Data diagnosis
Process, data-source, loss and risk map plus one recommended pilot question.
Industrial AI · biotech · pharma · foodtech
AI for production: shadow pilot first, demonstrated effect next
We help biotech, pharma, cosmetics and food manufacturers detect early deviation signals, explain likely causes and test decisions against agreed KPIs.
NPNT operating model
The production line remains under technologist and quality-system control
At the first stage AI observes and advises; it does not control equipment or alter a validated process. The team compares predictions with actual outcomes and expert decisions.
01 · Workflow
From a business problem to a verifiable result
AI advice does not replace process regulations, release control, the quality system or the accountable specialist. Final scope depends on data quality, integrations and security requirements.
- 01
Loss
Choose one question: variability, downtime, quality, yield or resource use.
- 02
Observability
Assess sensors, LIMS/MES records, events, gaps and technologist decisions.
- 03
Shadow mode
The model predicts and explains signals without affecting the line.
- 04
KPI before/after
Compare detection lead time, stability, analysis time or avoided losses.
02 · Deliverables
What the system may provide
- 01Early warning of deviation
- 02Forecast of a parameter trajectory
- 03Ranking of likely causes
- 04An action scenario for the technologist with an expected KPI
- 05End-to-end visibility from raw material to release
03 · What the estimate covers
Scope, data and deployment determine the investment
After a short brief, NPNT proposes a first-stage architecture, a budget range and a result criterion. A detailed public tariff is intentionally avoided because integrations and responsibilities differ.
Shadow pilot
Data preparation, model, expert interface, logging and comparison with a pre-agreed KPI.
Industrial environment
Integration, observability, roles, operating procedures, change control and scale-up.
04 · Operations
Operations
Deployment in a Russian or agreed protected environment, with version, quality, access and change control.
Model monitoring
Data quality, drift, false alerts and a regular report.
Scenario development
Monitoring plus improvement cycles and new explanatory features.
Multiple areas
A shared observability layer with a separate KPI for every process.
A bounded first stage
Describe the problem—we will outline the architecture of the first step
An initial assessment does not require medical records, trade secrets or a data dump. Process context and the intended outcome are enough.
- 01The process, product or audience that needs to change
- 02The data or materials already available
- 03Who owns the decision and which criterion will define success
Our first reply will clarify the process, available data, accountable specialist and success criterion. We will then propose a bounded first stage with an indicative timeline and budget.
Describe the problem →