01
What objective should the model optimize?
Define cost, service, throughput, energy, capacity, inventory or other objectives and how trade-offs should be handled.
AI, automation and optimization
Mathematical Optimization
Translate complex business constraints into a formal decision model that can evaluate better courses of action.
Mathematical optimization is useful when a decision involves competing objectives, hard constraints and a large number of possible combinations. We support formulate the problem, connect it to governed data and build the technical environment required to solve it repeatedly.
The work can cover linear, mixed-integer and other optimization approaches, but the focus remains on the operational decision: what must be optimized, which constraints cannot be violated and how the recommendation will be evaluated by people responsible for the outcome.
NoèmeX perspectiveMake the system intelligible first: reveal the structure, constraints and variables that determine where change can produce the intended outcome.
Related leadership priorities
AI priorities depend on who owns the decision, risk and operating outcome.
Use these pathways to connect AI, automation and optimization work to the executives responsible for governance, foundations and business performance.
Questions we address
The decisions that shape the mathematical optimization work
We start with the business process, decision, information and operating controls around the use case before selecting models, platforms or automation technologies.
02
Which constraints are truly binding?
Capture physical, contractual, safety, maintenance, staffing, capacity and policy constraints in a form the model can use.
03
What data is required for each solve?
Establish the source, freshness, validation and ownership of every parameter entering the optimization model.
04
How should scenarios and recommendations be reviewed?
Design human review, sensitivity analysis and explanation around the proposed solution before action is taken.
What we deliver
A production-minded capability, not an isolated experiment
The exact scope depends on the organization and use case, but the work typically produces a combination of these outputs.
01
Problem formulation
Formal objectives, variables, constraints, assumptions and scenario definitions linked to the business decision.
02
Data and parameter model
Governed inputs, validation rules and interfaces for the parameters required by each optimization run.
03
Solver model and benchmark
Optimization model development, baseline comparison and performance testing.
04
Scenario interface
A user experience for changing assumptions, comparing solutions and understanding trade-offs.
05
Scalable execution environment
Compute and solver architecture that supports production-scale optimization workloads.
06
Model governance
Versioning, testing, approvals, documentation and ownership for optimization logic over time.
Selected work
Evidence and operating context from enterprise transformation work
The global production-optimization platform provides direct evidence of solver-based optimization operating at multinational scale.
Infrastructure and solver savings70%+Global optimization platform · Shared resources
Scaling solver-based optimization beyond local laptops
The client already had optimization algorithms, but local execution constrained compute power and encouraged redundant solver purchases. A centralized platform allowed algorithms to run on shared resources, integrated real-time industrial data and gave regions a governed way to deploy and reuse optimization logic, reducing solver and infrastructure costs by more than 70%.
Read the related caseRelated services
Continue into the adjacent capability.
AI, automation and optimization often intersect with data, analytics, process and governance work. These pages keep each capability focused while making those relationships explicit.
How we work
Use the engagement model that fits the problem.
Work can begin as focused advisory and continue into implementation, embedded support, managed operations or run-and-maintenance depending on the maturity and criticality of the capability.
Project delivery
Formulate, engineer, test and integrate optimization models with the required enterprise data and systems.
Read more →Embedded teams
Add optimization, data and platform specialists to internal engineering teams.
Read more →Managed services
Operate optimization platforms and model workflows when long-term specialist ownership is required.
Read more →Your Contact Persons
Connect directly with the people responsible for this work. Explore their experience and contact options.
Zahra Fathisalout
Founder · Eng., CSP, CMLAI
Corporate governance · ASC-C.Dir. candidate
Works across strategy, enterprise transformation, data, AI and governance, bringing operational engineering experience to complex transformation decisions.
View profile →
Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
View profile →
Mathematical Optimization
Start with the business problem, the workflow and the decision that needs to improve.
Share the objective, constraints, current planning method, data inputs and how often the decision must be made. Our teams can determine whether mathematical optimization is appropriate and how it should be operationalized.