Jira Bot
A Google Chat agent that runs a sprint board end to end — reading and writing across Jira, GitLab, Notion, Confluence, Crashlytics, Drive, and Calendar through MCP tools, workflows, and scheduled crons.
Read the case studyPostforge
A scheduled generate-and-publish pipeline that turns a content plan into images and vertical video and posts them to Reels, TikTok, and Shorts on time — multi-page, local-first, with per-page review gates.
Read the case studyFlow Kit
Thirty-four orchestrated skills chained into one command-driven pipeline — the same orchestration discipline we bring to business workflows, in the open.
Read the case studyFAQ
What does a typical first automation look like?
Most clients start with one painful process — invoice intake, order reconciliation, content localization, report assembly. We deliver the first working pipeline in weeks, measure the hours saved, then expand to neighboring processes.
Do we need to replace our current tools?
No. Our automations wrap around the systems you already use and connect them through their APIs. The goal is fewer copy-paste steps between tools, not a rip-and-replace project.
How soon will we see a return on an automation?
Most clients recover meaningful hours within the first few weeks of the first pipeline going live, because we deliberately automate the highest-volume, most repetitive process first. We measure the time saved so the return is a figure you can see, not a promise you have to take on faith.
How do you keep people in control of an AI automation?
Every pipeline is built with human-in-the-loop controls: approval steps for anything consequential, confidence thresholds that route uncertain cases to a person, and escalation paths when something looks wrong. The AI prepares the work; your team keeps the final say.
Which systems can you integrate with?
If it has an API, we can almost certainly connect it. In practice that means Google Workspace, Jira, GitLab, Notion, Confluence, common CRMs and ERPs, spreadsheets, messaging platforms, and e-commerce backends. We integrate around your existing stack rather than asking you to migrate.
What happens if an automation breaks?
Pipelines are built with retries, idempotency, and logging, so a transient failure recovers on its own and a real problem is captured with enough context to fix fast. Because we operate what we build, monitoring and failure recovery are part of the engagement, not an afterthought.