HelmUnit
Case studies

Five industries. Same loop.
Different backlog.

Every engagement starts the same way: a free 30-minute call, one honest question — "what eats the most hours in your business?" — and a first automation live — sometimes in days, sometimes weeks. What we build depends entirely on what we find. Here's how we'd approach it across five industries.

Free 30-minute call firstScale in fractionsYou own everything

Cases 1–3 are drawn from real work delivered by the team at azurevibes.dev. Cases 4–5 use representative figures for those industries.

01
Industry
Construction & calculations
What we'd build
AI document processing → BOM extraction → SharePoint integration
Capacity path
¼→½→¼

From a full day of manual quoting to 45 minutes

The call.

A mid-sized construction contractor comes to the free 30-minute call with a familiar complaint: estimators spend most of a workday building each quote by hand — pulling numbers from project drawings, specs, and BOMs, cross-checking them against supplier pricing, and assembling everything into a document a client can actually approve.

The biggest issue.

Quoting is the bottleneck for the entire sales pipeline. A slow quote means a slower yes — or a competitor's faster one landing first. And the knowledge of how to build a quote correctly lives in two senior estimators' heads, not on paper.

the loop in action · document → automate → improve → monitor

Document

We map exactly how a quote gets built today: which documents get read, which numbers get pulled, which rules get applied, where the judgment calls happen.

Automate

We build an AI pipeline that reads incoming project documents, extracts the calculations and BOM items, and connects into the existing SharePoint — so output lands where estimators already work.

Improve

Early runs surface edge cases — unusual drawing formats, non-standard line items — that we feed back into the system over the following weeks.

Monitor

The pipeline runs continuously, watched and maintained, with exceptions flagged for human review rather than failing silently.

1 day → 45 min
Quote production time — and it no longer depends on one person's head.

Scaling month to month.

Start at ¼ capacity to build and prove the first automation on real project data. Once quoting is live and stable, scale to ½ capacity for a two-month push to extend the same pipeline to change orders. With both automations stable and monitored, scale back down to ¼ — mostly monitoring and small improvements, with room to scale up again for the next project type whenever you're ready.

02
Industry
Environmental & field services
What we'd build
Back-office system + employee portal + field mobile app
Capacity path
¼→1→1 & ½→¼

12 hours a week of paperwork, gone

The call.

An environmental services company describes a familiar back-office problem: field technicians fill out paper forms on-site, which then have to be manually transcribed into spreadsheets back at the office — sometimes days later, sometimes with data-entry errors that only surface during client reporting.

The biggest issue.

Two full-time-equivalent hours per day, across the office team, go into re-typing information that was already captured once in the field. Reporting is always a week behind reality.

the loop, run twice · once for the office, once for the field
Loop 1 — the back-office portal
¼→1

Document

We walk the full path a piece of field data takes: from the technician's clipboard to the final client report, and every hand-off in between.

Automate

We build the back-office system and employee portal that replace the scattered spreadsheets — one place for jobs, records, and reporting.

Improve

We tune the portal around how the office team actually works day to day, cutting steps out of the most common reports.

Monitor

The portal becomes the office's system of record, watched so data-quality issues surface immediately, not at month-end.

Loop 2 — the field mobile app
1 & ½→¼

Document

With the office side live, we map the technician's on-site workflow in detail — what they capture, in what order, and under what conditions.

Automate

We build the field mobile app and connect it to the back-office over an API, so technicians capture data once, on-site — and it flows straight in with no re-typing.

Improve

We refine the app's field layout after the first couple of weeks of real technician use, cutting extra taps out of the most common entries.

Monitor

The whole loop — field to office — runs as standard daily operations, watched so nothing drifts and issues surface immediately.

12 hrs/week
Of paperwork eliminated across the back-office team — paper forms are gone.

Scaling month to month.

Because this is two loops, capacity tracks the build. Start at ¼ to document how field data moves today, then step up to full capacity to build the back-office portal (loop 1). For the heaviest stretch — the back-office API plus the field mobile app (loop 2) — push to 1 & ½. Then scale back down to ¼ for monitoring and small improvements as field teams ask for them.

03
Industry
Wholesale / order processing
What we'd build
AI extraction pipeline → automatic quote & invoice generation
Capacity path
¼indefinitely

9 out of 10 orders now quote and invoice themselves

The call.

An SMB selling through unstructured order emails — no fixed format, no portal, just however the customer chose to write it — describes staff spending most of each morning retyping order details into their system before anything can be quoted or invoiced.

The biggest issue.

Every order requires a human to read an email, interpret what's being asked for, and manually re-key it. Mistakes creep in. Order-to-invoice takes hours, sometimes a full day, purely on admin — before any actual fulfillment work begins.

the loop in action · document → automate → improve → monitor

Document

We collect a sample of real incoming order emails and map every variation staff have learned to handle by instinct.

Automate

We build an AI extraction and calculation pipeline that reads incoming order emails, pulls out the details, and generates the quote and invoice — with humans reviewing rather than retyping.

Improve

We tune the extraction against several weeks of real order volume, steadily narrowing the set of orders that need manual handling.

Monitor

The pipeline runs on every incoming order, with anything it isn't confident about routed to a person instead of guessed at.

9 of 10 orders
Quoted and invoiced automatically — staff only review rather than retype.

Scaling month to month.

Start at ¼ capacity to prove the pipeline on real order data within the first month. Then simply stay at ¼ capacity as an ongoing arrangement — not just maintenance, but handling the tasks that surface after the first project: new order formats as customers appear, continuous tuning, and steadily reducing whatever manual work is left. A good reminder that scaling up isn't always the point — sometimes a quarter is exactly the right size, indefinitely.

04
Industry
Logistics & freight
What we'd build
AI document pipeline → invoice-vs-shipping reconciliation with discrepancy flagging
Capacity path
¼→½→¼

Turning a stack of shipping documents into one clean record

The call.

A freight/logistics operator's 30-minute call surfaces a pattern common across the industry: a single international shipment can generate dozens of separate documents — bills of lading, CMRs, customs paperwork, freight invoices — each arriving in a different format, from a different party, at a different time.

The biggest issue.

Reconciling freight invoices against shipping documents consumes hours of staff time per week, and discrepancies — a wrong weight, a missing customs code — are often only caught after a client has already been billed incorrectly.

the loop in action · document → automate → improve → monitor

Document

Map exactly which documents arrive, in what formats, from which parties, and what "correct" reconciliation looks like today.

Automate

Build an AI document pipeline that reads incoming shipping documents and freight invoices, cross-checks them against each other, and flags discrepancies before billing — rather than after.

Improve

Tune the system against the specific document formats this operator's carriers and customs partners actually use.

Monitor

Keep the pipeline running against every shipment, with a live view of reconciliation status instead of a weekly scramble.

Caught before billing
Hours per week returned to the back office, and discrepancies caught before invoices go out instead of after.

Scaling month to month.

Start at ¼ capacity to prove the reconciliation pipeline on one carrier relationship and a real batch of shipments. Scale to ½ capacity to extend the same pattern across multiple carriers or add customs-document handling. Scale back down to ¼ for ongoing monitoring once the core flow is stable.

05
Industry
Manufacturing & distribution
What we'd build
System integration connecting production, inventory & order systems via API
Capacity path
½→¼

Keeping inventory and production data in sync — automatically

The call.

A manufacturing/distribution business's 30-minute call reveals a common pain: production data lives in one system, inventory in another, and orders in a third — with someone manually reconciling all three every day just to know what can actually be shipped.

The biggest issue.

Nobody fully trusts the inventory numbers, because they're always a day behind reality. Sales staff quote delivery dates that production sometimes can't hit, because the systems aren't talking to each other.

the loop in action · document → automate → improve → monitor

Document

Map how production, inventory, and order data currently move (or fail to move) between systems, and where the manual reconciliation actually happens.

Automate

Build a system integration connecting production, inventory, and order systems via API, so stock levels update automatically as production completes and orders are placed — no manual reconciliation.

Improve

Refine thresholds and alerts (low stock, production delays) based on how the operations team actually responds to them in the first weeks.

Monitor

Keep the integration running and watched, with a live dashboard replacing the daily manual check.

Real-time trust
Inventory numbers the sales team can actually trust, and delivery promises that match what production can deliver.

Scaling month to month.

Start at ½ capacity — connecting three systems is a heavier first build than a single-document pipeline. Once integrations are live and stable, scale down to ¼ capacity for monitoring, with the option to scale back up for the next integration (e.g., connecting a supplier's system next).

The pattern

Five backlogs, one way of working

Start with the call.

Every engagement opens with a free 30-minute call and one honest question — then a first automation live on your real data within weeks.

Scale in fractions.

Push up to ½ or full capacity to clear a backlog, then scale back down to a quarter for monitoring. Sometimes a quarter is the whole story.

Nothing rots silently.

Whatever we build stays documented, monitored, and yours — with hours and euros saved reported every month.

What's eating your team's hours?

Every approach above starts the same way — a free 30-minute call and one honest question. We won't know what's worth automating in your business until we hear about it from you.

Book a free 30-minute call
or write to crew@helmunit.com · reply within one business day
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