Stop guessing which model, prompt, or skill to use. Get a data backed answer in the chat you're already in.
For teamsSee the business impact of what works across your whole org and turn it into the playbook your top performers already use.
cold-outreachskill applies here. It's landed 2.4x better than ad-hoc drafts on similar prompts in the last 90 days. Want me to invoke it?Which model, which prompt, which skill, which context. Your team picks based on what they did last time, not what works.
When AI output falls short, your team fixes it. They re-prompt, edit, rephrase, or just do it themselves. That effort is invisible to every analytics tool you have.
Your team can't see which approaches actually produce better outcomes, so they keep doing the same things.
Specific suggestions in the chat your team is already using.
When someone's prompts skew low-delegation for too long, we flag it with examples of higher-leverage alternatives for the same task.
When a session matches a known workflow (cold outreach, code review, debugging), we surface what's worked for that workflow before.
Your org installed skills and prompt libraries that Claude underuses. We catch the misses and suggest the right one when the prompt warrants it.
Patterns your top performers use can be reviewed, edited, and blessed as your org's recommended approach.
Every suggestion is surfaced through MCP. The user always decides whether to take it.
From install to first suggestion in the same afternoon. Everything important happens client-side. Only structured metrics ever leave the device.
Your team installs the FullOversight Chrome extension. Prompt classification runs locally in the browser. Raw conversation content never leaves the device. Only structured metrics are transmitted.
As your team works, the extension watches what gets accepted, what gets fixed, and what gets thrown out. Available from day one. Becomes sharper as more sessions accumulate.
Suggestions show up where work happens, inside Claude or ChatGPT through MCP. Your team asks "which model should I use for this" or "is there a skill that fits this prompt" in the chat they're already in. No separate tool, no second login.
Suggestions get better as your own history grows and as the cross-user dataset fills in. See how your patterns compare to peers at similar companies, with monthly benchmark refreshes.
Measurement is how the suggestion engine learns what works. Here's what it sees.
How often is the AI's initial output used without changes?
What fraction of interactions require the human to ask for a fix?
Total active time your team spends reviewing and reworking output.
How much of the conversation is spent getting the AI to understand the request?
Which workflows get delegated to AI and how each one performs.
cold-outreach skill applies here and tends to land 2x better than ad-hoc rewrites. use it?The suggestion surface, delivered inside the chat your team already uses.
You're using Claude or ChatGPT every day for marketing emails, code, planning docs, whatever the work is. You don't want to read dashboards. You want to know which model, which prompt, which context to use for the task in front of you.
You're planning the next phase of AI adoption. Expand, consolidate, build internally? You need data on what's actually working in production before you make the call.
You're making AI adoption work. We give you the measurement framework to prove what's landing and fix what isn't, with evidence, not anecdotes.
Platform providers track usage. Evaluation companies test output quality. Nobody tells your team which configuration to use for the task in front of them.
Conversation content never leaves your employees' devices. Classification runs locally. Only structured metrics are transmitted, and the extension is open source.
Classification runs locally in the browser. Only structured metrics are transmitted, things like first_shot_accepted, intent_label, session_count. The extension is open source. Audit the code yourself.
OpenAI won't flag that their tool underperforms on your legal workflows. Anthropic won't surface that your team spends more time correcting output than writing it themselves. We're tool-agnostic by design. The only thing we sell is the suggestion.
The suggestion engine routes across model, prompt, skill, and context ecosystems, so vendor neutrality matters even more.
Peer-reviewed methodology · Independent suggestions · Tool-agnostic
The suggestion surface is what we're building with the first users. Your sessions and your reactions to early versions are how we figure out what actually helps.
Free for individuals using their own data. We're recruiting a small cohort of friendly-beta users across role archetypes (marketing, engineering, CS, ops) to shape the v0 suggestion surface.
A working partnership, not a discount pitch. We're figuring out which signals predict whose work ships well, which suggestions move those signals, and how any of it varies across teams. Partners who show up, suggest metrics, and react to early versions shape what gets built.