Show the workflow
Explain what people do today, what information they use, and where time, consistency, or visibility is lost.
AI in practice
What changes when AI enters a real workflow.
Real delivery results · clients under NDA
Three anonymized examples based on systems delivered by Lanexas. The client identities remain protected by NDA; the operating problem, human decision, and measured result stay visible.
Start with the operation
You do not need to arrive with a model in mind.
A useful AI project starts with a repeated decision, a costly manual process, or a risk the team sees too late. We define what should improve, where human judgement remains, and how the result will be measured before choosing the technical mechanism.
Bound the decision
Define what the system may recommend or automate, what evidence it must expose, and when a person takes over.
Measure the change
Compare the operational baseline with the delivered workflow instead of treating model output as the business result.
Delivered examples
The business workflow before the architecture.
Each example begins with an operational problem that a non-technical team could recognize. The detailed case study remains available for readers who want the engineering context.
01 / INVENTORY
Planning stock before it becomes a problem
Wholesale supplier and multi-warehouse distributor
Operating scale
Multiple warehouses, tens of thousands of SKUs, and thousands of orders per day
Before
Purchasing teams combined reports, spreadsheets, and individual judgement to decide what to order. Popular products could run out early while slower items accumulated in storage.
What Lanexas built
A forecasting service estimated demand by SKU and warehouse, identified stockout risk, and recommended which product to replenish, where, and in what quantity.
In daily work
A purchasing or planning specialist reviewed every recommendation and could approve or change it. The system supported the decision; it did not place orders autonomously.
Operational change
The team spent less time assembling recommendations and could see potential shortages and excess stock earlier.
Measured delivery results
IndicatorBeforeAfterChange
- Indicator
- Time to prepare purchasing recommendations
- Before
- 2–3 hours
- After
- 20–30 minutes
- Change
- ≈80% less time
- Indicator
- Out-of-stock rate for high-demand SKUs
- Before
- ≈8%
- After
- ≈5%
- Change
- 35–40% lower
- Indicator
- Excess inventory
- Before
- Baseline level
- After
- ≈12% lower
- Change
- ≈12% lower
02 / FLEET
Helping dispatchers assign the right vehicle faster
Logistics and transport operator with its own fleet
Operating scale
Hundreds of vehicles and hundreds to thousands of daily shipments
Before
Dispatchers manually compared vehicle location, capacity, driver schedule, and vehicle condition for every order. At higher volume, assignment slowed down and routes or vehicle usage became less efficient.
What Lanexas built
The system compared available vehicles and orders, recommended the best assignment, estimated completion time, and warned about potential delays.
In daily work
The dispatcher saw the recommendation and supporting operational context in one interface. The final vehicle assignment remained with the dispatcher.
Operational change
Assignments became faster, fewer manual checks were needed, and each dispatcher could coordinate more shipments at once.
Measured delivery results
IndicatorBeforeAfterChange
- Indicator
- Time to assign a vehicle
- Before
- 5–7 minutes
- After
- 1–2 minutes
- Change
- ≈70% less time
- Indicator
- Empty mileage
- Before
- ≈18%
- After
- ≈15%
- Change
- 15–17% lower
- Indicator
- Delayed shipments
- Before
- 11%
- After
- 8%
- Change
- ≈27% lower
03 / PAYMENTS
Directing fraud analysts to the transactions that matter
Payment service and fintech platform
Operating scale
Hundreds of thousands to millions of transactions per day
Before
Static rules sent many transactions to manual review. Normal payments could become false positives, while analysts had to inspect a broad queue with limited prioritization.
What Lanexas built
A risk-scoring workflow evaluated transaction history, amount, user behavior, and other signals, then recommended approval or additional review and exposed the reasons behind suspicious activity.
In daily work
The risk and fraud team saw prioritized transactions and the contributing signals. A specialist retained the final decision in disputed or higher-risk cases.
Operational change
Manual review became more targeted, initial assessment accelerated, and the fraud team carried a smaller low-value review load.
Measured delivery results
IndicatorBeforeAfterChange
- Indicator
- Transactions sent to manual review
- Before
- ≈4%
- After
- ≈2.8%
- Change
- ≈30% lower
- Indicator
- False positives among alerts
- Before
- ≈25%
- After
- ≈18%
- Change
- ≈28% lower
- Indicator
- Initial suspicious-transaction assessment
- Before
- 40–60 seconds
- After
- 10–15 seconds
- Change
- ≈70% less time
A practical first conversation
Bring us the process, not an AI specification.
Show us a workflow that consumes time, produces inconsistent decisions, or reveals problems too late. We will help determine whether AI creates enough value there—or whether simpler automation is the better answer.
- No model or technical brief required
- Human review and fallback behavior defined explicitly
- Success tied to an operational baseline
Have a product challenge?
Start with the problem. We will help shape the next step.
Send a short note first. If there is a fit, the next step is a no-charge 30-minute call with a founder. We reply within two business days.