Enterprise AI
AI inside your boundary, under your control.
We design AI systems that respect where your data may go, who may use what, and which actions need a person — then build and run them.
Deployment options
Choose where intelligence runs.
Each option has a real trade-off. We help you choose per use case, not once for everything.
| Option | When it fits | Trade-off |
|---|---|---|
| Hosted models behind a gateway | Fast start and broad model choice. | Data leaves your environment under provider terms — governed by policy, redaction and audit at the gateway. |
| Private cloud deployment | Data must stay in your own cloud account. | More setup and operating cost; fewer model choices. |
| Self-hosted models | The strictest data control or offline requirements. | Highest operating effort; capacity and model quality are yours to manage. |
Governance & guardrails
Control that’s designed in, not bolted on.
Governance
- Access policies by team, role and use case.
- Audit logging of every model call.
- Spend controls and usage reporting.
- Versioning of models and prompts.
Guardrails
- Prompt-injection defenses — user content is never treated as instructions.
- Output validation before results are used.
- Redaction wherever sensitive data is involved.
- Human approval boundaries for consequential agent actions.
We don’t claim certifications or compliance on your behalf. We design to the requirements you’re accountable for, and document how the system meets them.
Responsible AI, in practice
Practices you can inspect, not principles on a poster.
The same seven practices run in our production systems today. Each one leaves something you can ask to see.
| Practice | What it means for you | What you can inspect |
|---|---|---|
| Decide where AI belongs | Some steps stay deterministic or run by people. AI goes where it earns its place. | Decision record |
| Human approval for consequential actions | Agents propose; people approve changes to records, money and communications. | Approval log |
| Least-privilege tools | Agents act only through scoped, allow-listed tools. | Tool permission list |
| Data boundaries | Personal data is minimised. Private or self-hosted models when data can’t leave. No training on your data without your consent. | Data-flow diagram |
| Evaluate before release | Accuracy, failure modes and prompt-injection tests — and bias checks where decisions affect people. | Evaluation report |
| Tell people when AI is involved | AI-generated output is labelled for the people who use it. | The product itself |
| Monitor and roll back | Quality and drift monitoring, an audit trail and a tested way back. | Runbook |
Integration & modernization
Change the system without stopping the business.
01
Assess
Today’s architecture, risks, data flows and what must keep running.
02
Plan
An incremental path with identity, integration and data decisions written down.
03
Replace
New services behind existing interfaces — no big-bang cutover — then retire what’s no longer needed.
Bring us the hard constraints.
Data residency, legacy systems, approval rules — tell us what you’re working within and we’ll design inside it.