Process cost
Cut waste in staffing, routing and inference spend.
32%typicalProduction ML that lowers operating cost.
A registered metric, a live process, a system you can roll back.
- 97.3% Planner accuracy
- Planner accuracy
- 14M+ Events processed daily
- Events processed daily
- 61% Fewer false positives
- Fewer false positives
- 81% Documents with no human touch
- Documents with no human touch
Targets from the reference designs on this site, not measured client results.
Not technical? Start here
Most of this site is written for engineers. This part is not.
- What we do
- We find a decision your team currently makes by hand, hundreds or thousands of times a month, and build software that makes it instead — inside the process you already run, not beside it.
- What you get
- One number moves, and you can see it move. We agree which number that is before anything is built, and you get a dashboard on it plus a way to switch the whole thing off if it underperforms.
- What you need
- A process that costs real money and some record of how it has gone. You do not need a data team, a strategy, or to know what any of this is called. If the honest answer is that it is too early, we say so.
Outcomes
Six ways we cut operating cost.
A model sits in the process that spends the money and moves a registered metric.
Fraud and leakage
Score risky events in line with the transaction, with a cost on every miss.
0.79Visual inspection
Catch defects on the line and close the review loop.
96.8%typicalInternal copilots
Answers over your documents, with citations a person can open.
0.94Decision scoring
A score with a kill-switch, an audit trail and a named owner.
74.5%typicalDemand and inventory
Cut overstock, stockouts and overtime with a forecast planners actually use.
2.7%Tech stack
ML and data we run in production.
ML
Data
Services
Work
Reference designs.
Three reference designs. Full write-ups on the work page.
Data PipelinesMetricsReference design
USDDub
Dashboards were six hours behind reality. Now they are five minutes behind.
Data freshness
6 hours→5 minutes
ForecastingOptimizationReference design
Demand Forecasting + Inventory Optimization
Planning ran on a spreadsheet and one growth rate. Stock now follows actual demand.
Weighted MAPE
12.1%→2.7%
LLMRAGReference design
Internal LLM Copilot (Policies + Tools)
Staff asked colleagues instead of reading the policy. Now they ask and get an answer they can check.
Answer accuracy
61%→89.2%
Product teardown
Not sure ML is your answer? Neither are we, yet.
6 questions about your process and your data. You get the engagement that actually fits, the biggest risk we can see, and the next three steps — before you talk to anybody.
Do not do ML yet
No signal recorded and nothing live to attach to. We say so, and tell you what to log.
Discovery pilot
A real process, unproven data. Two weeks to find out whether it is learnable at all.
Proof of concept
Enough data to try. One narrow slice, one honest baseline, real numbers.
Production build
Queryable data, system under load. Model, serving, monitoring and rollback together.
Project calculator
How much would this cost?
Pick a starting point, then adjust the team and hours.
Starting point
Complexity
Pace
Estimated project
Estimated cost €14,500
310 hours across 5 roles · blended €47/h
- Delivery
- 5–7 weeks
- Risk
- low
Well-understood problem shape. A fixed-scope pilot is usually enough to settle it.
Two ways to start: a fixed-scope pilot from €5,000 (2–4 weeks) or a dedicated engineer from €5,000 a month. These rates price larger builds; the written proposal fixes scope and price.
Invitation
Let's Build Something Difficult.
Send the cost line you want moved.
Or start from a price estimate
Start a conversation