Agentic Engineering
Designing agents around defined workflows: the tools they can use, the actions they can take, and the points that require human review.
- Tool use & orchestration
- Permissions & human oversight
- Agent evaluation
Specialized consulting & engineering
AI, engineered
for the work.
We work on the agents, models, data foundations, and physical integrations that turn AI into a working system.
Expertise
Four areas of focus.
A view of the whole system.
A useful AI system depends on how it acts, the data it uses, and the environment it operates in. Our work spans those connections.
AI/ML
Choosing and adapting models for a defined task, then shaping inference and deployment around latency, cost, and reliability.
- Model selection & adaptation
- Inference & deployment
- Evaluation & reliability
Data
Structuring, moving, and validating the data that models and people rely on. Connecting source systems to the applications that use them.
- Architecture & data contracts
- Pipelines & retrieval
- Quality & validation
Physical AI
Connecting models to signals and actions in physical environments, with attention to the conditions in which the system must operate.
- Sensing & connected systems
- Edge inference
- Software & hardware integration
Approach
Start with the work.
Engineer from there.
Start with the decision, workflow, or physical process. Then choose the models, data flows, and interfaces that fit its requirements.
Define the problem
Map the workflow, understand the constraints, and establish what the system needs to do.
Design the connections
Shape the architecture across data, models, tools, and interfaces. Make responsibilities and controls explicit.
Evaluate in context
Test the behavior and failure cases against the environment where the system will run.
A conversation is a good start
Bring the problem.
Let’s think it through.
Tell Nick what you’re building, where it needs to run, and what is getting in the way.