Fifteen things we build, across three areas. Every one of them starts the same way, with the number it has to move.
You don't need to know which of these you want. Tell us the outcome, and we'll tell you which of these gets you there fastest, or that none of them do, if that's the honest answer.
Private, cost-efficient AI built for one job and deployed inside your own boundary. We don't wrap someone else's API and call it a product.
Models built and trained for the specific job you need done, on your own data, not a general-purpose model bent into shape. We start from the task and work backwards to the smallest thing that solves it.
You get The trained model, the training and evaluation pipeline, and a benchmark you can re-run yourself as your data changes.
Compact language models, fine-tuned for a narrow task and hosted by you. For well-defined work these beat renting a frontier model on cost, latency and control, and the weights stay yours.
You get A tuned model, the serving setup, and a cost-per-request figure you can compare against what you pay an API today.
Any model we build runs inside your boundary: your cloud account, your VPC, your on-premise hardware. This is how we build by default, not a paid upgrade, and it's what makes data-residency questions answerable.
You get Infrastructure as code, a deployment your team can audit, and a written data-flow description showing nothing leaves.
Models that run on the hardware itself: a machine, a sensor, a handheld, a vehicle. They keep working with no connectivity, and no data ever leaves the device to be processed.
You get A quantized model sized to your hardware, the on-device runtime, and measured latency and power figures on your actual target device.
You already run AI and it isn't paying for itself. Often the model isn't the problem. It's the pipeline feeding it, how it's hosted, or that nobody tied it to an outcome. We find which, and fix that.
You get An assessment of what's actually wrong, a costed plan, and the rebuild or replacement of the parts worth changing.
Extraction, classification and routing over the documents and data your business already runs on (invoices, contracts, forms, reports) with the pipeline that feeds it and the checks that keep it honest.
You get The pipeline, the model, an accuracy measurement against real samples, and a defined path for what happens when it isn't confident.
Built on WebGL and Three.js, with our own rendering core for the parts where a general-purpose library costs more than it gives. No plugins, no installs, and fast enough for a mid-range phone.
Large, multi-dimensional datasets rendered so people can actually explore them (pan, filter, drill in) at a frame rate that keeps the interaction feeling immediate rather than queued.
You get The visualization, the data layer feeding it, and measured frame rates at your real data volume rather than a demo subset.
Let customers configure a product and see the result live: materials, dimensions, components, pricing. Runs in the browser on the phone they're already holding, which is where most of them will use it.
You get The configurator, the rules engine behind the options, and hooks into your pricing or ordering system.
A live 3D model of a real thing (a machine, a line, a building) kept in sync with data coming off it. Worth building when seeing the current state in 3D genuinely changes a decision someone makes.
You get The 3D model, the real-time data binding, and the alerting or state logic that makes it useful rather than decorative.
Open, inspect and mark up engineering models in a browser, without a CAD license at every desk. Useful when the people who need to look at a model aren't the people who make them.
You get The viewer, the conversion pipeline from your source formats, and measurement or annotation tools if you need them.
You have 3D and it's too slow, too heavy, or too expensive to run. We work at the level most teams abstract away: geometry processing, projection, draw-call and memory budgets, and make it fast.
You get A profile of where the time actually goes, the optimizations applied, and before-and-after numbers on your target hardware.
On AWS, Azure or GCP. We'll recommend the cheap, boring option when it's the right one. A lot of cloud work is sold as transformation when a straight migration would have done the job.
Moving what you run today into the cloud, including a straightforward lift-and-shift when that genuinely fits your budget and timeline. Not every workload earns a rewrite on the way across.
You get A migration plan with a cost model, the migration itself, and a rollback path for each stage.
Re-architecting an application to use the cloud properly, when the current shape is what's holding you back. Worth doing when your costs scale with the wrong thing, or the architecture blocks something you need to ship.
You get The re-architected system, the infrastructure as code behind it, and the reasoning for each structural decision.
Reviewing what you're actually running and finding where the money goes. We treat spend as a design constraint, and we'll tell you when the savings aren't large enough to justify the work.
You get A breakdown of current spend, a ranked list of changes by saving against effort, and the changes you approve.
The layer underneath a private model: inference serving, GPU sizing, autoscaling and cost control. This is usually where self-hosted AI quietly gets expensive, so it's worth designing rather than inheriting.
You get The serving infrastructure, a scaling and cost model, and monitoring that reports cost per request rather than just uptime.
That's the normal starting position, and it's our job rather than yours. Tell us the outcome you want to move and we'll tell you which of the fifteen gets you there, or that none of them do.