Why Rightbrain
The problem: most AI never makes it to production
95% of AI proofs-of-concept fail before deployment. Not because the models don’t work, but because everything around them doesn’t.
Teams spend months building scaffolding: APIs, infrastructure, observability, compliance, rollback logic. By the time it’s ready, the opportunity has passed.
8 months is the average time it takes a team to move from prototype to production. Most never make it.
The real bottleneck: operations, not intelligence
Prototypes are easy. Production is hard.
The infrastructure trap
“Week 1: we built a compelling demo.” “Week 4: we need error handling.” “Week 8: where are the logs?” “Week 16: the compliance team has questions.” “Week 32: let’s start again, properly this time.”
Teams build everything from scratch, only to realize they needed versioning, audit trails, and rollback from day one.
The Rightbrain approach: agents you can operate
Rightbrain replaces custom infrastructure with agents that intelligently use the four primitives you make available to them (tasks, skills, collections, and connections — each managed and versioned independently) and a set of production controls that come built in: human-in-the-loop approvals, evals, fallback models, versioned revisions, and a tamper-evident audit log.
Rightbrain turns AI from an experiment into an operational system.
Proof in action: PAL.health
Goal. Launch a production-ready AI health coach app without building any AI infrastructure.
Solution. Rightbrain powers every AI feature in their platform.
Results.
- 20+ AI agents live in production
- 1 engineer needed to operate them
- Model swaps and fixes shipped daily, without a rebuild
I can switch models, fix issues, and ship updates daily without waiting on engineers. Without Rightbrain, this simply wouldn’t be possible.
— Chris Davison, Founder & CEO, PAL.health
We started with one agent where we knew we’d see value. Now we’re stacking different use cases as we uncover new processes to automate.
— Joe Mclaughlin, Account Director, Rocket SaaS
Reliability and governance you can show
The operational story is not a promise; it is instrumented.
- Reliability, proven. Give any agent a fallback model and a provider outage fails over mid-run without restarting, preserving the work done so far. The run records which model served it, why the primary failed, and the per-model token and credit split, so you get reliability and observability at once. You can rehearse this before it happens: chaos testing injects deterministic tool and model faults into a run and lets you inspect the recovery.
- Governance, by construction. Runs, sessions, and history never leave the project that owns them. When you share an agent, a public link is read-only inspection, never execution; when you clone one into another project, secret-bearing connections copy as metadata only and come across marked for re-authentication. Every project keeps control of its own credentials, model policy, and runtime history.
- Speed, measured. One workspace-intelligence agent read 24,200 characters across a set of documents through a Notion MCP connection and returned a 5,200-character weekly briefing, a 4.7x compression, in 107.8 seconds from a single API call, with Rightbrain managing the OAuth token lifecycle throughout.
Why it matters
Companies that win with AI aren’t the ones with the biggest models. They’re the ones that ship fast, iterate safely, and scale predictably. Rightbrain gives you the foundation to do exactly that, from an idea in natural language to a production agent in a fraction of the time.