Model Engineering
Build the right-sized model. Run it where it belongs.
We build, train, and fine-tune language models across the full size spectrum, from small domain models to large, and select the one that fits your problem. Then we deploy it on the infrastructure that fits your constraints: on-premise, in the cloud, or hybrid, driven by data sovereignty, latency, control, and cost.
Right-sized models
Right-sizing the model is a critical decision
Model size shapes cost, latency, accuracy, and how much control you retain, so getting it right is critical. Rather than default to the largest available model, we evaluate the full spectrum and the technique that fits, from retrieval and fine-tuning to distillation and, where it is genuinely warranted, training from scratch. We then recommend the model matched to your problem and your constraints.
Small (SLM)
Fine-tuned small models, often behind a retrieval pipeline. Cheaper to run, fast enough for the edge, and more accurate on your domain than a generic frontier API. The right default for most enterprise workloads.
Medium (MLM)
Medium-sized models for tasks where a small model cannot carry the reasoning but a frontier model is more than the task requires. The balance point of capability, cost, and control.
Large (LLM)
Large and frontier models where the task genuinely demands broad reasoning or world knowledge, deployed with cost and latency engineered in rather than assumed.
How we build
From your data to a model you own, in five disciplined stages
Building a model is an engineering lifecycle, not a single training run. Every model we deliver passes through the same five stages, each with a gate you can inspect, so what reaches production is measurable, reproducible, and yours.
Reference architecture
The components we deploy for a production language model, numbered by the stage that governs them. The same architecture runs on-premise, in the cloud, or hybrid.
Data curation and rights
We assemble, clean, label, and de-duplicate the training corpus, and confirm you hold the rights to use it. Provenance is recorded for every source, so the model can withstand later scrutiny.
Adaptation method
We choose the lightest technique that meets the target: retrieval over a base model, supervised fine-tuning, parameter-efficient methods such as LoRA and QLoRA, preference tuning, or distillation from a larger model. Training from scratch is reserved for the rare case that warrants it.
Evaluation gates
Before anything ships, the model is measured against a domain golden set and a regression suite: task accuracy, hallucination rate, safety behaviour, latency, and cost per request. A model that fails a gate does not advance.
Release engineering
The passing model is versioned in a registry with its data lineage, evaluation results, and quantized serving artifacts, then deployed through the same CI/CD discipline as any other production system.
Monitoring and retraining
In production we track drift, quality, and cost, and retrain on a schedule or trigger you approve. The model improves under control rather than degrading unnoticed.
Where it runs
Deploy where data sovereignty, latency, and cost decide
We deploy AI where it belongs, driven by your constraints, not vendor preference.
On-premise
Your hardware, your data, your control. The right choice for sovereign, regulated, or sensitive workloads where data cannot leave your environment.
Cloud
Elastic scale and managed infrastructure when burst capacity and speed-to-deploy matter more than physical control, with cost-per-inference engineered to stay predictable.
Hybrid
The emerging default: sensitive workloads on-premise, elastic workloads in the cloud, one coherent system. Control without giving up scale.
Infrastructure and delivery
The infrastructure to deliver AI inside the enterprise
A model is only as valuable as the platform that runs it. We design, build, and operate the infrastructure beneath the model, sized to your throughput and your security posture, so production AI is delivered on your terms rather than a vendor's.
Sovereign and air-gapped deployment
For classified, regulated, or sovereign workloads we deploy fully disconnected from the public internet, with model weights, data, and inference kept inside your perimeter.
Canadian data residency
Data, models, and inference stay in Canada when the mandate requires it, on your premises or in Canadian cloud regions, with residency documented for your auditors.
GPU infrastructure design
We size and specify on-premise GPU clusters and cloud accelerator pools for the actual workload: training against inference, batch against real time, and the quantization that keeps cost per request predictable.
CI/CD for models
Models ship with the same pipeline discipline as software: versioned artifacts, automated evaluation gates, staged rollout, and rollback in minutes rather than days.
Observability
Every request is traced from prompt to response, with quality, latency, cost, and drift dashboards that your operations team owns from day one.
Production support
After launch we operate alongside your team or hand over fully, with runbooks, monitoring, and a retraining cadence agreed in advance.
Work with us
The right model, in the right place.
We match the model to the problem and the deployment to your constraints: on-premise, cloud, or hybrid.
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