Manufacturing Intelligence
Operation planning, “team × time” dependencies, material readiness, quality control and explanations of deviations.
first applied system
R&D · data · production planning
ModulDim AI Lab
We research how AI can help the production manager identify risks early and compare options. Reliable data and testing on our own projects come first; expansion comes later.
Status in development
Focus domain-specific AI
Foundation real ModulDim processes
Mission
Not another chatbot. Intelligence that understands the physical product.
General-purpose models handle general knowledge well. But a specific project's schedule, operation dependencies, engineering modes and a particular home's history live in ModulDim's own context.
That is why we are developing a managed AI system, not a single “large model”: domain data, our own quality evaluations, model routing, tools and human decisions where they matter most.
What we need to own
We control what creates our advantage.
ModulDim does not need to own every model or all computing infrastructure. We need to own our product logic and a way to verify that AI genuinely helps.
- 01 / Data
- The structure of projects, production, quality, building systems and service.
- 02 / Evals
- Tasks and metrics that reveal accuracy, risk, cost and latency.
- 03 / Context
- Rules, memory, tools and permissions that make a model part of AI OS.
- 04 / Feedback
- Signals from production and operation that trigger the next improvement.
Research map
Four systems across one lifecycle.
Each direction starts with a measurable task. If a result cannot be verified in a process or product, it is not yet a production AI feature.
Product Intelligence
Turning a lifestyle scenario, site and constraints into a space configuration with visible trade-offs.
research directionAutonomy Intelligence
Forecasting and coordinating energy, water, climate and security with local scenarios and manual control.
research directionLifecycle Intelligence
Digital home passports, service diagnostics, maintenance forecasting and knowledge that feeds back into the next product.
research directionAI system
The model is just one layer of the system.
Closed and open models can work side by side. The choice depends on accuracy, privacy, region, speed, cost and availability.
PRODUCTION LOOP
- 01Requestperson or system
- 02Contextpassport · plan · rules
- 03Routermodel and tool selection
- 04Actionanswer · forecast · signal
CONTROLLED CORE
ModulDim AI OS
Permissions · memory · decision log · security policies · human approval
DEVELOPMENT LOOP
- 01Evalswhat quality means to us
- 02Datasafe real-world + synthetic
- 03Adaptationprompt · retrieval · post-training
- 04Monitoringaccuracy · cost · latency
Rule: in-house control for critical product intelligence; the external ecosystem for models, compute and specialised infrastructure.
Research protocol
From a task to a measurable feature.
A small autonomous team can move fast if it does not start by training a model. First come the task, a reference baseline and a way to measure results.
- 01
Define the task
One role, one workflow, an expected outcome and a risk boundary.
- 02
Build an evaluation
Reference cases, clear scoring and a blind comparison with the current process.
- 03
Collect safe data
Anonymised examples and synthetic scenarios created with domain experts.
- 04
Build the smallest working system
Context, tools and the most affordable model that meets the required threshold.
- 05
Test in the product
Manual QA, a pilot, A/B testing and monitoring of errors, cost, latency and availability.
- 06
Feed experience back into the cycle
Complaints, corrections and new edge cases become the next version of evaluations and data.
Initial register
Benchmarks to start with.
This is a public map of planned evaluations, not a claim of finished models. Statuses will be updated alongside real results.
| ID | Working task | What we measure | Status |
|---|---|---|---|
| EVAL–01 | A “team × time” conflict in the production plan | Conflict recall · false alerts · explanation time | first candidate |
| EVAL–02 | Risk of materials not being ready for an operation | Early detection · cause accuracy · action usefulness | evaluation planned |
| EVAL–03 | An anomaly in a quality checklist | Defect recall · supporting evidence · manual review share | evaluation planned |
| EVAL–04 | Energy and resource scenarios for an autonomous home | Safety · savings · resilience to failures | future direction |
| EVAL–05 | Service diagnostics using the digital passport | Diagnostic path accuracy · time to resolution · explainability | future direction |
Trust by design
Personal data is not raw material by default.
An AI feature must be more than accurate. It needs clear permissions, limits on autonomy, an action log and a safe way to shut it down.
01Private environment
Customer and production data are separated from public research and do not automatically become training data.
02Synthetic tests
Sensitive scenarios are reproduced as controlled tasks with a known answer or error.
03Human in the loop
Decisions affecting safety, money or physical actions have a defined level of human approval.
04Portability
Context, evaluations and logic are not permanently tied to one provider or model.
Conceptual AI illustrations of working principles, not photographs of deployed systems.
Methodological references
From sovereign AI to a lab you can start without billions.
READ / 01Why companies should build their own AIteletype.in ↗
READ / 02How to launch an AI lab on a limited budgetteletype.in ↗
ModulDim's approach is an independent adaptation of these principles to a domain-specific physical product, Ukrainian manufacturing and autonomous living.
An open challenge
Have a process where AI should deliver measurable results?
Describe the task, available data and how to verify the outcome. We will start with an evaluation, not a model.