Custom AI, engineered around your operation
We don't sell generic AI software. We engineer intelligence around your specific business, data, workflows and operational constraints.
Custom AI covers the models, agents and software that turn your data into decisions — machine learning, deep learning, multimodal models, LLM integrations and decision systems built for a specific environment rather than a generic one.
Off-the-shelf AI performs well on average problems. Operations are rarely average: data is proprietary, constraints are specific and errors have real cost. Value comes from intelligence fitted to the process it serves.
01
Start from the decision
Identify the decision the system must make, who relies on it and what an error costs.
02
Fit the data strategy
Work with the data you have, design how to capture what is missing, and plan for drift.
03
Choose where it runs
Cloud, on-premise or edge — chosen for latency, privacy and cost, not convenience.
04
Integrate, then evaluate
Connect to existing systems and measure against the operational outcome, not only model accuracy.
- Decision intelligence for operations and quality
- LLM integrations on enterprise knowledge and tools
- AI agents that execute multi-step workflows
- Multimodal models combining image, sensor and text
- Edge AI for real-time inference at the machine
- AI added to existing enterprise software
- Faster, more consistent decisions
- Automated knowledge work
- AI that fits existing systems
One discipline, inside a complete system.
In software
Data pipelines ingest images, sensor streams and enterprise records.
Vision and ML models interpret what is happening and why.
Decision logic, optimization or agents choose the next action.
Commands are issued to controllers and enterprise systems.
Results are measured against the expected outcome.
Every cycle feeds data back to improve models and parameters.
In the physical world
Cameras, sensors and machine signals capture the state of the process.
Objects, defects, positions and conditions are identified in real time.
Constraints of the machine, material and safety envelope are respected.
Robots, actuators and machines execute with precision.
Post-action inspection confirms the physical result.
The process becomes more consistent with every run.
- 01
Sense
Software · Data pipelines ingest images, sensor streams and enterprise records.
Physical · Cameras, sensors and machine signals capture the state of the process.
- 02
Understand
Software · Vision and ML models interpret what is happening and why.
Physical · Objects, defects, positions and conditions are identified in real time.
- 03
Decide
Software · Decision logic, optimization or agents choose the next action.
Physical · Constraints of the machine, material and safety envelope are respected.
- 04
Act
Software · Commands are issued to controllers and enterprise systems.
Physical · Robots, actuators and machines execute with precision.
- 05
Verify
Software · Results are measured against the expected outcome.
Physical · Post-action inspection confirms the physical result.
- 06
Optimize
Software · Every cycle feeds data back to improve models and parameters.
Physical · The process becomes more consistent with every run.
- ↺ Optimize feeds back into Sense — the loop closes.
Let’s build
Have a process this could change?
Describe the operation, the data and the constraints. We'll outline how we would engineer it.
Something else? Talk to Dproots