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Real projects. Before & after workflows. ROI numbers you can verify. Browse by category or scroll to find the problem that looks like yours.

Document Automation · Logistics

From 40 Hours of Data Entry to Zero

Three people, 10,000+ invoices a month, a 12% error rate — and two failed automation attempts. Now every invoice flows through automatically: classified, extracted, routed to the ERP in minutes. 99% accuracy. The team that used to key data is focused on vendor negotiations.

Before
Manual entry → Spreadsheet → Email routing → 3 days / 12% errors
After
Invoice in → AI classify & extract → Auto-route to ERP → Minutes / 99% accuracy
Annual savings€155K
Error rate12% → <1%
How we calculated this

3 FTEs redirected to higher-value work (est. €135K loaded salary) + error remediation and rework costs eliminated (est. €20K/yr).

Drowning in manual data entry? Let’s fix that →
PythonAzure Document IntelligenceFastAPIPostgreSQL
AI Agents · SaaS

5,000 Conversations a Day — Without Hiring

5,000 support tickets a day, 8-hour response times, and an enterprise client questioning the SLA. Now 78% of conversations resolve automatically — right answer, no human in the loop. Complex cases still escalate, but the team chooses which problems deserve their time.

Before
Email inbox → Manual triage → Agent reply → 8h avg response
After
Message in → AI agent + KB lookup → Auto-resolve or escalate → <2 min response
Annual savings€210K
Auto-resolved78% of tickets
How we calculated this

Avoided hiring 5 additional support agents to handle volume growth. At ~€42K loaded cost per seat: 5 × €42K = €210K/yr in headcount not added.

Support queue out of control? Talk to us →
LangChainGPT-4oPineconeFastAPIRedis
Process Automation · Enterprise

The Workflow Engine That Replaced 6 Spreadsheets

Six spreadsheets, three email chains, and approval decisions that took days. Now everything moves through one platform — requests routed intelligently, approvals automated where rules allow, every item tracked in real time. What used to take days happens the same day.

Before
6 spreadsheets → 3 email chains → Manual approval → Days of lag
After
Request in → Smart routing → Auto-approvals → Live tracking
Annual savings€105K
Approval cycleDays → same-day
How we calculated this

6 team members reclaiming ~2h/day previously spent on manual routing and approval chasing. 6 × 2h × 250 days × €35/h (fully loaded) = €105K.

Approval chains slowing you down? We can help →
PythonCeleryReactDocker
Web Application · Analytics

Ask Your Data a Question. Get an Answer.

A question goes to Slack at 9am, the data team writes a SQL query, the answer arrives next morning — too late. Now stakeholders type a question in plain English and get an instant answer with visualisation. No SQL, no bottleneck, no next-day wait.

Before
Business question → Slack data team → SQL query written → Report next day
After
Type question → AI → SQL → Auto-visualise → Instant answer
Annual savings€78K
Query timeNext-day → instant
How we calculated this

Recurring BI contractor reports eliminated (€39K/yr) + 12h/week of senior analyst time freed at €31/h × 50 weeks (€39K).

Waiting on the data team for answers? Let’s talk →
Next.jsOpenAID3.jsSupabase
Website · Local Business

A Phone Repair Shop That Looks Like It Belongs in 2026

No website, no online trust signals — every searcher who found nothing moved on. caremyphone.de now answers every question before prospects call — services, pricing, reviews. Bilingual, mobile-first, 98/100 PageSpeed. Built in 3 weeks.

Before
No web presence → Word of mouth only → Zero online trust signals
After
Google search → Fast bilingual site → Walk-ins & bookings
PageSpeed score98 / 100
Launch time3 weeks
Need an online presence that actually converts? Start a conversation →
HTML/CSSJavaScriptResponsiveSEOPerformance
Document Automation · Healthcare

From 3-Day Backlog to Same-Day Patient Intake

Receptionists transcribing paper forms field by field into the EHR — errors in patient records, a 3-day backlog, and GDPR exposure that hadn’t been audited in months. Now intake forms feed directly into the EHR same-day at 97%+ accuracy, validated via HL7 FHIR API and archived in GDPR-compliant storage.

Before
Paper form → Manual transcription → EHR data entry → 3-day backlog
After
Scan / upload → AI extraction + validation → FHIR API sync → Same day / 97% accuracy
Annual savings€175K
Intake backlog3 days → same-day
How we calculated this

~7.5 FTE-hours/day of transcription eliminated across the clinic network (€47K) + reduced GDPR exposure (€40K risk premium) + same-day processing unlocks patient throughput gains (€88K).

Paper intake still slowing your clinic down? Let’s talk →
PythonAzure Document IntelligenceFHIR APIPostgreSQL
Process Automation · Finance

Month-End Close in 4 Days, Not 12

12 working days every month, four analysts, hundreds of manual reconciliation steps, a 30% rework rate. Now month-end close takes 4 days — transactions ingested, matched, exceptions flagged automatically. Audit-ready reports generate on schedule. The analysts review edge cases, not spreadsheets.

Before
Raw feeds → Manual matching → 30% rework → 12-day close
After
Transaction feeds → AI reconciliation engine → Exception queue → 4-day close / 99% accuracy
Annual savings€175K
Close cycle12 days → 4 days
How we calculated this

4 analysts × 8 working days recovered per monthly close × 12 months × €365/day = €140K direct labor + restatement and rework costs eliminated (est. €35K).

Month-end close still a fire drill? We can help →
PythonpandasAzure OpenAIPostgreSQLFastAPI
AI Agents · E-commerce

800 Returns a Week. Handled in 6 Hours, Not 4 Days.

800+ returns a week processed by hand — emails, photos, policy checks, four overwhelmed people, 4-day waits. Now returns process in 6 hours: requests read, photos analysed, policy applied consistently. Only genuine edge cases reach a human. Team went from 4 people to 1 managing exceptions.

Before
Customer email + photos → Manual policy check → Human decision → 4-day SLA
After
Return request → AI classify + vision check → Auto-decision → 6h SLA
Annual savings€155K
Decision SLA4 days → 6h
How we calculated this

Returns team reduced from 4 FTEs to 1 (3 × €38K = €114K saved) + consistent policy enforcement cuts fraud and chargeback losses (est. €41K/yr).

Returns backlog growing faster than your team? Talk to us →
PythonLangChainGPT-4 VisionFastAPIRedis
Document Automation · Manufacturing

ISO Audit Prep: From 6 Weeks to 3 Days

40+ production lines and every ISO cycle meant 6 weeks of analyst time — pulling records, compiling reports, hunting for gaps. Now production data feeds the compliance system continuously, ISO reports generate on schedule, and documentation gaps get flagged before auditors find them. Next audit prep: 3 days.

Before
Production records → Manual compilation → Gap hunting → 6-week audit prep
After
Live production data → Auto ISO report gen → Proactive gap alerts → 3-day audit prep
Annual savings€130K
Audit prep6 weeks → 3 days
How we calculated this

4 analysts × ~48 weeks of audit prep reclaimed at €1,100/wk = €53K + external ISO consultancy eliminated (€65K) + avoided non-conformance penalties (€12K).

Compliance prep eating weeks of analyst time? Let’s fix that →
PythonAzure AIPostgreSQLReactDocker

€1.18M+ in combined annual savings across these projects. If one of these problems looks like yours, the conversation starts here.

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