Private codebases for frontier AI
Production Code.Decision Context.
We acquire private production codebases and the operational artifacts around them — tickets, PRDs, architecture docs, support threads, postmortems. Not just what was built, but why.
Shutting down or sunsetting a product?
Your code is more valuable than you think.
The systems your team spent years building — the COBOL mainframe, the internal platform, the monolith that ran the business — didn't stop being valuable just because the company moved on. AI labs are paying for exactly this kind of code: real, battle-tested, full of the engineering judgment that only comes from shipping under real constraints.
Defunct companies
Your startup shut down but the code still exists. We turn years of engineering into a payout.
Legacy systems
Migrating off a legacy stack? The old system has training value that the new one never will.
What We Acquire
The Entire Software Engineering Process
A codebase alone is a snapshot. We acquire the full development lifecycle — the plans that shaped it, the collaboration that refined it, and the operational context that kept it running. This is how AI learns to build software the way enterprises actually do.
Planning & design
PRDs, RFCs, architecture decision records, design documents. The reasoning before the first line of code.
Collaboration & review
Code reviews, PR discussions, technical debates, team threads. How engineers negotiate trade-offs in practice.
Production codebases
Complete repositories with full commit history, branching patterns, and engineering decisions baked into every refactor.
Build & deploy systems
CI/CD pipelines, build scripts, deployment configurations, monitoring setups. How code gets to production.
Test suites & QA
Unit, integration, and end-to-end tests. The verification layer that defines what "correct" means.
Operations & incidents
Postmortems, runbooks, incident timelines, on-call rotations. What happens after deployment when things go wrong.
Backlog
Evaluate message queue options
PLAT-112
Draft API versioning RFC
PLAT-108
Rotate production secrets
SEC-155
Audit third-party SDK licenses
PLAT-120
Runbook for payment failover
OPS-44
Dependency vulnerability scan
SEC-160
To Do
PCI scope review
SEC-142
Ledger service RFC
PLAT-89
Webhook retry backoff
PAY-312
Idempotency key migration
PAY-315
In Progress
Adyen integration
PAY-301
Reconciliation service
PAY-305
Circuit breaker middleware
PLAT-95
Done
Stripe load test
PAY-298
Cost analysis memo
PAY-295
Gateway interface spec
PAY-290
Vendor evaluation matrix
PAY-287
Throughput benchmark
PAY-283
Discovery & audit
Vendor evaluation
Architecture RFC
RFC approved
Adyen integration
Stripe consumer path
Reconciliation svc
Ledger service
PCI compliance
Data migration
Integration testing
Load testing
Security review
Go-live
Rollout & monitoring
Runbook & handoff
src/payments/gateway.ts — Lines 47-58
reviewer on gateway.ts:52
This retry logic needs a circuit breaker — we hit cascading failures last quarter without one.
Suggestion:
+ const breaker = new CircuitBreaker(gateway, { threshold: 5 });
author replied
Good call. Added exponential backoff with jitter + breaker at 5 consecutive failures. Also pulled the threshold into config so ops can tune it without a deploy.
#payments-migration · 4 members
Sarah C.
10:02 AM
PCI scope review came back cleaner than expected. Adyen processing can live in an isolated VPC with no shared state.
Marcus R.
10:15 AM
That simplifies things. Here's what the reconciliation flow looks like:
async function reconcile(stripeSettlement, adyenSettlement) {
const combined = mergeByReference(stripeSettlement, adyenSettlement);
const variance = computeVariance(combined, generalLedger);
if (variance.amount > THRESHOLD) {
await escalate(variance, { channel: '#payments-ops' });
}
return { settled: combined.length, variance };
}Jun L.
10:23 AM
Should we handle the case where Adyen settlement is delayed? Last time that happened we had phantom variance alerts for 6 hours.
2 replies
Sarah C.
10:31 AM
Yes — adding a settlement window buffer. If Adyen hasn't settled within 4 hours of expected time, we mark it pending instead of flagging variance.
ACCT-PROC.cbl
COBOL
IDENTIFICATION DIVISION.
PROGRAM-ID. ACCT-PROC.
DATA DIVISION.
WORKING-STORAGE SECTION.
01 WS-ACCOUNT-REC.
05 WS-ACCT-NUM PIC X(10).
05 WS-ACCT-NAME PIC X(30).
05 WS-ACCT-BAL PIC S9(9)V99.
05 WS-ACCT-TYPE PIC X(02).
88 SAVINGS VALUE 'SA'.
88 CHECKING VALUE 'CH'.
88 MONEY-MARKET VALUE 'MM'.
01 WS-TRANS-AMT PIC S9(9)V99.
01 WS-NEW-BAL PIC S9(9)V99.
PROCEDURE DIVISION.
MAIN-PARA.
PERFORM INIT-ACCT
PERFORM PROCESS-TRANSACTIONS
UNTIL WS-EOF = 'Y'
PERFORM CLOSE-FILES
STOP RUN.
PROCESS-TRANSACTIONS.
READ TRANS-FILE INTO WS-TRANS-REC
AT END MOVE 'Y' TO WS-EOF
NOT AT END
EVALUATE WS-TRANS-TYPE
WHEN 'CR' PERFORM CREDIT-ACCT
WHEN 'DB' PERFORM DEBIT-ACCT
WHEN 'XF' PERFORM TRANSFER-ACCT
END-EVALUATE
END-READ.Codebases
Beyond GitHub
The codebases AI labs need most aren't on the public internet. Legacy enterprise systems, niche domain-specific code, proprietary tooling — with real commit history, real engineering decisions, and real complexity. Not another JavaScript todo app.
- Decades of accumulated logicCode spanning the 1970s through today. Multiple generations of architecture layered together — the kind of complexity AI needs to learn to navigate.
- Underrepresented domainsHealthcare systems, industrial control, financial infrastructure, telecom — verticals where AI training data barely exists.
- Full operational contextNot just source files — commit history, build systems, test suites, deployment scripts, and the engineering decisions that shaped them.
Infrastructure Code
The Code That Runs The Infrastructure
Behind every production system is infrastructure code that defines how it's deployed, scaled, and maintained. Terraform modules, Kubernetes manifests, CI/CD pipelines, Helm charts — the operational DNA of real infrastructure that AI has barely seen.
- Production-grade patternsNot toy examples — real multi-environment setups with networking, security groups, IAM policies, and monitoring. The patterns that take years to develop.
- Evolution over timeInfrastructure code with commit history showing how systems evolved — migrations, scaling decisions, incident-driven changes, cost optimizations.
- Cross-tool complexityReal deployments span Terraform, Kubernetes, CI/CD, and monitoring. We source the full stack, not isolated snippets.
Infrastructure as Code
Configuration Management
Container & Orchestration
CI/CD Pipelines
infra/production/main.tf
Terraform
resource "aws_vpc" "production" {
cidr_block = var.vpc_cidr
enable_dns_hostnames = true
enable_dns_support = true
tags = merge(local.common_tags, {
Name = "${var.env}-vpc"
Environment = var.env
ManagedBy = "terraform"
})
}
resource "aws_subnet" "private" {
count = length(var.availability_zones)
vpc_id = aws_vpc.production.id
cidr_block = cidrsubnet(var.vpc_cidr, 4, count.index)
availability_zone = var.availability_zones[count.index]
tags = merge(local.common_tags, {
Name = "${var.env}-private-${count.index}"
Tier = "private"
})
}
module "eks_cluster" {
source = "./modules/eks"
version = "~> 3.0"
cluster_name = "${var.env}-platform"
vpc_id = aws_vpc.production.id
subnet_ids = aws_subnet.private[*].id
instance_types = var.node_instance_types
desired_size = var.node_desired_count
max_size = var.node_max_count
enable_irsa = true
cluster_version = "1.28"
}Anonymization
The How, Not The Who
The point of enterprise artifacts for AI training isn't who made the decisions — it's how they made them. Our anonymization pipeline strips personally identifiable information while preserving the operational knowledge, decision points, and process patterns that make the data valuable.
Raw Artifact
Anonymization Pipeline
Anonymized Output
What stays, what goes
Names, emails, company identifiers, and proprietary details are replaced with consistent anonymous tokens. The business logic, financial structures, decision rationale, and process flows remain intact — exactly what AI needs to learn how enterprises work.
Private data. Verified environments.
Production-ready agents.
Tell us what you need — we will scope availability, anonymization, and pricing.