Proof before code
We formalize the problem and prove the approach before writing production code. Surprises happen on the whiteboard, not in your incident channel.
Hard math,
working software.
Nusear is a consulting firm for problems that resist ordinary engineering: large and complex data modeling, algorithmic design, data architecture, application design, and applied mathematical solutions. We do the mathematics — then we ship the code.
- Modelswith invariants, not folklore
- Proofsbefore production code
- Codeyou can maintain after we leave
Maths in. Software out.One senior team, both crafts
Proofs where they matter. Demos where they help.
Capabilities, not slogans
OptimizationGraph algorithmsNumerical methodsStochastic modelingSimulationData systemsType-driven designFormal specification
A firm built on the math.
Nusear pairs applied mathematicians with senior software engineers — often in the same person. We take problems that resist ordinary engineering, formalize them properly, and deliver working systems with the derivation attached.
- Mathematical modeling as an engineering discipline
- Production systems, not notebooks and slideware
- Small senior teams, direct access
The Nusear Approach
Rigor you can ship.
Most engagements fail in the gap between analysis and code. We close that gap by staffing both sides of it with the same people — and by proving the approach before building it.
Speak with a SpecialistMath and engineering together
The people who derive the model are the people who build the system. Nothing is lost in translation because there is no translation.
Senior people only
No handoffs and no juniors learning on your budget. The person you brief is the person who does the work.
Documentation you keep
Specifications, derivations, design records, and runbooks stay with you. Our goal is a team that no longer needs us.
How Engagements Work
Three steps to an answer you can defend.
- 01
Formalize the problem
We turn a vague pain into a precise specification: entities, constraints, invariants, data shapes, and success criteria everyone can sign.
- 02
Model and prove
We design the model or algorithm, analyze complexity and error, and validate it against your real data and your worst edge cases.
- 03
Build and hand over
We implement the production system, measure it honestly, document the design, and hand your team the keys.
Practice Areas
Ways We Can Help
Data Modeling
Large, complex domains, modeled precisely.
Conceptual, logical, and physical models for data that outgrew its spreadsheets and ad-hoc schemas: rich invariants, evolving meaning, and structure too interconnected for guesswork.
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Algorithmic Design
Algorithms that survive contact with real data.
Custom algorithms for optimization, search, matching, scheduling, and inference — designed with complexity analysis, correctness arguments, and benchmarks before they reach production.
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Data Architecture
Data systems you can reason about.
Storage layouts, pipelines, and distributed architectures chosen by invariants and access patterns, not fashion. We design for correctness first, then for scale.
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Application Design
Applications shaped by the model, not the framework.
Domain-driven application design where the mathematics lives in the core and the framework stays at the edges. APIs, workflows, and interfaces your engineers can maintain.
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Applied Mathematics
Mathematics as a service, not a slogan.
Modeling, analysis, simulation, and numerical methods for operational problems: pricing, risk, logistics, forecasting, and systems that need a theorem, not a trend.
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Client Notes
What people say about the work.
Draft quotations from the kinds of teams we work with. Replace with real client voices during content review.
Speak with a Specialist
VP Engineering, national logistics platform
Nusear replaced a 40-minute batch job with an algorithm that finishes in 90 seconds. They showed us the proof before they showed us the code.
Head of Data, healthcare analytics firm
They modeled a domain we had been mis-modeling for six years. The migration plan alone paid for the engagement.
CTO, industrial manufacturer
The first consultants who ever told us not to build something. Their simulation said the idea would fail. It would have.
Director of Platform, financial services
The architecture review found two correctness bugs that money was quietly leaking through. Both are now invariants enforced by the database.
Founder, ML startup
They took our notebook prototype to a designed application with a tested mathematical core. Our engineers still work from their specification.
Chief Actuary, insurance group
Our model is finally written down as mathematics instead of spreadsheet folklore. The auditors noticed immediately.
