Custom applications
Run selected AI workloads on infrastructure your organisation controls, on site or in your private cloud account, with agreed data flows, permissions and operating responsibilities.
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The approach
Start with a business task and decide where each part of the system should run. We assess the model, the supporting components and your access requirements together, then agree how to evaluate the result and who will operate it.
A sample or redacted workflow, where the data lives, the people who use it, expected workload and existing infrastructure. The assessment establishes a feasible model and hardware plan, access boundaries, evaluation criteria and operating ownership before a deployment is proposed.
On premises means running the agreed components on hardware at your organisation’s site. A customer-controlled private cloud uses infrastructure in an account you control, with agreed network, identity, region and provider arrangements. The choice affects hardware access, connectivity, support and day-to-day ownership.
Map the complete data flow: who can submit a request, which documents each person can retrieve, what is logged, where backups go and what administrators or support staff can access. Any external connector or hosted component needs its own agreed boundary. Deployment location alone does not establish privacy or security.
An internal document assistant can retrieve authorised material, show the sources used and help staff prepare an answer. Source permissions need to carry through retrieval, and the workflow should handle missing information and uncertain answers with review or escalation.
For customer support, a first version can draft a reply, suggest a category or route a request to the right person. Agree which actions require approval and how a person takes over. Text, images, audio and document inputs depend on the selected model and any OCR, speech or other processing components; each supported input path needs testing.
Bring a sample or redacted task, the data types and document sizes, expected request volume, peak concurrent users and a useful response-time target. We assess model memory, GPU capacity, storage, networking and the supporting software against that workload. Existing hardware compatibility is established through assessment and testing.
Kimi K3 and specific GLM releases are examples to assess only when the exact model’s current licence, commercial-use terms, supported inputs, hardware and tested quality fit. Open weights do not by themselves establish an open-source licence or unrestricted commercial use. Kimi K3’s published serving recipe requires substantial GPU infrastructure, with multi-node configurations for production traffic.
Evaluate representative tasks before rollout: answer quality, source accuracy, language coverage, latency, concurrency, failure handling and the review effort your team needs. Keep acceptance examples separate from configuration examples and agree when the system should hand work to a person.
Official model and serving references:
Self-hosted components can reduce dependence on a third-party inference API’s per-token prices and policies. Connected hosted services can still carry outside dependencies, charges and terms. Compare the full cost of hardware or cloud rental, licences, power and networking, storage, backups, patching, monitoring, staff and ongoing evaluation; savings are not automatic.
Agree who manages access, monitors usage and failures, tests model updates, applies patches, restores backups and handles incidents. Define retention and support access, review the system as documents and workloads change, and budget for those operating responsibilities.
Possible applications
Connected systems
Feasibility, access and provider requirements are reviewed during discovery.
Related capabilities
Go deeper
That requires validating the complete stack and its network behaviour. Check the model server, embeddings, OCR, speech, telemetry, updates, external connectors, backups and support access. An offline setting in one library is not evidence that the whole system makes no external calls. Agree the isolation requirement and verify every component before making that claim.
Sizing depends on the exact model, input lengths, precision, concurrency and response-time targets, plus the other components in the workflow. A smaller task may suit a different model from a large general-purpose deployment. We assess the existing infrastructure before proposing hardware; there is no blanket promise of ordinary-server compatibility.
Quality varies with the task, language, source material, model and configuration. Use representative evaluations and human review to decide whether a candidate is useful for your workflow. Private deployment does not guarantee equal quality to every hosted model.
It gives you choices over infrastructure and access. Compliance and security still depend on your policies, data handling, configuration, operating practices and applicable requirements. A deployment is not an automatic certification or a promise that data never leaves the agreed boundary.
Describe the task, where the data lives, the intended users and your existing infrastructure. A sample or redacted workflow can help scope an assessment. Keep credentials and confidential records out of the initial brief; agree a suitable way to review material with the team.
Have a workflow in mind?
Tell us what your team does today and what you want to improve.
Describe the workflow, your current tools and the result you need.
