Questions people actually ask

No. Systems can be designed to operate entirely within your environment when that is what your requirements call for. Other customers choose managed private processing or a hybrid architecture where some workloads stay inside and others do not. The right model depends on what the data is and what your policies already say about it.

No. We intentionally avoid unnecessary dependence on a specific model or provider. Wherever it is practical, processing components are interchangeable — the transcription engine, the OCR engine, the embedding model and the language model are all stages with defined inputs and outputs rather than permanent commitments.

Not necessarily. Hardware requirements depend heavily on the workload and on how quickly results are needed. Many batch-processing workloads run perfectly well on modest systems, because a job that has all night to finish does not need to finish in seconds. Our workload planner will give you a rough hardware class in about a minute.

Generally, no. We can specify appropriate hardware, but we prefer customers to purchase equipment directly from whichever vendor they choose. Our job is to make the system work, not to make money by selling a larger server — and staying out of that business is what makes the sizing recommendation trustworthy.

Often, yes. Existing servers, workstations, GPUs, virtualization environments and storage may all be usable depending on the workload. We evaluate what you already have before recommending that you buy anything, and a surprising number of first projects run entirely on equipment that sits idle overnight.

No. Cloud infrastructure and commercial AI services can be extremely useful. We simply believe their use should be intentional. Some workloads belong locally, some belong in the cloud, and many organizations benefit from both. What we object to is the cloud being the default for everything because nobody ever decided otherwise.

Yes. An organization may allow one category of information to use cloud AI while requiring another category to remain entirely on-premises. Systems can be designed around those boundaries so that the rule is enforced by the architecture rather than by everyone remembering it.

We can build conversational interfaces and internal AI assistants where they make sense. But we do not believe every AI project needs to become a chatbot. Many of the most useful systems we build operate automatically in the background and nobody ever types a prompt into them.

Yes. Document processing is one of the primary uses for this technology. Depending on the collection we combine text extraction, OCR, metadata extraction, indexing, embeddings, summarization and search — and the original document always remains the source of truth.

Yes. Speech-to-text processing can operate on local or controlled infrastructure without automatically sending recordings to a public AI provider. For most meeting workloads the transcript is not needed for hours, which makes this considerably cheaper than people expect.

Yes, but we prefer to combine AI with established security tools rather than replacing them. Dedicated scanners perform the specialized testing they are good at. AI helps correlate, explain, prioritize and report on their findings, with the underlying evidence preserved so a human can check the reasoning.

Ideally we evaluate it and replace a component. That is one of the main reasons we build pipelines around normalized data instead of coupling an application tightly to a single AI provider. The surrounding workflow should not care which engine did the work, as long as the output has the same shape.

Yes. Projects can include ongoing management, software and model updates, security patching, monitoring, job and storage management, performance tuning, workflow improvements and integration support. Some customers want one implementation; others want the system looked after indefinitely.

With a conversation about the work rather than the technology. You do not need to know which model you need, which GPU you need, or what your AI strategy is. Tell us what you are trying to accomplish and what your data rules are, and we will work backward to what makes sense.