plugnpl.ai
Use Cases

Enterprise AI that delivers real business outcomes

A look into customer projects from insurance, logistics, manufacturing and a group of companies with many sites, anonymised, with real results and honest stumbling blocks.

Book a free consultation
From customer projects

What teams actually built

A selection, not the full list. Every project is anonymized, what stays is the process, the approach and the result.

Logistics

Overhang dispatch: an agent team prepares the decision

One recommendation per overhang, ready before the shift starts.

Decision preparer
Insurance

Checking assistant: contract data matched against the policy

The whole portfolio becomes checkable instead of a sample.

Reconciliation agent
Insurance

Sales assistant: the annual review prepared in one minute

Five source systems merged into one briefing.

Briefing agent
Manufacturing

Complaints: 30% more cases handled by the same team

Hundreds of mails a day become structured, traceable cases.

Inbox to data
LogisticsDecision preparerBuilt with Multi-agent workflow

Overhang dispatch: an agent team prepares the decision

Groupage cooperation of mid-sized forwarders

The situation

  • A hub network with 6 transshipment hubs and around 600 line hauls to partner depots
  • Every morning, at every hub: freight that did not fit on the night line. Send it along, add a truck or bundle it?
  • Warehouse stock and line history sit in separate systems, and extra trips often run at a negative margin

What we built

  • A coordinating agent steers two data agents, one for live stock and one for line history
  • A fixed rule set: relevance, then capacity, then profitability, with seasonal logic from the same month a year earlier
  • Exactly one recommendation per overhang: regular line, cost reference for an extra trip, or check by hand

Results

~7 min

analysis per hub, finished before the shift starts

≤ 50

target depots per hub and day, still answered in minutes

1

reasoned recommendation per relevant overhang

What we learned

  • No big bang: one hub, one shipment type, then step by step
  • Acceptance comes from traceability. Capacity, contribution margin and data basis come with every recommendation
  • Invent nothing: missing data is marked as missing, never estimated
InsuranceReconciliation agentBuilt with Microsoft Copilot in Teams

Checking assistant: contract data matched against the policy

Industrial insurance broker, transport and logistics

The situation

  • Contract data is entered by hand in the portfolio system, the policy is the binding document, and the two have to match
  • Quality assurance checks field by field, with policies partly in English and in other date and amount formats
  • A six-figure contract portfolio, so checking by hand only ever reaches a sample

What we built

  • Contract number in, consolidated comparison table out, straight into Teams
  • 12 fixed check fields with a mapping from document term to system field, English synonyms included
  • Normalisation before the comparison, with a status per field: match, deviation with both values, or missing

Results

Seconds

per contract, instead of reading the policy and comparing the screen

12

check fields, always the same, always normalised

Full portfolio

checkable instead of samples, with data gaps surfaced

What we learned

  • A hard rule: never guess. Anything missing or unreadable is reported as NULL
  • A side effect nobody expected: the agent uncovers systematic data gaps
  • The person decides what happens next, correct it in the system or take it up with the insurer
InsuranceBriefing agentBuilt with Microsoft Copilot Studio

Sales assistant: the annual review prepared in one minute

Industrial insurance broker, transport and logistics

The situation

  • Before every annual review: contracts, premiums, open items, tickets and correspondence collected from 5 or more systems
  • Correspondence is often filed without a contract reference, so finding it swallows hours
  • Customer knowledge sits in people's heads and is lost when the account manager changes

What we built

  • An agent in Microsoft Copilot Studio, right inside Teams and M365 Copilot
  • Completeness comes from the database, the language model prioritises and summarises
  • Two skills, annual review briefing and quick customer check, plus six starter prompts

Results

1 min

runtime instead of 2 to 3 days of preparation per customer meeting

≤ 1 day

data age instead of exports up to 12 months old

5+

source systems merged into a single briefing

What we learned

  • The biggest lever was current data, not the model
  • In the first test the largest contract by premium was missing, which is why the split between database and model is now explicit
  • The department was the test group, with a weekly comparison against real, manually prepared reviews
ManufacturingInbox to dataBuilt with plugnpl.ai

Complaints: 30% more cases handled by the same team

Kitchen manufacturer, more than 1,000 employees

The situation

  • Every fourth hour in sales support went into complaints
  • Hundreds of emails a day, in dozens of formats and languages, arriving at different mailboxes
  • Entry by hand, often incomplete, so mandatory details had to be requested or added later

What we built

  • One central mailbox, so every complaint arrives at a single point
  • The AI reads the mails and transfers their content into a standard layout
  • Missing mandatory fields are detected and the follow-up question is drafted by AI, sent only after review
  • A complaints database builds up on its own, text and image

Is the process broken? We have never handled that many complaints in a single day.

Quality assurance, kitchen manufacturer

Results

+30%

complaints handled, with the same team size

−80%

manual data entry through standardisation

100%

traceability of every AI interaction

What we learned

  • Human in the loop for every follow-up question that reaches a customer
  • Every AI interaction is tracked, so the trail stays complete
  • Specialists gain time for the critical cases instead of repetitive data entry
See the platform behind it

Two more projects, a request portal and an inspection app for a group of companies with 38 sites, were built with Launchpad. They are on the Launchpad page.

All customer projects are anonymized. The content comes from the talk "AI in the Mittelstand: use cases from practice" at the AI Circle Mittelstand, Düsseldorf, 24 September 2026. Projects are delivered by just experts, the team behind plugnpl.ai.

Transferable

Five patterns, and where they fit in your company

The industry changes, the pattern stays. Find yours and start there.

Decision preparer

Fits when recurring operational decisions rely on data from separate systems: tour planning, stock dispatch, shift planning, maintenance priorities

To the case

Reconciliation agent

Fits when system data and documents have to match: invoice against order, master data against supplier papers, compliance records

To the case

Briefing agent

Fits when information from several systems is collected before a meeting: customer and supplier talks, service visits, handovers to colleagues

To the case

Inbox to data

Fits when unstructured mails are meant to become structured cases: complaints, service requests, incoming orders

To the case

Build instead of buy

Fits when company-specific processes fit standard software only through expensive customising: approvals, requests, inspection protocols

To the case

What it takes: structured data, a subject expert with time for feedback and a clearly bounded first use case.

What we learned

Six factors that decide whether an AI project lands

Out of customer projects, and applicable long before the first line of code.

01

Start with one concrete process

The annual review, the overhang, the complaint. Not "we need AI".

02

Bring the department in from day one

A test group, short feedback loops, a weekly comparison against real work results.

03

Data is the biggest lever

Current, released data does more for the result than the better model.

04

Invent nothing

Completeness comes from the database. Anything missing is reported as missing, never estimated.

05

The AI prepares, the person decides

Human in the loop for every recommendation, every follow-up question and every approval.

06

Build on what is already there

Teams, M365, Azure, the platform you already run. No new tool for the people using it.

Across the enterprise

One platform, many governed use cases

Filter by your industry to see the most relevant use cases.

Customer service automation

Governed assistants that resolve tickets, draft replies and surface the right knowledge — grounded in your own systems, with full audit trails.

Enterprise knowledge search

Ask questions across SharePoint, SQL, Dynamics 365 and SAP. Permission-aware RAG keeps every answer grounded in your data.

Finance & accounting

Automate invoice processing, reporting and reconciliation. Add human-in-the-loop approval for every sensitive step.

Multi-agent workflows

Orchestrate complex business processes across departments with prebuilt, governed agents working together.

Software & app development

Turn AI-assisted coding into a governed capability with Launchpad — ship internal apps in hours, not months.

Compliance & document review

Summarize contracts, flag risks and check policies — with complete transparency for legal and compliance teams.

Let's map AI to your use case.

Book a free consultation and we'll show you — on your own use case — how governed AI works for your people, in your own cloud.

Book your appointmentTry the demo
30-minute call · tailored to your use case · no obligation