Our technology
Enterprise AI, built on
rigorous foundations.
Model-agnostic, sovereignty-first and auditable end to end — from data ingestion through to the interface a caseworker actually uses.
The AI stack
Four layers. Complete coverage.
Every layer is ours to operate or yours to own. Nothing in the stack assumes a connection to a service you do not control.
L4 · Application layer
What your staff and citizens actually touch. Interfaces, gateways and the audit trail that makes them defensible.
| ENG-1288 | Prompt · citizen intake | gov-intake-01 | ALLOW | 09:41:00 UTC |
| ENG-1291 | Retrieval · policy corpus | gov-policy-04 | ALLOW | 09:42:07 UTC |
| ENG-1305 | Prompt · benefits triage | gov-benefits-02 | REVIEW | 09:43:04 UTC |
| ENG-1312 | Response · redacted PII | gov-intake-01 | ALLOW | 09:44:01 UTC |
| ENG-1320 | Retrieval · case history | gov-casefile-07 | ALLOW | 09:45:08 UTC |
| ENG-1333 | Prompt · appeals draft | gov-appeals-03 | ALLOW | 09:46:05 UTC |
| ENG-1341 | Response · eligibility memo | gov-benefits-02 | ALLOW | 09:47:02 UTC |
API gateway
Versioned, rate-limited endpoints your existing systems can call.
Copilot interfaces
Task-shaped assistants embedded in the tools staff already use.
Audit logging
Every prompt, retrieval and response recorded and exportable.
Access control
Role and clearance-aware permissions enforced at the model boundary.
L3 · Inference layer
Where the model runs, under a latency SLA. Serving, scaling and retrieval engineered for production load, not demos.
Inference optimization
Quantisation, batching and caching to hold cost per query flat.
Autoscaling clusters
Capacity that follows demand across GPU and CPU pools.
RAG retrieval engine
Grounded answers with citations back to the source document.
<100ms latency SLA
Measured at p95, contractual, with penalties we are willing to sign.
L2 · Model layer
The model is tuned to your domain, not rented from someone else’s. We select, fine-tune and evaluate — and you keep the weights.
Foundation model selection
Open-weight and commercial models benchmarked against your own tasks.
Fine-tuning & RLHF
Supervised and preference tuning on your data, on your hardware.
Alignment & red-teaming
Adversarial testing against the failure modes your regulator cares about.
Model evaluation
Task-level scorecards with a pass mark agreed before we start.
L1 · Data layer
Your data never leaves your perimeter. We ingest, clean and index it inside your own environment, then hand you the keys.
Data ingestion pipelines
Batch and streaming connectors into your existing warehouses and record systems.
Preprocessing & cleaning
Deduplication, PII handling and normalisation before anything reaches a model.
Vector embeddings
Domain-tuned indexes that stay inside your classification boundary.
Secure data vaults
Encrypted at rest and in transit, with key custody you control.
Model development
From raw data to production. Five stages, each with an exit criterion. A model does not move to the next stage because the calendar says so.
01
Data preparation
Secure ingestion, cleaning, annotation and vectorization of domain data. Air-gapped where required.
02
Selection or pre-training
We select or pre-train a foundation model for your domain — Llama, Mistral, or one built from scratch.
03
Fine-tuning & RLHF
Supervised fine-tuning on your labelled data, then RLHF to align the model to your standards.
04
Evaluation & red-teaming
Benchmark testing, adversarial red-teaming, bias assessment and capability evaluation.
05
Deployment
Production release with monitoring, version control and a retraining pipeline. Cloud, on-premise or air-gapped.
Safety & alignment
AI a government can audit. A model that cannot be explained cannot be defended at a public inquiry, so explicability is a build requirement rather than a research interest.
Constitutional AI principles
Models are trained against explicit written guidelines, not only RLHF reward signals, so outputs are evaluable against a stated standard.
Human oversight loops
Every production deployment carries human-in-the-loop checkpoints for high-stakes decisions.
Bias testing & fairness audits
Systematic testing across demographic groups and edge cases before any government deployment.
Interpretability
We surface reasoning traces and citation links so a decision-maker can see why a model reached its answer.
Specifications
- <100ms
- Inference latency SLA
- 99.9%
- Uptime SLA
- SOC 2
- Type II certified
- ISO 27001
- Information security
- Air-gapped
- Deployment option
- Zero-trust
- Network architecture
Want the architecture in detail?
We share reference architectures and evaluation methodology under NDA ahead of an RFP response.