Infrantic

Proof from client work

Real operational problems, working systems

The problems clients brought us, the structure we put around them, and what changed for the team. Details are simplified to protect client specifics.

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Detailed case studies
3+
Featured end-to-end project
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Operational gaps they close
6+

Case studies

Three systems, in detail

Each one started as a familiar operational problem. Here is what we built and what changed for the team.

  1. Manufacturing and supply

    Inventory and procurement automation

    Stock levels, supplier requests, approvals, and purchase orders connected into one flow.

    Built with

    • n8n
    • OpenAI
    • Supabase
    • Slack
    • Gmail
    • Jira
    Challenge
    Low-stock alerts, supplier selection, approvals, and purchasing were handled by hand across several people and tools, so reorders ran late and were hard to trace.
    What we built
    1. 1A stock form and sheet feed one trigger
    2. 2An AI agent checks history and drafts the RFQ
    3. 3Approvals go to Slack with the context attached
    4. 4Approved requests raise the PO and update stock
    What changed
    • Reorders start when stock runs low, not when someone notices
    • A complete trail of who approved what
    • Far less copying between sheets, email, and chat
  2. Engineering services

    AI technical document proofreader

    A controlled first-pass review for technical PDFs and scanned files.

    Built with

    • Gemini
    • Python
    • Next.js
    • Supabase
    Challenge
    Senior reviewers checked the same predictable issues across long PDFs and scans, which made reviews slow and inconsistent.
    What we built
    1. 1PDFs and scans are uploaded into a review workspace
    2. 2AI runs a first pass against an approved terminology list
    3. 3Likely issues are highlighted for the reviewer
    4. 4The reviewer accepts, edits, or dismisses each flag
    What changed
    • Every document gets the same structured first pass
    • Reviewers spend their time on judgement, not repetition
    • Terminology stays consistent across documents
  3. Professional services

    Role-based task management

    Executor, checker, and approver workflows with rework loops and overdue visibility.

    Built with

    • Next.js
    • PostgreSQL
    • Supabase
    • Email alerts
    Challenge
    Nobody clearly owned each step between the people doing, checking, and approving the work, so tasks stalled and rework went unrecorded.
    What we built
    1. 1Roles defined for executor, checker, and approver
    2. 2Each task moves through explicit review stages
    3. 3Rejections loop back with a reason attached
    4. 4Overdue work and workload visible on one board
    What changed
    • A named owner at every stage
    • Consistent review steps across the team
    • A full history of rejections and rework

Patterns that repeat

Different clients, the same building blocks

Most of our systems are combinations of a few proven patterns. Recognising yours is usually the first step.

  • Trigger, decide, act

    An event in one system (a low stock level, a new form) starts a flow that gathers context, applies rules or AI, and acts in another system.

    • Procurement
    • Alerts

  • One shared record

    A single internal ID follows an order or request through every stage, so departments update one thing instead of reconciling many.

    • Garment orders
    • Order tracking

  • AI first pass, human final say

    AI does the repetitive first read of documents and flags likely issues. A person reviews and decides.

    • Document review
    • Proofreading

  • Role-based ownership

    Executor, checker, and approver roles with rework loops, so every step has a name next to it.

    • Task management

  • Exceptions, not everything

    People are notified about what needs attention, not every update. Overdue and at-risk work surfaces itself.

    • Overdue alerts
    • Risk visibility

  • One view of the work

    A dashboard built from live data replaces the weekly report that used to be assembled by hand.

    • Reporting
    • Dashboards

Behind each case

How these systems came together

  1. 01

    Map the current workflow

    Who does what, in which tool, and where the work waits. The map usually shows the first system to build.

    • Process map
    • Gap list
  2. 02

    Design the structure

    Decide what triggers the flow, where people approve, what the shared record looks like, and which tools stay.

    • Flow design
    • Data model
    • Checkpoints
  3. 03

    Build and connect

    Automations, integrations, and any custom screens, built in the client's own accounts.

    • Working system
    • Integrations
    • Documentation
  4. 04

    Run it with the team

    The people who own the workflow run it with us for the first cycles, and we refine based on what they hit.

    • Training
    • Refinements
    • Handover

Outcomes

What we measure after launch

We judge a system by the operational change it produces, not by how much it automates.

  • Time from trigger to action, before and after
  • How often a step needed manual chasing
  • Errors caught before they reached a customer or supplier
  • Hours the team gets back each week
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Signals we track

Cycle time
How long an order, approval, or request takes end to end.
Touches
How many people handle the same piece of information.
Exceptions
How early problems become visible, and to whom.
Adoption
Whether the team actually uses the system without being reminded.

What is slowing your business down?

Let's make your business easier to operate

Infrantic exists to simplify the machinery behind modern businesses. Tell us where the friction is, and we'll design the system that removes it.

  • We find the friction
  • We design the system
  • We automate the work
  • We build the software
  • We connect everything
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