MODEL ENGINEERING
The Right Model for Your Constraints. Not Just the Biggest One Available.
Model selection, distillation, and quantisation across frontier and open-weight families, tuned to your latency, cost, and deployment environment. Including on-premises and air-gapped. Built with the FDE framework.
Schedule a ConsultationDefaulting to the Biggest Model Is an Architecture Decision You'll Regret
Frontier models are powerful but expensive at scale, dependent on external APIs, and off the table for data residency requirements. Regulated industries need latency budgets, cost per call at millions of requests, and full auditability.
HOW WE DO IT
Selection, Distillation, Quantisation. All Three When Needed.
Model Selection
Claude, GPT-4o, Gemini, Llama, Mistral, Phi, Qwen: evaluated on your task, not benchmarks. The right model for the job.
Fine-Tuning & Distillation
Transfer frontier model capabilities into smaller, faster, cheaper models purpose-built for your domain.
Quantisation
GGUF, AWQ, GPTQ: precision reduction for deployment on constrained hardware without unacceptable quality loss.
On-Premises Deployment
Full model serving stack on your infrastructure. No data leaves your network, no external API dependency.
Air-Gapped Deployment
For environments with zero external connectivity. Models packaged, verified, and deployed entirely offline.
Infrastructure-as-Code
Terraform-managed deployment. Reproducible, auditable, version-controlled infrastructure from day one.
Security Posture Integration
RBAC, SOC 2 compliance, row-level security designed in from the architecture phase (not bolted on after).
WHERE IT PAYS OFF
When the Default Won't Do
Regulated Industries
Data residency requirements that prohibit cloud APIs. Models must run within jurisdictional boundaries.
Government & Defence
Air-gapped environments with zero external connectivity. Full model stack deployed and served offline.
High-Volume SaaS
Cost optimisation at scale. Millions of inference calls where frontier model pricing becomes untenable.
Enterprise
On-premises deployment for IP-sensitive workloads where proprietary data cannot leave the network.
IN PRODUCTION
In Production: Perlucem & Sectona
Perlucem
POC to production on AWS with Terraform IaC. SOC 2 compliance as a design constraint, not an afterthought.
Stack: React, Tailwind, RDS, row-level security + RBAC.
SOC 2-compliant production deployment.
Sectona
Private secured SharePoint integration on Azure AI Foundry. No data leaves the client environment at any point in the pipeline.
Environment: Azure AI Foundry, private SharePoint, zero-egress architecture.
Full data sovereignty maintained throughout.
What Are Your Actual Deployment Constraints?
Latency budget, cost per call, data residency, air-gap requirement — bring us the constraints and we'll tell you exactly which model architecture solves for all of them.
Schedule a Consultation