Pricing your AI Operating System around ROI.

We do not price around random outputs or disconnected AI features. We custom quote around expected business impact so your operating system produces more value than it costs.

The pricing conversation

The same question comes up every time, and the answer is always tied to measurable business return.

CL

Client

How much does this cost?

RA

RIV AI

If we do this properly, your AI Operating System should produce more ROI than it costs.

This is why every quote is custom. We align scope with value creation, then track performance so your investment stays accountable.

How pricing works

Most engagements include a build phase and an operating phase, with expansion based on results.

Upfront Build & Integration

Architecture, implementation, and integration of your AI Operating System based on your workflows and systems.

Monthly Operating Retainer

Ongoing hosting, monitoring, maintenance, optimization, and support to keep your AI OS healthy and improving.

Expansion As Needed

Add modules, workflows, or integrations over time as your team scales and new ROI opportunities become clear.

What drives price

  • Number of AI OS modules deployed in phase one
  • Integration complexity across CRM, ERP, inbox, and finance tools
  • Workflow volume and automation depth
  • Compliance, security, and reporting requirements
  • Support level and speed of ongoing iteration

Included in every engagement

  • Business-value scoping tied to target outcomes
  • Implementation roadmap with milestone visibility
  • KPI baseline and monthly ROI tracking framework
  • Team enablement so adoption is not a bottleneck

How each AI OS module creates ROI

Your quote reflects where value is expected to show up first, then how we expand from there.

Knowledge

Faster answers and less internal waiting time.

KPI examples: Onboarding speed, internal response time, repeated question volume.

Agents

Higher lead capture and better response coverage.

KPI examples: Response time, missed inquiry rate, conversion lift.

Workflows

Lower admin load and fewer process errors.

KPI examples: Hours saved, rework volume, throughput speed.

Visibility

Better decisions with real-time operational clarity.

KPI examples: KPI visibility latency, bottleneck frequency, forecasting confidence.

Fluency

Higher adoption and sustained ROI from your systems.

KPI examples: Adoption rates, usage consistency, support dependency trend.

How we keep ROI accountable

Step 1

Define target business outcomes before implementation begins.

Step 2

Establish a baseline of current time, cost, and performance metrics.

Step 3

Deploy prioritized AI OS components against the highest-value constraints.

Step 4

Review KPI movement and optimize scope to keep value above cost.

Common pricing questions

Next Step

Book a strategy call and we will map the highest-ROI path for your AI Operating System.

1. Clarify your top constraints and opportunities.

2. Identify which modules should go first.

3. Align pricing with expected outcomes.