Cut AI Development
Time & Cost
AI coding agents spend up to 80% of their tokens and billable time wandering through your codebase. Vylor gives your agents the right context upfront, turning endless exploration into finished deliverables.
Outcome delivery · Measurable token reduction · Seamless integration
- Claude Code
- Cursor
- Claude Desktop
- VS Code
- Antigravity
- Cline
Your agent spends most of its time
looking, not building.
AI coding agents don't know your codebase, so they crawl it, again and again. That exploration is billed exactly like real work.
Whole agents spent just finding things
Before writing a line, agents spin up background explorers that read file after file to rebuild a picture of your repo, and you pay for every turn.
grep, ls, find… repeat
Endless search and directory-listing calls to locate one function. Each round-trip costs tokens and model time while nothing actually ships.
Every file read gets paid for again
Files pulled in during exploration sit in the prompt cache and are re-read on every later turn, so wasted exploration keeps getting more expensive.
Up to 8x Better Cost Efficiency
Same scope. Same outcome. A fraction of the spend.
Optimized plan-execute workflow focused on outcome delivery.
More iteration cycles and context rework per completed task.
Want Vylor credits for your team?
Talk to usArchitected for outcome delivery,
not endless discovery.
Traditional agents waste the majority of their budget finding where things live. Vylor restructures the workflow so models spend their compute doing what they do best: writing great code.
Targeted context, zero blind discovery
Instead of letting agents explore your workspace file by file, Vylor supplies structured, AST-level code intelligence upfront. Your agent knows precisely where to look on turn one.
Outcome-driven execution cycles
Replaces runaway search and retry loops with focused, plan-and-execute workflows that deliver verified changes in a fraction of the turns and token burn.
Works across your existing agent stack
No proprietary IDE lock-in or developer workflow disruption. Vylor integrates naturally with the coding agents, CLI tools, and editors your engineering team already uses.
Find the right fit for your team's codebase.
Every engineering organization has a unique repo architecture, agent stack, and budget. Connect with our team to evaluate your current agent spend and see how Vylor can be tailored to your environment.
- Tailored to your repo scale
- Up to 8x token savings
- Strict privacy & security
See exactly how much Vylor
will cut your costs.
One command reads your existing Claude Code and Claude Desktop sessions and shows the spend, tokens and time that went into avoidable exploration.
Runs 100% locally. No data is sent anywhere.Install
Standalone binary. No Python needed.
$ curl -fsSL https://raw.githubusercontent.com/Vylor-AI/vylor-saving-estimator/main/install.sh | shRun it
Auto-discovers your sessions (defaults to the last 30 days).
$ vylor-estimate- --week · last 7 days
- --all · all-time
- --since 2026-09-01 · custom start date
- -d · detailed breakdown
Read your savings
- Total
- What your analyzed sessions cost, in dollars, tokens and agent-time.
- Estimated Vylor savings
- The share spent on avoidable exploration that Vylor's structured solution eliminates.
- Top savings sources
- What drives it: Explore sub-agents, searches, directory listing.
The result estimates avoidable exploration, not a guaranteed saving. It shows how much of your usage looks like work a code-intelligence tool could have handled. Sessions with optimized context workflows are skipped automatically.
Your code never leaves your machine.
Zero training. Zero leaks.
Vylor is built local-first. Your code stays entirely on your computer, so your proprietary intellectual property and sensitive credentials never leave your control.
Code stays on your computer
Everything runs directly on your machine. Your project is analyzed locally, and your source code is never uploaded, synced, or sent to outside servers.
- 100% local analysis on your machine
- Zero code uploaded to outside servers
- Works completely offline and private
Never used to train AI models
Your code is never stored, collected, or used to train or fine-tune any AI models. What you build belongs 100% to you and your team.
- No AI model training on your work
- No prompt logging or harvesting
- Your intellectual property stays protected
Secrets and passwords stay hidden
Vylor automatically ignores your .env files, private keys, passwords, and .gitignore rules so sensitive credentials are never exposed to the AI.
- Ignores .gitignore and .env files automatically
- Prevents API keys from leaking to AI
- Safe by default with zero setup needed
You see everything your agent sees
No hidden cloud activity or mystery scripts. You have complete visibility and control over exactly what information is shared with your agent.
- Transparent, inspectable activity
- No background cloud uploads
- Built for teams with strict security rules
Have strict security or compliance requirements?
We work directly with engineering teams and security leads to review our local architecture.
Give your AI agent
the context it deserves.
Talk to our team to discover how Vylor fits your engineering workflow, or run the local estimator to benchmark your team's avoidable AI spend.
Measurable ROI · 100% local analysis · Tailored engineering setup