Use cases

Ship AI that stays online, stays on budget, and keeps your product fast

Real examples for founders and product teams. See what breaks today and what changes when ModelRiver runs behind your app.

ModelRiver sits between your app and AI providers to handle reliability, routing, budgets, and observability.

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Browse ready-made workflow templates in the docs

Works with leading AI providers
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Support & product

Customer chat that stays online

Keep chat online during provider outages

Your in-app assistant handles product questions and support tickets. When one AI provider goes down, chat stops. Users assume your product is broken.

  • Users keep getting answers while ModelRiver routes to a backup provider
  • Your team avoids emergency pages and manual provider switching

Example: A B2B SaaS app embeds help chat. OpenAI has an outage at 2pm. Support keeps running without your team waking up.

Learn more Live replies Try the ticket triage workflow
Related Auto failover Streaming

Ops & back office

Documents into clean records

Cut manual data entry on invoices and forms

Contracts, invoices, and intake forms arrive as PDFs and emails. Someone copies fields by hand, or the data lands in your system messy and inconsistent.

  • Turn uploaded documents into fields your CRM or database can use immediately
  • Reduce rework from inconsistent formats across teams

Example: A vendor invoice arrives by email. Amount, date, and vendor name land in your accounting tool with no manual entry.

Learn more
Related Structured data Guardrails

Product experience

Heavy AI without slowing your app

Keep your app fast while long AI jobs finish

Summaries, reports, and long analyses take time. If users wait on a loading screen, they leave. Under load, your app starts to feel broken.

  • Return control to users immediately while AI work runs in the background
  • Deliver results when ready without blocking the product experience

Example: After a sales call ends, a summary and action items appear in the CRM five minutes later. The rep never waited on a spinner.

Learn more Get notified when it's done Try the review insights workflow
Related Background jobs Webhooks

Founders & finance

AI spending you can explain to finance

Prevent surprise AI bills before finance sees them

AI usage grows fast. One busy week can blow past budget, and nobody notices until the invoice arrives.

  • Stop runaway usage before it becomes a line item nobody expected
  • Give finance a clear cap and usage view before approving more spend

Example: Your staging environment has a $200/month cap. A runaway test stops before it turns into a $2,000 mistake.

Learn more
Related Spending limits Analytics

E-commerce & support

Refunds handled end to end

Automate refunds without handing AI the keys to your money

Refund requests pile up. Reading each one, checking the order in your CRM, checking policy, issuing the refund, and writing back eats hours — but trusting AI with your money blindly is worse.

  • AI reads each request and recommends approve, deny, or escalate — your backend verifies the order and policy before anything moves
  • Refunds are issued by your systems on verified facts, and the customer reply is written from the confirmed refund, not a promise

Example: A customer asks to refund order #1234. AI recommends approval. Your backend verifies the order and issues $42 through your payment provider. Then AI drafts "Your refund of $42.00 is on its way" — and your backend sends it.

Get the refund template Read the story How pipelines work
Related Backend pipeline Webhooks Background jobs
Audience

Who this is for

ModelRiver sits behind your product, not in front of it.

Good fit

  • Ship AI into an existing product without rebuilding when providers change
  • Avoid downtime and surprise AI bills without hiring an infra team
  • Give ops one place to manage routing, limits, and request visibility

Not the goal

  • A chatbot website builder or standalone chat UI
  • A fully managed AI service where you do not bring your own provider keys
  • Replacing your product. ModelRiver powers what your users already use.

You keep your own AI provider keys and billing. ModelRiver is the control layer on top: reliability, budgets, and visibility.

Common questions

Quick answers for teams evaluating ModelRiver.

You point your app at one ModelRiver endpoint instead of calling each AI provider directly. Most teams keep their existing request shape and configure providers, failover, and limits in the console.

Yes. You connect your own provider keys in ModelRiver. You pay providers directly and pay ModelRiver for the platform layer on top.

ModelRiver can route the request to a backup provider you configured. Your users keep getting responses without custom fallback code in your app.

Yes. Set project and environment spending limits so runaway tests or traffic spikes stop before they turn into surprise invoices.

No. ModelRiver is infrastructure behind your product. You keep your own UI and app experience. ModelRiver handles routing, reliability, budgets, and observability.

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