Your codebase should not depend on who happens to remember it.
Persistent codebase intelligence for engineering teams carrying invisible complexity.
SMPL is an AI system that maps your architecture, evaluates work before engineers lose time to ambiguity, and accumulates expertise that never quits, forgets, or walks out the door. When execution is needed, that same intelligence extends into delivery.
When knowledge stops walking out the door, teams move faster.
Make your codebase legible, investigable, and less fragile.
SMPL is an AI system that gives engineering leaders persistent understanding of how their systems actually work, so ticket evaluation gets sharper, investigations get faster, and execution stops depending on a few overburdened people.
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Your backlog is a clarity problem before it becomes a capacity problem.
Teams slow down when work reaches engineers before anyone truly understands it. What looks like a throughput problem is often an understanding problem in disguise.
Senior engineers spend their days on repeated archaeology: tracing code paths, interpreting tickets, answering the same architectural questions, and reconstructing old decisions.
When key people leave, undocumented knowledge leaves with them. The codebase becomes more fragile, more political, and harder to change safely.
Code completion tools help individuals type faster. They do not make the system more legible, the backlog more coherent, or the organization less dependent on tribal knowledge.
Persistent codebase intelligence for teams that have outgrown tribal knowledge.
Persistent codebase understanding
Maps your repository as a living system: architecture, dependencies, patterns, and the relationships that actually govern change.
Investigation before execution
Evaluates tickets, traces relevant code paths, identifies edge cases, and turns ambiguity into a usable plan before work starts.
Institutional memory that compounds
Every investigation and evaluation deepens the system's understanding, so the fiftieth answer is better than the fifth.
Works where your team already works
Shows up in the systems your team already uses, so understanding is available inside real workflows, not trapped in a separate tool.
Proof that understanding compounds.
Engineering Team Deployment
20+ person team · Series B · Complex monorepo
- 67 effort points completed in the first week
- 89 tickets reviewed and triaged in a single day
- Onboarded to the full codebase in under 24 hours
Support Operations Deployment
High-volume support · Legacy systems · Distributed team
- Root cause identified in 15 minutes, not days
- Knowledge accumulated across every resolved issue
- Support-to-engineering escalations dropped significantly
Deploy in days. Useful immediately. Smarter every week.
Connect
Connect repositories and systems. SMPL begins mapping your architecture, dependencies, and patterns.
Map
Build a living model of the codebase so questions, risks, and dependencies become visible.
Investigate
Use that understanding on real tickets and incidents, before engineers lose time to ambiguity.
Accumulate
Every task leaves the system smarter, building reusable institutional knowledge instead of throwing it away.
Start with clarity. Expand into investigation and execution.
Codebase Intelligence
$2K/mo
- Persistent repository mapping
- Architecture documentation
- Dependency analysis
- Institutional knowledge capture
Investigation Engine
$5K/mo
- Everything in Codebase Intelligence
- Ticket triage and evaluation
- Root cause analysis
- Impact assessment before execution
Execution Peer
$10–20K/mo
- Everything in Investigation Engine
- Production code delivery
- PR creation and review response
- Execution grounded in accumulated understanding
Start with legibility. Expand only when the system proves itself. Productive in 7 days or your first month is free.
Questions
How does it understand our codebase?
SMPL is an AI system that maps your entire repository, including files, dependencies, architecture patterns, naming conventions, and the relationships between components. This isn't keyword search. It builds a structural understanding of how your system works.
What tools does it integrate with?
It works with your existing stack — GitHub, GitLab, Linear, Jira, Slack. It shows up where your team already works, not in a separate interface.
Is our code secure?
Your code never leaves your infrastructure. We deploy within your environment, not ours. No code is stored, transmitted, or used for training.
How is this different from Copilot or Cursor?
Code completion tools help an individual write faster. SMPL builds persistent understanding of your system, evaluates work before engineers touch it, investigates root causes, and can extend that understanding into execution.
What size team is this for?
Best fit is teams of roughly 10 to 100 engineers, where codebase complexity, backlog pressure, and knowledge concentration are already slowing the organization down.
What if it doesn't work for us?
Productive in 7 days or your first month is free. We deploy, you evaluate, and you only pay if it delivers.
Stay in the loop.
Early access, case studies, and what we're learning about persistent codebase intelligence in real teams.
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Not ready to deploy? Start with a codebase intelligence report.
Get a free Codebase Intelligence Report: a practical view of your architecture, dependencies, and knowledge concentration before you commit to a larger engagement.
We'll be in touch within 48 hours.
Make your codebase legible, investigable, and less fragile.
Request Your Free Reportteam@smpl.io