Softwavz
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Softwavz
Most AI projects stall because they are built as experiments rather than as features. We take the opposite route: the model is one component inside a product that already has a login, an audit trail, a permissions model and somewhere for the output to go. That is the difference between a demo that impresses once and a system your team opens every morning.
Our AI work is already running in production. AI Vet Pro turns a spoken consultation into a structured SOAP note. EyeCare Pro grades retinal images for diabetic retinopathy in seconds. Advocate Pro surfaces relevant precedent from a firm’s own document history. Each of those started as the same conversation you are about to have.
Dictated consultations, site inspections or field reports converted into the exact record format your system expects — not a transcript, a filled-in form.
Image classification and anomaly detection for clinical screening, quality control and defect spotting, with confidence scores a human can overrule.
Invoices, contracts, lab reports and purchase orders read automatically, with the extracted fields pushed straight into your ERP or database.
Retrieval-based assistants that answer only from your own documents and cite the source, so staff can trust the answer instead of double-checking it.
Demand, capacity and scheduling models that feed real planning screens — the kind driving our production planning platform.
Rule-plus-model guardrails such as drug interaction warnings, threshold alerts and triage scoring, designed to flag rather than to decide.
We look at your data before promising anything. If the data will not support the outcome you want, we say so in the first week rather than the sixth month.
One workflow, one measurable target — minutes saved per record, percentage of documents auto-processed, recall on the cases that matter.
Permissions, audit logging, human override, fallback behaviour when the model is unsure, and monitoring of accuracy over time.
Your team gets the runbook. We keep watching for accuracy drift, because a model that was right last year is not automatically right today.
No. Where we use third-party models we use configurations that exclude your data from training, and for sensitive workloads we deploy models you host yourself.
Every output we ship is reviewable and overridable by a person, and the system records who accepted or changed it. For clinical and compliance work that audit trail is not optional.
Less than people expect for document and speech tasks, because we start from pre-trained models. Image classification for a specialised defect usually needs a few thousand labelled examples — we tell you the honest number after looking.
Book a 45-minute session and we will walk through ai solutions against your own workflow.
