Research direction
Intelligence that can show its work.
We are exploring how AI can support enterprise decisions with explicit constraints, traceable sources and human judgment. These questions guide our product design.
Our thesis
A useful answer needs more than fluent language. It needs the right context, a check against the rules and a clear path back to its sources. That is the standard we are designing toward.
Research areas
Questions behind the product.
- 01
Neuro-symbolic configuration
How can a system that understands language also prove that what it proposes can be built?
Exploring how language models can propose candidates while constraint solvers check them against modeled rules, and how to find alternatives when requirements conflict.
- 02
Industrial knowledge representation
How should products, processes and costs be modeled so knowledge survives decades of change?
Investigating a shared model of products, rules and relationships, with versions and source history that can survive changes in business systems.
- 03
Grounded search and knowledge capture
How do drawings, legacy rule tables and expert conversations become a model a machine can reason over?
Exploring extraction and retrieval with source references, permission boundaries and human review of captured rules.
- 04
Verifiable agents
When is it safe to let an agent act, and how do you know?
Studying explicit action permissions, checks before execution, approval gates and traces that help people understand what an agent did.
- 05
Optimization under uncertainty
How do you price, promise and plan when costs, capacity and supply all move at once?
Exploring decision support that makes assumptions, competing objectives and uncertainty visible to the person making a commitment.
Evaluation priorities
Make reliability measurable.
Our evaluation approach will test against defined product rules and reviewed reference cases. These are the measures we plan to use as prototypes develop.
- Constraint violation rate
- How often proposed outputs break the rules represented in a test model.
- Provenance coverage
- The share of values traced to a source, a rule or an approver.
- Intervention rate
- How often a person has to correct an agent.
- Time to reviewed answer
- The time from a request to a checked result that a person can assess.
Work on problems that matter to the physical economy.
Help explore the connection between AI, enterprise knowledge and the decisions behind complex products.