Custom Software vs Off-the-Shelf: A Business Decision Guide
When buying software saves money and when it quietly costs more than building it.
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Engineering & Simulation · Fluid dynamics solvers built for your physics
CFD solvers engineered for your physics — not a vendor's roadmap.
Bespoke computational fluid dynamics solvers — finite-volume and finite-element codes tuned to your geometry, physics models, and HPC budget.
Delivered from our Chennai and Bengaluru studios — to clients across India and overseas.
// The problem
Your simulation licence costs more than the cluster running it.
Commercial CFD packages are remarkable generalists, and that generality is billed per core. Teams routinely discover that running the larger simulation they actually need is blocked not by hardware but by licensing arithmetic.
The second constraint is extensibility. When your application sits outside the mainstream — reacting flows, non-Newtonian rheology, cavitation, tightly coupled fluid-structure interaction — you need physics the package does not implement and you cannot add.
Does any of this sound familiar?
Per-core licensing capping simulation size
Hardware sitting idle because the licence, not the cluster, is the limit.
Physics you cannot implement
Models outside the vendor's scope, with no route to add them.
A solver you cannot inspect
Results you must trust without visibility into how they were produced.
Cannot ship simulation inside your product
Commercial licences that prohibit embedding in software you sell.
What it costs to leave this alone
Per-core licensing turns every increase in simulation fidelity into a recurring cost increase. A solver you own converts that into a one-time engineering investment that scales with hardware instead.
// How we fix it
Our approach to Custom CFD Solver Development
We build solvers from the discretisation up — finite-volume, finite-element or spectral, chosen for your governing equations — with turbulence closures and custom physics implemented to your specification and MPI/CUDA parallelisation that scales on your actual hardware.
Verification and validation are contractual deliverables, not claims: method of manufactured solutions to confirm observed order of accuracy, then validation against analytical cases, published benchmarks and your own experimental data, with documented error metrics.
Talk to us about your project// What changes
The return, in plain terms
What you should expect to be different once this is in place.
0
Per-core licence cost
Scale simulations with hardware rather than with licensing spend.
V&V
Documented
Verified order of accuracy and validation against benchmarks and your own data.
Yours
To extend and embed
Source you own, with physics you can add and rights to ship it in your product.
// What's included
Everything in Custom CFD Solver Development
Discretisation built for your equations
Finite-volume, finite-element, or spectral schemes selected for your governing equations, mesh topology, and accuracy requirements — not forced into a general-purpose mould.
Turbulence & physics models
RANS, LES, and DES closures, plus custom models for multiphase, reacting, compressible, and non-Newtonian flows implemented and validated to your specification.
Parallel & GPU acceleration
MPI domain decomposition and CUDA/OpenMP kernels that scale from a workstation to a cluster — with strong and weak scaling measured, not assumed.
Validated against benchmarks
Verified with method of manufactured solutions and validated against analytical cases, published benchmarks, and your own experimental data before release.
Meshing & pre/post-processing
Mesh import from standard formats, quality diagnostics, and output written for ParaView, Tecplot, or a custom visualisation front end.
Coupled multiphysics
Fluid-structure interaction, conjugate heat transfer, and co-simulation with structural or thermal codes where your problem demands it.
// Who this is for
Is this you?
R&D teams constrained by licensing
Where simulation scale is limited by cost rather than capability.
Groups needing non-standard physics
Reacting, multiphase, non-Newtonian or coupled problems.
Product companies embedding simulation
Who need to ship solver capability inside their own software.
// Problems we've solved
From challenge to result
Representative engagements, described in terms of what changed for the business.
The challenge
A team could not run simulations at the required resolution because per-core licensing made large parallel runs uneconomic.
What we did
Benchmarked the workload, then built a purpose-designed solver with MPI decomposition and GPU kernels validated against their experimental data.
The result
Simulation scale became a function of available hardware rather than licence budget.
The challenge
A required physics model was unsupported by the commercial package in use.
What we did
Implemented the model within a custom solver, verified with manufactured solutions and validated against published benchmarks.
The result
The team could simulate the phenomenon central to their work, with documented accuracy bounds.
// Technology
The stack we reach for
Chosen for durability and fit — and handed over as documented code you own outright.
C++
Fortran
CUDA
MPI
OpenMP
PETSc
OpenFOAM
Python
ParaView
// Questions
Custom CFD Solver Development — FAQs
Three reasons dominate: licence economics (per-core costs make large parallel runs prohibitive), extensibility (you can implement physics the commercial model does not support), and embedding (a custom solver can ship inside your own product, which a commercial licence cannot). If a commercial package genuinely fits, we will tell you so.
Verification and validation are contractual deliverables, not afterthoughts. We verify the discretisation with the method of manufactured solutions to confirm the observed order of accuracy, then validate against analytical solutions, published benchmarks, and your experimental data with documented error metrics.
Very often that is the right call, and it is cheaper. Custom OpenFOAM solvers, boundary conditions, and physics libraries deliver most of the benefit at a fraction of the cost. We recommend a from-scratch kernel only when the underlying framework genuinely blocks your requirements.
Yes — we target your actual environment, whether that is a single multi-core workstation, an on-premise HPC cluster with a job scheduler, or cloud instances. Scaling behaviour is profiled and reported on your hardware, not ours.
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Other things we engineer
Most clients start with one service and grow into others. One team owns the whole stack.