Feerasta systems are designed around private data boundaries, human oversight, audit trails, explicit model policy, and deployment options that match the risk of the work.
We map systems of record, document stores, data sensitivity, retention boundaries, and integration paths before any production build.
Actions are classified by risk. High-impact actions require approval, escalation, or refusal instead of unsupervised automation.
Inputs, sources, prompts, tool calls, recommendations, approvals, and outcomes are logged so the system is reviewable.
Each deployment defines approved models, vendors, retention settings, fallback rules, evaluation criteria, and prohibited data flows.
Cloud, private cloud, on-prem, and hybrid deployments are scoped based on data sensitivity, latency, integration, and compliance needs.
Launched systems get monitoring, monthly reports, change logs, incident review, retraining notes, and an optimization backlog.
Not every workflow needs the same deployment model. The right answer depends on data sensitivity, user permissions, action risk, and integration depth.
Marketing, website, local SEO, reviews, and low-risk content systems with clear approvals.
CRM, finance, inbox, calendar, documents, and operations systems with access controls and audit logs.
Legal, clinical, financial, or sensitive IP work with citations, stricter human review, and private deployment options.
On-prem or private-cloud AI where data must stay on your ground and operation is attested over time.
Feerasta Enterprise is built for buyers who need more than a demo. The assessment produces the security, governance, and implementation context needed before a serious pilot.