Your coding agent is
Archy gives AI coding agents a live system model — architecture, schema, dependencies, risk, and prioritized work — so they stop wasting money on context rebuild, false starts, and rework.
get_context({ objective: "add Stripe webhook" }){
"profile": "build",
"relevantFiles": [
"server/routes/stripe-webhooks.ts",
"server/services/payment-service.ts",
"shared/schema.ts"
],
"relatedRoutes": [
{ "path": "POST /api/stripe/webhook",
"file": "server/routes/stripe-webhooks.ts" }
],
"riskItems": [
{ "title": "Webhook signature not verified",
"severity": "high",
"action": "Add signature verification" }
],
"architectureNotes": [
"Frameworks: Express, React, Drizzle ORM",
"Repo risk score: 32"
]
}How it works
From invisible waste to visible progress.
Set up fast, expose what is wasting time and money, then let your agents repair the product with system-aware context.
Connect in 10 minutes
Connect your repository and Archy maps architecture, dependencies, schema, service boundaries, and user journeys into a live system model your agents can query through MCP.
See where money is leaking
Archy surfaces risk, rework pressure, architectural drift, and the highest-cost problems first so your agents stop fixing the wrong thing.
Repair toward market-ready
Your agent pulls the right task, makes the change, and Archy verifies the result on the next pass so the product gets stronger instead of noisier.
The problem and the fix
Fast agents still waste serious money.
They generate from prompts and context windows, not from a full understanding of your system. Archy closes that gap.
Without Archy
Blind agents create expensive churn
Without system context, agents create false starts, wrong-file fixes, regressions, and cleanup work that should never have existed.
Prompt stuffing burns time and tokens
Dumping docs into a prompt is expensive, incomplete, and unreliable. Agents need a live system model, not another context window tax.
No feedback means no progress
Without a reliable baseline, you cannot tell whether the product is actually getting stronger. Teams stay stuck in almost-ready mode.
With Archy
Live system state via MCP
Architecture, schema, risk, and remediation context delivered to the agent while it is actually making decisions.
Prioritized, not guessed
High-impact tasks ranked by system analysis. Agents start with the right fix instead of whatever file happens to be open.
Verified improvement
Each pass rechecks the system. You see what improved, what regressed, and what still needs work.
Why this is different
More context is not system understanding.
Better prompts do not create a live architecture model. Faster agents just make expensive mistakes faster. Archy gives the agent persistent system state, not another temporary guess.
Longer context windows don't help
Dumping more files into a prompt creates noise, not understanding. The agent still has no model of how the system fits together.
Better models still guess
A smarter model guessing at architecture is still guessing. Archy provides deterministic facts: schema, dependencies, service boundaries, risk scores.
One-shot analysis decays instantly
A snapshot of the repo last week is stale today. Archy maintains a live model that updates with every analysis pass.
// Agent claims next remediation task
claim_next_task()
// Archy returns:
{
"task": {
"title": "Add rate limiting to /api/auth/*",
"severity": "high",
"file": "server/routes/auth.ts",
"context": {
"currentState": "No rate limiting on auth endpoints",
"impact": "Brute-force login attacks possible",
"relatedFiles": ["server/middleware/rate-limit.ts"],
"verificationCriteria": "Rate limit middleware applied to all auth routes"
}
}
}Trust and security
Your code is safe with us
Enterprise-grade security for your repository data. We read your code to analyze it. We never store, share, or train on it.
Your code stays private
Repository data is processed in isolated environments and never shared across accounts. We don't train on your code.
Read-only access
Archy only reads your repository. We never push code, modify branches, or make changes to your codebase.
No secrets stored
Environment variables, API keys, and credentials are excluded from analysis. Sensitive patterns are detected and filtered before processing.
Deterministic analysis
Core analysis is rules-based, not LLM-generated. Results are reproducible, auditable, and consistent across runs.
Encrypted at rest
OAuth tokens encrypted with AES-256-GCM. All data encrypted at rest and in transit. SOC-2 compliance in progress.
US legal entity
Architectural Intelligence LLC. Wyoming incorporated, US-governed agreements.
Stop paying for blind execution.
Connect your repository in 10 minutes. Use the 3-day free trial to expose what is wasting time, money, and engineering effort.