A private knowledge layer for enterprise engineering
Your AI coding tools often don't make your eng teams faster.
Neocortex builds a knowledge layer from your own engineering history, so the tools your engineers already use produce code that matches your architecture and survives review. Measured on your sprint data, in five weeks.
Private to your org · Security-reviewed deployment · Fixed $30K pilot
Design partners and advisors from
Why teams stall
Every engineer has an AI assistant. Sprint velocity hasn't moved.
Adoption is high, throughput is flat. Generic assistants don't know your systems, your standards, or your legacy code - and in financial services, compliance rules break their output before it reaches review.
- 01
Output dies in review
Suggestions ignore your patterns, so senior engineers rewrite them.
- 02
No number to report up
Per-seat licenses, no measurable change in throughput or cycle time.
- 03
Blind to your systems
Legacy cores, internal libraries and compliance rules are invisible to generic models.
What we do
A knowledge system built from how your teams actually ship.
Your engineering organization has years of solved problems on record. We turn that record into a knowledge layer your AI agents run on - so their output matches your architecture, your standards, your compliance rules.
- 1
Learn from your history
Your Git, PRs and tickets are analyzed under your access controls and governance.
- 2
Build the knowledge layer
Your architecture, conventions and review rules become structured knowledge - private to you.
- 3
Your agents level up
Copilot, Cursor, Claude Code plug in unchanged and start shipping code that passes review.
What changes
Same tools. Different output.
- Generic patternsYour architecture
- Rewritten in reviewMerged as written
- Individual autocompleteTeam-level throughput
- AnecdotesA baselined number
Who it's for
Built for large engineering orgs where correctness is expensive.
Fintech & regulated
Decade-old core systems, audit trails and controls that generic assistants break.
30+ engineers
Enough history to learn from and a board asking what AI actually returned.
Tools already rolled out
Copilot or Cursor is deployed. Adoption is high. Velocity is flat.
The pilot
Five weeks to a number your board will believe.
Week 0
Baseline locked from your last 6 sprints - your Git, your Jira.
Weeks 1–2
We install and tune your knowledge layer. One of our engineers sits with your pod.
Weeks 3–4
Your pod runs it on live sprints, independently.
Week 5
Measured against baseline: +40% throughput, or the pilot fails.
$30K
Fixed fee, one pod
5 wks
Baseline to result
+40%
Throughput or it fails
Fixed price. Private to your org, security-reviewed deployment. Success defined before we start.
How we baseline and measure it: our guide to engineering productivity metrics.
Book a pilot call
FAQ
The questions buyers ask first.
- How is our code handled?
- The knowledge layer is private to you and never used to train shared models. Deployment is scoped to what your security team approves - we walk through the exact architecture and data flows during the pilot scoping call.
- Do we replace Copilot or Cursor?
- No. You keep the tools your engineers already use. Neocortex is the knowledge layer underneath them.
- How is +40% measured?
- From your own Git and Jira: merged throughput and cycle time across the pilot pod, against a 6-sprint baseline locked before we start.
- What do we need to commit?
- One pod, five weeks and read access to your repos and tickets. No added headcount.
Prove it on one pod. Expand from there.
The fastest way to find out what AI can actually do for your engineering org.
Book a pilot call