Get more shipped work from your AI budget
Your agents should spend their capacity building, not rediscovering how your product works. Your team should see what changed without piecing it together from code.
Start from the work that costs you most
Your next feature shouldn't start with another investigation
Give Claude Code the business logic its task needs. Spend less time waiting for repository searches and fixing missed rules.
Ship faster on your own →Four AI bills. One expensive bottleneck
Stop burning premium capacity on repeated discovery and correction. Test how much less AI spend can deliver the same accepted work.
Make your AI budget go further →More agents. Fewer changes coming back
Give agents a shared understanding across services. Catch effects on billing, permissions and customer paths before they become another correction cycle.
Shorten your release cycle →Your release queue shouldn't grow with your agent fleet
Let reviewers see changes to product behavior and the paths they affect. Release more work without adding a reviewer for every new agent.
Clear the review bottleneck →Keep the margin on every client change
Scope unfamiliar systems, give agents the right context and show clients what changed. Turn discovery and rework hours into delivery capacity.
Improve delivery margins →Thousands of screens. One visible product
Run the model, Intelligence and agent workflow inside your infrastructure. Understand the affected business logic and review changes visually.
Plan a private pilot →Free senior engineers from routine explanations and approvals
Add an account role without missing plan restrictions.
See the SaaS startup case →Lower the cost of a completed customer action
Resolve an invoice dispute using current entitlement and refund rules.
See the AI product case →Change old software while preserving business behavior
Move invoice generation out of a monolith.
See the migration case →Spend less reaching the first paid proof
Research a narrow gap, build one workflow, sell a pilot.
See the idea-to-revenue case →One Intelligence
Three places it works
AI building your product
- Change accuracy — The agent sees what a change actually affects before it touches the code.
- Less rework — Fewer wrong changes reach review, so the second pass stops being where the work is.
- Lower AI cost — The same answer on a third of the context, because nothing is reconstructed twice.
- Faster onboarding — A new engineer — or a new agent — can ask product-level questions on day one.
- Change impact — What will break, before anything is executed.
Intelligence · Vibe · Cloud Compute · MeetAI
AI operating your product
- Support — The agent knows the customer, the plan, the entitlement and what it is allowed to do.
- Billing — The invoice, the limits, the contract state and which adjustments are permitted.
- Sales — The account, its real usage, its commercial state and which upsells are actually valid.
- Customer success — Adoption, churn signals, the state of the product and what to recommend.
- Agent networks — One shared understanding instead of every agent rebuilding the same picture.
Intelligence · MCP
From an idea to buyers
- Find the market opportunity — What buyers use today, where they hurt and what they are not getting.
- Define what should be built — Requirements that say why they exist — the opportunity, the pain and the evidence.
- Find likely buyers — Companies that match the profile, each with its evidence and a score.
- Turn signals into conversations — Messages written from each company’s own signals, sent by you.
- Feed buyer evidence back — Replies bear on the positioning and the requirements; repeated ones become proposed changes.
GoBots · Market · ICP · Outreach
Find your work in it
The same Intelligence can answer a developer, a founder, a reviewer or an agent differently — without forcing any of them to reconstruct the same underlying reality.
Stop reconstructing the product before every task
Ask how behavior works, trace business logic and see what a change can affect — without keeping the whole system in your head.
Get useful understanding before the first explanation meeting
Move through an unfamiliar product from the level of behavior and rules, then go down to implementation only when you need it.
Put a requirement next to the product that already exists
See what is already there, what would change and what still needs clarification before work is handed to engineering.
Keep visibility without reading every implementation detail
See how product behavior evolves, what people and AI changed, and where attention is actually required.
Review the behavior that matters, not every file that moved
See which product logic a change reaches and compare implementation with the intended requirement or expected behavior.
Know what a client request means before you price and promise it
Put a brief against the existing product, expose scope and open questions, and hand the team work grounded in the same understanding.
Give coding agents enough product context to work from
Create Intelligence-backed tasks and expose them through MCP so agents start closer to the real product meaning.
Give each agent step only the Intelligence it needs
Connect product and company context to existing workflows so autonomous systems can operate with less repeated discovery.
Get the answer you need without becoming the analyst
Validate outcomes, compare intent with what was built, and surface exceptions without spending attention on the underlying complexity.
The measurement does not
change with company size
One repository, fifty questions written as real engineering requests, the model held constant, answers scored blind. This is the part that is measured; everything on the pages behind these cards is a situation, not a case study.
Measure the difference
on your own system
Build Intelligence for your repository, or put a baseline around one agent workflow. Both start from the same layer.
10,000 STARTING CREDITS INCLUDED · YOU SEE THE ESTIMATE BEFORE A BUILD STARTS · PROCESSING STOPS WITH YOUR BALANCE AND RESUMES AFTER A TOP-UP