Answers to common technology questions.

Inside your environment. That is either your own cloud account, your on-premise hardware, or a dedicated single-tenant Stellar Cascade region in India. Nothing is sent to a public model API unless you explicitly ask for that and sign off on it.

Yes. We integrate at whatever layer your vendor exposes — HL7, FHIR, direct database reads, or file drops if that is all there is. We have done all four. The integration approach is settled during discovery, not assumed.

Answers are retrieved from your own documents and always carry the citation. If the retrieval finds nothing relevant, the system says so rather than generating something. Clinical-risk use cases additionally go through a human review step that your team defines.

We build to data-residency, consent and retention rules from the start, and hand over the documentation your accreditation and audit processes need. We are not a law firm — your compliance team signs off, and our job is to make that sign-off straightforward.

A working pilot on one ward or one department typically runs four to six weeks after the architecture is signed off. Discovery is two weeks before that.

Infrastructure is sized to your actual usage and quoted as a monthly figure before the build starts. There is no per-seat AI licence, so adding staff does not change the bill.

It depends on your volume and how much variance you can tolerate. Below a certain steady-state throughput, an API is cheaper and simpler. Above it, dedicated GPUs win — often substantially. We run the numbers on your actual traffic during discovery and show you the crossover point rather than pushing a preference.

Yes, that is the default. Our engineers work in your repository, your branching model and your review process. You own everything we write.

Data boundaries are designed before anything is built: per-tenant retrieval scopes, isolated storage, and controls that prevent one tenant's content reaching another's context window. This is the part enterprise buyers probe hardest, so it gets documented properly.

We provide the architecture documentation, data-flow diagrams and control descriptions that those questionnaires ask for. Your team still owns the response, but they are not writing it from scratch.

Good — that shortens discovery. We assess what is there, tell you what carries over to production and what needs rebuilding, and price from that.

For retrieval-based systems, yes — the evidence the model saw is logged alongside the answer, so a reviewer can reconstruct the decision. For scoring models, we favour approaches that carry feature-level attribution. Where a use case genuinely cannot be explained, we say so and recommend keeping a human decision-maker.

Not unless you choose that. Deployments run in your own cloud account in an Indian region, on your own hardware, or in a dedicated in-country Stellar Cascade tenancy.

It varies by document type and scan quality, which is why we benchmark on your actual documents during discovery rather than quoting a headline number. Low-confidence extractions are routed to a human instead of being guessed.

We build the logging, versioning and approval controls that audits ask about, and hand over the documentation. Your audit team makes the call, but they should not find gaps in the trail.

Yes. Integration is usually via API, message queue or scheduled file exchange depending on what the vendor supports. We settle the approach during discovery.

Often not. We assess what is installed during the floor walk. Sometimes lighting is the real problem and the camera is fine. Where a sensor genuinely cannot support the inspection, we say so and specify what would.

Inference runs on local hardware, so inspection continues. Results queue locally and sync to the dashboard when connectivity returns. Nothing on the line waits for the cloud.

Fewer than most people expect for obvious defects, more than most expect for subtle ones. We give you a realistic sample requirement after seeing the defect classes, and we can start with synthetic augmentation where real defect images are scarce.

Your team retrains. Part of the handover is the labelling and retraining workflow, because a system only your vendor can update is a system that goes stale.

Yes, usually via OPC UA, MQTT or a direct database write, depending on the vendor. Integration is scoped during discovery.

It is normal, and it is the first thing we assess. A lot of the value comes from the index reconciling inconsistent naming and revision practice, rather than requiring you to clean up first.

Yes, through OCR and layout analysis. Accuracy depends on scan quality, which is why we benchmark against a sample of your worst documents, not your best.

No. Everything runs in infrastructure you control, and your content is not used to train any shared model. This is stated in the contract, not just the architecture.

We extract metadata, text and where needed geometry from common formats. Which formats matter for you is settled during the archive audit.

We provide the compute; your existing solver licences apply. Where licence pooling is the bottleneck rather than hardware, we will tell you that instead of selling you GPUs.

Usually depth in one area they have not had time for — serving economics, evaluation infrastructure or the enterprise security work. We work alongside your team, not instead of it, and we are explicit about where we are not needed.

Yes, and we will also tell you when not to. Migration makes sense at certain sustained utilisation levels and is a distraction below them. We model your actual usage before recommending a direction.

Both, though most engagements are weighted toward infrastructure and evaluation. If the modelling work is your core differentiation, you should keep it in-house and we will build around it.

Fixed-scope for discovery and defined builds, monthly retainer for managed infrastructure. Both quoted before work starts.

Rarely, and not as a first move. The audit usually finds two or three tools worth keeping and several worth consolidating at renewal. We sequence around your contract dates rather than forcing early exits.

The audit produces a usable picture in four to six weeks. The first department workload on the shared platform typically follows within a quarter. Full consolidation is a multi-quarter programme and we will not pretend otherwise.

Your choice. We can hand it over with documentation and training, run it under a managed agreement, or run it while your team ramps and then hand over. All three are common.

Yes. We are often brought in for the AI and infrastructure layer while an incumbent continues to run applications. Clear boundaries are agreed at the start.

Training, documentation and departmental champions are part of scope, not an afterthought. Adoption is measured and reported alongside technical delivery.