Services
Physics AI consulting and implementation
Service 01
Physics AI Opportunity Assessment
Find where Physics AI can actually create leverage.
Determine where Physics AI is technically and economically justified. TecunTecs evaluates engineering workflows, simulation bottlenecks, available data, candidate model approaches, validation requirements, and deployment constraints before anyone commits to a large implementation.
- Engineering workflow review
- Repeated simulation bottlenecks
- Existing simulation and experimental data
- Geometry, meshes and design variables
- Accuracy and error tolerance requirements
- Candidate surrogate-model approaches
- Applicable NVIDIA PhysicsNeMo workflows
- Build versus buy considerations
Problem addressed
A team suspects Physics AI could help, but has no defensible way to decide which workflow to target, what accuracy is required, or whether the economics justify an implementation program.
Who it is for
CTOs, VPs of Engineering, R&D directors, simulation and CAE leads evaluating Physics ML adoption for the first time, or reassessing a stalled internal effort.
What TecunTecs does
TecunTecs reviews the engineering workflow end to end, inspects available simulation and experimental data, identifies the repeated or expensive computations, and maps candidate Physics ML approaches against required accuracy, latency, and deployment context.
Typical inputs
- Description of the engineering workflow and its bottlenecks
- Representative solver setups, geometry and mesh conventions
- Samples or an inventory of existing simulation / experimental data
- Accuracy, latency and deployment expectations
Typical outputs
- A written technical assessment of where Physics AI is and is not justified
- Candidate model approaches with trade-offs
- Data readiness and gap analysis
- A pilot roadmap with scope, effort and validation criteria
How success is evaluated
Success means a clear, evidence-based go / no-go decision. A well-argued recommendation not to pursue Physics AI for a given workflow is a valid and valuable outcome.
Logical next step
If the assessment identifies a justified use case, the natural next step is a scoped Physics ML pilot.
Service 02
PhysicsNeMo & Physics ML Pilots
Turn a promising use case into a working Physics AI prototype.
Build and benchmark a working surrogate-model prototype. TecunTecs prepares engineering data, adapts appropriate PhysicsNeMo or PyTorch architectures, trains models, compares against baselines, and determines whether the approach merits production investment.
- Simulation dataset pipelines
- Mesh and geometry preprocessing
- PhysicsNeMo workflows and PyTorch implementation
- Neural operators and graph neural networks
- MeshGraphNet, DoMINO, FNO, Transolver and related approaches
- Training, evaluation and baseline comparison
- Numerical error analysis and inference benchmarking
- Out-of-distribution testing
Problem addressed
A candidate use case exists, but nobody knows whether a surrogate model can reach the accuracy, generalization and speed the engineering workflow actually requires.
Who it is for
Simulation groups, computational engineering leads, and ML engineering teams that have domain data and a defined target but lack Physics ML implementation capacity.
What TecunTecs does
TecunTecs builds the smallest useful experiment: dataset pipelines from solver output, mesh and geometry preprocessing, an appropriate architecture implemented in PhysicsNeMo or PyTorch, training on GPUs, and quantitative comparison against a conventional baseline.
Typical inputs
- Simulation datasets, meshes and geometry
- Solver configuration and boundary conditions
- Target quantities of interest
- Baseline runtimes and required error tolerances
Typical outputs
- A trained prototype surrogate model and reproducible training pipeline
- Error analysis against held-out and out-of-distribution cases
- Inference benchmarks versus the conventional solver
- A recommendation on whether production investment is warranted
How success is evaluated
Success is measured numerically: error against held-out solver results, behaviour outside the training distribution, and inference latency in the context where the prediction is used.
Logical next step
If the prototype meets its criteria, the work moves to production deployment and integration.
Service 03
Production Physics AI
Move Physics AI from notebook to engineering workflow.
Turn validated models into engineering infrastructure. TecunTecs deploys GPU-backed inference, APIs, containers, cloud infrastructure, Kubernetes workloads, model pipelines, and the internal applications engineering teams operate day to day.
- Production model serving and APIs
- Docker containerization and CUDA/GPU deployment
- Cloud infrastructure and Kubernetes workloads
- Distributed and batched inference
- Model and data pipelines
- Observability and deployment automation
- Internal engineering applications
- Integration with existing engineering systems
Problem addressed
A validated model sits in a notebook. Engineers cannot use it, it is not reproducible, and there is no infrastructure to run inference reliably.
Who it is for
Engineering organizations and software teams that need an operable system: platform leads, cloud architects, AI engineering leads, and heads of engineering software.
What TecunTecs does
TecunTecs containerizes the model, builds GPU-backed inference services and APIs, sets up cloud and Kubernetes infrastructure, automates the training and data pipelines, adds observability, and integrates the service with the tools engineers already use.
Typical inputs
- A validated model and its training pipeline
- Expected request volume, latency and batch patterns
- Cloud or on-premise environment and security constraints
- Integration points in the existing engineering stack
Typical outputs
- Containerized, GPU-backed inference service with an API
- Cloud / Kubernetes deployment and automation
- Retraining and data pipelines
- Monitoring, logging and operational documentation
How success is evaluated
Success means engineers use the system in their normal workflow, results are reproducible, and the service is observable and maintainable by the client's own team.
Logical next step
Once inference is operable, the interface layer usually determines how much value the model actually delivers.
Service 04
AI-Native Engineering Software
Build modern interfaces around trusted engineering tools.
Combine scientific software, interactive visualization, Claude, MCP, and bespoke full-stack applications to make advanced engineering workflows easier to use — without asking a language model to invent the physics.
- Bespoke engineering web applications
- Engineering copilots and Claude integrations
- MCP servers around trusted engineering tools
- Natural-language interfaces for existing solvers
- Interactive 2D and 3D scientific visualization
- Engineering dashboards and automated reports
- Human-in-the-loop workflows
- Frontends for Physics AI models
Problem addressed
Powerful engineering capability is locked behind scripts, command lines and specialist knowledge, so few people in the organization can use it.
Who it is for
Scientific and engineering software companies, internal tooling teams, digital engineering leads, and technical founders building engineering products.
What TecunTecs does
TecunTecs builds the application layer: bespoke engineering web apps, interactive 2D/3D scientific visualization, dashboards, automated reports, and — where genuinely useful — Claude and MCP interfaces that orchestrate validated tools rather than replace them.
Typical inputs
- Available APIs, solvers and validated models
- The workflows and users the software must serve
- Data formats, result files and visualization needs
- Branding, security and deployment requirements
Typical outputs
- A production web or internal application
- Interactive scientific visualization of fields, meshes and results
- Optional LLM / MCP interfaces around deterministic tools
- Documentation and handover to the client's team
How success is evaluated
Success means domain experts complete real work faster, with outputs that remain traceable to the underlying numerical models.
Logical next step
Most engagements start with a short scoping conversation about the workflow you want to improve.
Not sure what you need yet?
That is a valid starting point.
TecunTecs can begin by evaluating the engineering workflow and identifying where Physics AI is worth pursuing — including concluding that it is not.