Verifiable (Acceptance‑ready)
Checklist + regression.
Key highlights
- Acceptance checklist
- Regression tests
Your dedicated AI assistant that understands your business, 100% controlled, never leaks
Natural language → governed, traceable output. Reviewable before execution. Acceptance-ready in 10 days.
Data Sovereignty × Cost Control × Compliance Ready
100% On-Prem Deployment, Data Stays Yours, Verifiable in 10 Days
AI is powerful—but enterprise adoption often gets stuck on security boundaries, cloud uncontrollability, and integration back into existing systems
Sending confidential data to cloud inference makes leaks hard to trace; accountability, audits, and remediation become painful
AT&T said a dataset released on the dark web impacts about 7.6M current and 65.4M former account holders (≈73M total), and may include sensitive personal information such as Social Security numbers.
Model updates, policies, availability, and output consistency are out of your control; once it changes, your entire workflow becomes unstable
SailPoint's global survey reports that 80% of companies say their AI agents have taken unintended actions — including accessing unauthorized systems or resources (39%). The report also notes 23% have seen agents tricked into revealing access credentials. When cloud models are connected to tools and file permissions, one unintended action can become an irreversible mistake (e.g., deleting or overwriting critical data).
Even if answers are good, if you can't safely connect back to CRM/ERP/ticket workflows, it becomes copy‑paste and can't scale
BC’s Civil Resolution Tribunal found Air Canada responsible for misleading information on its website chatbot and ordered it to pay CA$812.02 (damages + interest + fees). If AI answers are inconsistent with authoritative policy pages and business rules, it's unsafe to connect outputs back into workflows.
From requirements, data governance, access control to delivery verification—we use enterprise-grade methods to make AI your standard process, not a one-time demo.
Checklists, rules, templates, logs—complete deliverables
Use delivery checklists and test sets to systematize quality; key outputs include sources, and can be regression-tested after changes.
On-Prem/Private, SSO/RBAC & audit trail
On-Prem / Private AI: data & inference stay in your network, integrate with SSO/RBAC and maintain audit logs.
One-time setup + annual maintenance, predictable costs
One-time purchase + annual maintenance, clear fee structure; can be budgeted with fixed specs and maintenance items.
Verifiable · Controlled · Traceable · Zero‑touch
Checklist + regression.
Rule‑based: answer / review / refuse.
Citations + logs.
Make legacy systems speak human language—zero code change.
From kW-scale server rooms to 240W desktops—sustainability with enterprise responsibility
Turn “we want AI” into verifiable delivery: define success criteria, configure rules/knowledge in UI, then validate with real questions and iterate.
Plug AI into your day-to-day operations workflow
Define outcomes with KPIs; reduce risk with rules + citations (NDA case details available).
One sentence to make legacy systems understand humans—no architecture change; AI drafts first, you decide whether to execute
Clear procurement breakdown, predictable total cost
Renew annual maintenance each year to keep lifetime software warranty active and receive hardware replacement for non-human-caused normal wear and tear.
Pick your scenario first (tasks, risks, verifiable deliverables), then validate quickly with the online demo.