Aiveris · AI adoption partner
Frontier AI,
engineered for
serious companies.
We help small and mid-sized companies adopt frontier language models the way regulated industries need them built: measured, governed, and production-grade from day one.
From production: 4.2× contract-triage throughput vs. baseline · $312K annualized run-rate savings net of model, licensing, and engagement cost · 0 material audit findings across 3 external reviews.
Running agents against real tools? See Aiveris Platform decide what executes.
Company knowledge scattered across docs, tickets, chat, and people? Company Brain maps workflows, owners, contradictions, and freshness before anyone pretends a chatbot fixed it.
Call Aiveris: (856) 367-6198
The adoption journey
How does Aiveris move companies from first AI pilot to production?
Aiveris runs four stages: readiness map, measured pilot, production system, and governance scale-up. Each stage has defined deliverables, exit criteria, and artifacts the client can keep.
What does an AI readiness and opportunity map cover?
2 weeksWe interview leaders, shadow operators, and map your workflows against frontier-model capabilities. You end with a ranked opportunity list and a pilot plan your CFO can price.
What is a measured AI pilot?
6–8 weeksOne high-value workflow, instrumented from day one. Accuracy, cost per run, reviewer time saved — all tracked against a baseline, with evaluation sets your domain experts sign off on.
What does an AI production system include?
8–12 weeksWe build and ship the pilot as a real internal system — auth, audit logs, human-in-the-loop review, cost controls, and fallbacks. Delivered with runbooks and a handoff your engineers own.
How do you scale AI governance across teams?
OngoingPolicy, review cadence, model-choice rubric, and a portfolio view of all AI workflows in flight. Makes your AI program legible to the board, auditable by compliance, and safe to expand.
What the work produces
Numbers our clients have defended to their boards.
Governance, by default
What does built for regulated industries mean for AI adoption?
It means access control, audit trails, redaction, human review gates, cost controls, and governance are designed into the first production path before the system expands.
How do you govern AI agents before they touch production systems?
Governed agents need a gateway, scoped namespaces, approval gates, policy checks, and audit evidence before they call SaaS, code, data, or internal systems.
- One governed path for every agent call into SaaS, code, data, and internal systems.
- Policy before execution using identity, namespace, action risk, approvals, and environment boundaries.
- Audit evidence by default so security, legal, and leadership can see what happened and why.
What is an MCP gateway?
Expose approved tool servers through one security boundary instead of dozens of ad hoc agent connections.
What is namespace isolation for AI agents?
Separate prod, staging, client, and department tools so agents only see what they should use.
What are AI agent approval gates?
Escalate destructive, costly, or sensitive actions before execution without stopping low-risk work.
What is an AI compliance trail?
Capture policy decisions, approvals, redactions, and tool-call evidence for review.
Verified agent delivery
How do you know an AI agent delivered?
An agent can finish a run and still miss the job. Aiveris builds checks around the result your business needs, with evidence for each requirement and a clear record of anything unresolved.
Define success.
Define the required output, the business rules it must satisfy, and who can approve exceptions before work starts.
Check the real result.
Inspect the delivered file or changed system against those requirements. When a task updates a live system, verify the change there.
Test the checks.
Try deliberately incorrect or incomplete results. A useful check must catch the failures that matter to the workflow.
Show what passed.
Keep evidence tied to the version reviewed. Use independent review where judgment remains. Required human approval stays pending until the right person gives it.
Example: An agent updates customer records. Verify the intended records changed, required fields are correct, and unrelated records stayed untouched. Missing evidence or approval keeps the job open.
FAQ
Common questions before an AI adoption call.
How does Aiveris move companies from first AI pilot to production?
Aiveris runs four stages: readiness map, measured pilot, production system, and governance scale-up. Each stage has defined deliverables, exit criteria, and artifacts the client can keep.
What does built for regulated industries mean for AI adoption?
It means access control, audit trails, redaction, human review gates, cost controls, and governance are designed into the first production path before the system expands.
What is a Company Brain operating map?
A Company Brain operating map links workflows, roles, systems, decisions, owners, freshness, contradictions, and confidence so teams can see how work moves before they automate it.
How do you govern AI agents before they touch production systems?
Governed agents need a gateway, scoped namespaces, approval gates, policy checks, and audit evidence before they call SaaS, code, data, or internal systems.
How do you find LLM cost waste in production?
Aiveris reviews safe logs, traces, prompts, and metadata to find redundant calls, model mismatches, cache gaps, and workflow patterns that create avoidable LLM spend.
How do you know an AI agent delivered?
Aiveris defines success before execution, checks the delivered result against those requirements, and keeps evidence of what passed or failed. Checks are tested with incorrect outputs. Unresolved requirements and missing human approvals keep the job open.
How do you start working with Aiveris?
Start with a 30-minute call. Aiveris reviews one workflow, names the right engagement stage, and leaves you with a concrete view of what a pilot would require.
Let's talk
How do you start working with Aiveris?
Start with a 30-minute call. Aiveris reviews one workflow, names the right engagement stage, and leaves you with a concrete view of what a pilot would require.