Use AI with confidential client files, private company plans and proprietary knowledge. We build assistants around your access rules and processing requirements, with local deployment when the work needs to stay on your own infrastructure.
Find my starting pointBased in Kuching, Malaysia.
On-site locally. Remote delivery worldwide.
Infrastructure you control
Company knowledge.
Useful answers.
External tools & cloud servicesOnly the connections you approve
One deployment option. We agree the data flow and test the boundary before launch.
What we could build
Some work cannot go into an unapproved AI service. Start with the files your client agreements, internal policy or commercial interests require you to control.
Law firms, accountants & advisers
“What changed in this client’s file?”
Compare private correspondence, agreements and meeting notes. Prepare a brief of changes and missing information, with links to the source. Keep each client’s files restricted to their assigned team.
Owners & leadership teams
“What changed in the plan—and what needs a decision?”
Bring together unpublished forecasts, board papers and contract negotiations. Prepare a briefing with supporting figures and source documents, available only to the authorised leadership team.
Engineering, software & R&D teams
“Why did we change this design, and what failed before?”
Search unreleased designs, internal code and test records. Recover the decisions behind the current version, with source references and access confined to the approved project team.
Where the model runs
A local LLM runs on your own machine or server. Private AI is broader: the model, documents, access, logs and connected tools all matter.
For work that needs on-site processing. We check hardware, capacity and any offline requirement against the actual task.
For teams that need remote access. Agree the hosting region, provider access, retention and responsibilities.
Keep selected work local and connect approved external tools where useful. Document exactly what crosses the boundary.
What we deliver
Choose one confidential task. Agree where its data may be processed, who may access it and what must stay local. Test model fit and check hardware, licence and operating costs.
Connect approved records and tools. Test useful answers, source references and draft actions. Check that restricted documents, retrieved passages and answers stay limited to authorised users.
Agree ownership, updates, backups, recovery and support. Leave operating notes and an acceptance record for the workflow you approved.
Work behind the approach
Scoped memory, source references and a distinction between recalled context and current truth.
Read the build fileInternal system · Company operationsCompany knowledge, agent roles, human approvals and an audit trail in one operating platform.
Read the build fileThese build files show our work with knowledge and controls. They do not establish a fully offline LLM deployment; that is tested for the system we agree with you.
Before you choose
It can be designed for offline use if the model, document processing, retrieval and other required components all run locally. We test the complete workflow with external connections disabled before making that claim. Remote access and cloud tools need separate decisions.
Yes, within an agreed scope. Retrieval-augmented generation (RAG) finds relevant passages and supplies them to the model with the question. We design source references and document permissions into the workflow. This is different from training or fine-tuning a model, and answers still need review.
Yes, within a defined workflow. We can compare document versions, prepare confidential briefs and create draft tasks from approved records. The application must enforce access and approval rules. Your team reviews important findings and approves consequential actions.
Yes, if we build and test those access rules. Hosting a model locally does not create permissions by itself. Retrieval, answers, conversation history and connected tools must respect the user’s role and client or project access. We test with authorised and unauthorised accounts, and agree administrator and support access separately.
It depends on the model, document size, response-time target and number of people using it at once. We test the task on suitable hardware before recommending a purchase. An existing machine may be enough for a small pilot.
Not automatically. Local inference is one part of the data path. We agree what may leave the environment and test that boundary for the deployed configuration. This includes files, model requests, document processing, search indexes, logs, backups and any external or remote support access.
No. Some business AI services offer contractual privacy protections and do not train on business data by default. Local deployment is worth evaluating when your requirements limit external processing or require operation on your own infrastructure. We compare provider terms, access, retention, model quality and operating costs before recommending an approach.
We agree an owner for model and software updates, document refreshes, access changes, monitoring, backups and recovery. Ongoing support has a defined scope. Self-hosting does not remove operating costs or maintenance.
Start with one task
Describe the task and what makes the information sensitive. No confidential files are needed for the free initial scope review.
Find my starting point