Meet Overdrive
Find where your agents waste effort, and fix it.
Connect See how Overdrive works
First cohort gets a free diagnose run.
Illustrated conversation with Overdrive in your assistant. The receipt is an example, not a customer result.
Common prompts
Ask in Claude, ChatGPT, or Grok Bot. Overdrive answers with a line you can trace back to its source.
“What is our AI efficiency score, and what is holding it back?”
AI EFFICIENCYvalue vs. spend, with sources
“Which agents should we improve first?”
FIX FIRSTweakest agents, ranked
“Where is AI spend weak across Sales, Finance, Engineering, and Support?”
WEAK SPENDnamed by cost center
“What did the work we shipped unlock for customers?”
UNLOCKEDlinked to shipped work
“If we remove the modeled hours saved, is anything left?”
RECEIPTS ONLYwhat your rows support
“Trace that figure to rows in our own systems.”
EVIDENCEfile and line cited
What buyers ask us
These are the questions we hear most. Overdrive is being built to answer each one from your own rows. Detection for these patterns is still in progress, so today Overdrive cites what your data shows and says plainly when the evidence is not there.
Runaway agents
“Is this agent stuck in a loop, or is the long run worth paying for?”
Agent loops vs long-running work worth paying forOverspend
“Our AI bill went up. What did we get for it?”
Before and after: measuring AI quality, cost, and timeModel and task mismatch
“Are we paying for a reasoning model on work a cheaper one handles, or losing more to cheap-model retries?”
The true cost of reasoning models vs cheap modelsAgents waking each other
“Are our agents handing work back and forth, or retrying failed tools when they should stop?”
Tool errors, handoffs, and when an AI agent should stopSub-agent misuse
“Are sub-agents splitting the work, or redoing it with the same context?”
Requirements and task decomposition for AI agentsRepeated context
“Are we resending the same context on every call when caching would apply?”
Repeated context and caching: paying for the same tokensPrompting
“Are vague prompts turning into retries and long runs?”
AI prompting best practices: structure one prompt wellUnused and overlapping plans
“Which seats go unused, which plans overlap, and is work routed by limits instead of fit?”
Unused AI plans, overlap, and limit-driven routingExisting-tool alternatives
“Is this a job a script, query, or tool we already own does fine?”
When an existing tool beats an AI agentYour data
Overdrive reads files you export and bring, as CSVs or through your assistant. There are no live integrations yet. Overdrive does not connect to or pull from your systems.
Early access. Using Overdrive with your own company data is still going through security review. Until it clears, tour Overdrive with a sample pack and join the waitlist for a seat.
spend.csv: line items by cost center, provider, and plan, in seats, tokens, or requests.requests.csv: model, tool, token total, status, and retry links per request. Prompt text only with consent.jira.csv: a Jira issue export.crm.csv: a CRM opportunity export.support.csv: a support ticket export.fpa.csv, finance_process.csv, and vendor_map.csv: FP&A lines, finance process activity, and which vendor belongs to which cost center.Privacy
Connect
Overdrive is a remote MCP server, not the factory and not the agents. Clock in at mcp.useoverdrive.ai/connect: copy-paste this into your agent, or Install here. Sign in with OAuth using an email magic link. Never paste a token or API key into chat. Opening that page does not mean live ingest is on.
https://mcp.useoverdrive.aihttps://mcp.useoverdrive.ai/mcpWORKS WITH
Claude
ChatGPT
Grok Bot