Fyngard helps regulated and family-run enterprises use frontier AI, such as Claude, without losing control of their data or getting locked into one provider.
The vehicle stays. It lives inside your own environment.
Often the most capable. Good for lower-risk, customer-facing work where you want the best model available.
Try it: change the engine and nothing else on the left moves. Illustrative only. Swapping is designed, configured and tested for each client, and we don’t promise automatic switching by default.
The big question in enterprise AI isn’t whether it will transform your business. It’s who owns and controls it when it does. Most organisations rent their AI: important workflows are built around one provider’s model, reached over the internet. That works until something outside your control changes it, such as a commercial decision, a new regulation or a geopolitical event.
Intelligent systems designed to operate within your own governed environment, keeping your data, IP, business logic, workflows and governance under your control. We don’t build another AI model and we don’t sell another SaaS subscription. We design and build the governed workflows, agents and AI capability that sit above the models.
Think of the model as an engine and your enterprise AI as the vehicle. You shouldn’t have to rebuild the vehicle every time you change the engine. Your data, business logic, agents, workflows, governance and context stay in your architecture, and the model underneath is chosen according to policies you agree with us.
A customer-service conversation might use a frontier model such as Claude, while a highly sensitive workflow runs on an approved model inside your own private environment.
No. Sovereign AI does not mean abandoning frontier models. Keep using Claude, GPT or another proprietary model wherever it gives you the capability you want. The difference is that no single provider has to be the permanent foundation of your whole AI estate.
Use the best AI available today. Keep control of your AI capability tomorrow.
You and Fyngard agree the governance and routing policies together. We turn them into the business workflow and the architecture behind it, taking account of data sensitivity, regulation, performance, cost and availability. Select a kind of work to see an example rule.
Lower-risk conversation gets the best model available. The workflow still sets what the AI can access, what it may say and when it hands over to a person.
The model is one component of the workflow, not the workflow itself.
These are illustrations. Your real rules are agreed with you.
These terms describe how a model’s code, trained weights and training data are made available. For a business the practical questions are simple: can we run it ourselves, how much can we change it, and who ultimately controls it? You don’t have to choose one type for everything.
| Open-source AI | Open-weights AI | Closed / proprietary AI | |
|---|---|---|---|
| Model weights | Available | Available | Not available. Accessed via API or service |
| Code and training data | Broadly available, aiming for reproducibility | Trained model available; full recipe usually not | Kept by the provider |
| Customisation | Potentially very high | High, often fine-tunable | Mainly prompting, configuration and provider-supported tuning |
| Where it runs | Your environment or private cloud | Your environment or private cloud | Typically the provider’s environment |
| Examples | Pythia, OLMo, BLOOM | Llama, Mistral, Gemma | GPT, Claude, Gemini |
A practical picture for business readers, not a legal definition. Licence terms need checking model by model.
Research from Nous Research, benchmarking 19 models, found that open-weight models typically use 1.5 to 4 times more tokens than closed models to complete the same task, and up to 10 times more for simple knowledge questions. A lower price per token doesn’t always mean a lower bill.
Rather than pouring your whole knowledge base into every prompt, we pull in only the data a task needs, using techniques such as semantic search and context compression. You can choose an open-weight model where sovereignty matters most without quietly paying for it in runaway compute.
Your own engineering team could probably build the routing and model-switching layer. It’s increasingly standard technology, and your cloud environment or a standard gateway can provide it. The harder part is designing the governed business workflow around it.
It doesn’t know what “collections” means, what the escalation policy is, what a customer previously committed to, or when the AI should stop and hand over.
Because your workflows, prompts, data pipelines and business logic are decoupled from any one provider’s API, a model can in principle be swapped through configuration rather than a rebuild. Where an approved alternative has been configured and tested, whether another commercial model, an open-weight model in your private cloud or a regional model, work can be redirected to it.
Depending on the deployment, switching may be a manual governance decision, a pre-defined routing rule or automated failover. We do not promise automatic switching by default. It is designed, configured and tested to match each client’s requirements, so a change at a model provider becomes a manageable event, not a crisis.
It means knowing who controls your AI capability, where business logic and workflow memory live, what is allowed to reach an external model, how that is governed, and how quickly you can respond when regulations or providers change. We build these questions into the architecture from the outset.
We won’t tell you nothing will ever leave your environment. We help you decide what may, enforce it, and show it. For some material the answer is “nothing, ever”. For other material, a leading external model is a reasonable choice.
Across Asia and beyond, the same principle applies: keep the important capability under your control, set clear rules on which data reaches which models, and use the right intelligence for each task.
Customer service, document processing and collections. Clear rules on what the AI may do and when a person steps in, keeping a human in the loop.
In collections, AI can prioritise accounts, recall previous commitments, draft communications, hold multilingual conversations and record outcomes, as a governed workflow.
Conversations that move between English, Bahasa Melayu, Mandarin, Thai and more within a single interaction, run as a governed business process rather than a chatbot.
The same architecture supports collections, customer service and employee assistance.
Knowledge assistants, document intelligence, multilingual patient communication, translation and triage support.
Strict rules on which data can reach which models, and on when the AI must defer to a person.
A private knowledge assistant for the office’s own documents, and a quiet review of how AI is used today.
Discretion, trust and continuity come first.
Document intelligence, decision support and process automation, built around your organisation rather than around whichever model sits underneath.
Agentic customer and employee workflows, multilingual and voice-based AI, knowledge assistants, decision support and domain-specific solutions.
A focused proof of value typically takes around two weeks, sometimes quicker and sometimes longer. One real problem is built and tested to show whether it creates genuine business value before anything larger is considered. The aim is something accurate, useful and relevant to your business, not an impressive demo.
Especially suited to a family or private office. None requires a large commitment.
Answers questions from your office’s own documents (reports, deal papers, trust and governance records, correspondence) and points back to the document behind each answer. It runs inside your environment, and access can mirror who sees what today.
A no-blame look at how people in and around the office already use public AI tools, what goes into them and where the exposure lies. You get a short, plain-English summary and sensible guardrails.
One real task chosen with you, such as summarising board or investment packs, preparing meeting briefs, drafting routine correspondence or sorting incoming documents.
We would rather listen than pitch. If it would be useful to talk, we’d welcome an informal chat to hear how your organisation works and see whether there is common ground.