What you get
Access for your team. Control for the business.
Employees get the best AI models through one secure login. Management gets centralized oversight, reporting, and budget control.
Access
The models people actually want to use, in one place.
- Claude, ChatGPT & Gemini through a single secure login
- The right models for the right roles
- Multi-model flexibility — no vendor lock-in
Control
Governance built in from day one, not bolted on.
- Centralized token allocation and budget caps, per team
- Permission-based user access
- Security guardrails and governance policies by default
Visibility
See exactly how AI is being used across the company.
- Full usage reporting and analytics
- Adoption and utilization across every team
- Spend you can actually explain on the P&L
Budgets, caps, and usage — per team, in one view.
Pricing that matches reality
Pay for usage. Not for seats nobody fills.
Per-seat enterprise licensing
You pay a flat per-seat cost across the whole organization, even though only a fraction of employees actually use AI daily. The economics rarely match the actual usage.
RothAI usage-based pricing
Scalable cost management without blanket enterprise licensing. You pay for the tokens your team actually uses — and you can see every one of them in the reporting.
Every token is visible in the reporting — no per-seat licenses, no hidden middleman markup.
Multi-model by design
Different work needs different models.
People genuinely prefer different models for different work — and adoption suffers when everyone is forced into one tool. RothAI lets each team work with the model that fits the job.
Models improve every month. With RothAI you switch as they do — without re-platforming the company.
- WritersClaude
- EngineersChatGPT
- Workspace teamsGemini
One workspace. Each team works with the model that fits the job — and you can change the lineup anytime.
Why it exists
We built it for ourselves first.
At Roth & Co we ran into the same problem as everyone else: employees were already using AI, and the business had no structure around it. We wanted our team to have the best models without giving up security, visibility, or cost control — and without locking everyone into a single vendor.
So we built our own internal AI platform. The goal wasn't to build another chatbot. The goal was to build something a business could actually manage, govern, and scale.