Private beta — 12 engineering teams onboarding
Software Memory Infrastructure

Your codebase has a past.
Nobody can explain it.

Git stores what changed. Jira stores what was planned. AI tools forget everything between sessions. SWAPCORE stores why — the reasoning behind every decision, every AI prompt, every correction. Permanently. Queryable in seconds.

Free for up to 5 devs
Works with Cursor + Claude
Zero workflow change
cursor · medibook · swapcore active
Dev
Why does our Claude integration always stream? Can't I just do a regular messages.create() call?
SWAPCORE
Found 1 decision record from sprint 3. Injecting context into Cursor...
Cursor
Don't use blocking calls here. SWAPCORE has a decision record explaining this constraint:
Decision · HIGH · Sprint 3 Switched all Claude API calls to streaming SSE Why: Blocking calls caused 8–12s wait → 40% abandonment in user testing. Streaming drops perceived latency to <1s. All future Claude calls MUST stream or this problem returns.
The stream() approach is correct. This constraint came from real user data — not a preference.
Integrates with
The problem

Three tools.
Zero reasoning.

Every engineering team relies on Git, Jira, and AI coding tools. They all store what happened. Not one stores why decisions were made — and that gap costs you weeks every time a developer leaves, a bug surfaces, or an AI tool repeats a mistake you already solved.

G
Git
Stores
What lines of code changed
Missing
Why this approach was chosen over the three alternatives the team debated
J
Jira
Stores
What was planned and tracked
Missing
Why the risk was deferred, why the ticket was rejected, why this constraint exists
AI
AI coding tools
Stores
What it generated this session
Missing
Everything from every previous session — every prompt, correction, rejected suggestion
What none of them store
The WHY

SWAPCORE stores the reasoning. Every architectural decision, every deferred risk, every AI prompt — with a mandatory why field enforced at the database level. Never optional. Never inferred and discarded.

How SWAPCORE thinks

Every decision is part
of a chain.

SWAPCORE builds a causal graph automatically. Every architectural choice links to the code it produced, the incidents it caused, and the AI prompts it influenced. Ask anything. Trace everything. In seconds.

01
🏛️
Architectural Decision
Stripe chosen — idempotency keys
02
💻
Code Commit
Payment processor migration
03
Risk Flag
Retry logic deferred
04
🔥
Incident
PAY-287 — 3 double charges
05
🤖
AI Prompt
Dev asks Claude — context injected
06
Fix + Learning
SHA256 idempotency — Claude knew
Why this chain matters
Six months later, a new developer asks "why do we use idempotency keys?" SWAPCORE returns the full chain in 3 seconds — Stripe migration → deferred risk → PAY-287 → fix. Zero documentation was ever written. The memory is the documentation.
What this prevents
A new developer removing the idempotency keys because they "look unnecessary." An AI tool suggesting a simpler implementation without knowing about PAY-287. A code review that misses the connection between a change and a 2-year-old architectural decision.
Why SWAPCORE

Three capabilities
nobody else has.

Mem0 stores user preferences. Cognee builds document graphs. Letta manages agent state. None store engineering reasoning. None map where AI fails on your codebase.

01 / 03
The mandatory why_text field

Every record has a why_text field enforced at the database level — NOT NULL, no exceptions. If the system cannot infer it from your commit message and PR, it asks. This single field is what separates SWAPCORE from every other tool. Mem0 stores what you told it. SWAPCORE stores why you built what you built.

Enforced at DB level — never optional
02 / 03
The causal decision graph

SWAPCORE builds a linked graph automatically: architectural choice → implementation → incident → fix → AI prompt → correction. This graph cannot be reconstructed retroactively. It must be built in real time. A competitor starting fresh cannot replicate 90 days of your decision history. After 90 days, the switching cost becomes informational — the strongest moat possible.

Cannot be rebuilt retroactively
03 / 03
The AI failure map

Every time a developer corrects an AI suggestion, SWAPCORE captures why. After 6 months, it has a precise map of exactly where Claude and Cursor fail for your specific codebase. "AI suggestions for authentication middleware are corrected 60% of the time — reason: missing context about our custom auth wrapper." No competitor can replicate this from the outside.

Specific to your codebase — compounds daily
Real-world example

Building MediBook —
AI medical booking with Claude

A solo developer builds a HIPAA-relevant medical appointment platform using claude-sonnet-4-5. Four weeks. Watch SWAPCORE memory compound — preventing compliance violations Cursor had no way to catch without memory of prior sessions.

App being built
MediBook — AI-assisted appointment platform
Patients describe symptoms in natural language. Claude AI triages severity, recommends specialist, and books appointments. Multi-tenant — each hospital is a strictly isolated org.
Next.js 14claude-sonnet-4-5SupabaseStripeVercelSWAPCORE
SWAPCORE captures automatically
DecisionsAI promptsRisk flagsManual correctionsAI deviations
The result
A new developer gets a complete onboarding brief — including HIPAA constraints, Claude streaming decisions, and Stripe webhook patterns — in under 3 seconds. Zero documentation written.
W1
Multi-tenant isolation — RLS pattern established
Week 1 · Monday
Dev
Building a multi-tenant medical platform. Each hospital is a separate org. What's the safest way to isolate patient data in Supabase?
Cursor
Use Supabase Row Level Security — auth.uid() IN (SELECT user_id FROM org_members WHERE org_id = records.org_id). For medical data this is critical — a policy miss means Hospital A can read Hospital B's patient records. HIPAA violation.
Dev
Implements RLS. Records two decisions at session end (90 seconds total):
DECISION · HIGH confidenceSupabase RLS for multi-tenant patient data isolation.RLS at DB level — a missed policy is a HIPAA violation. This pattern MUST apply to EVERY new table without exception.
RISK_FLAG · deferredNo audit log for data access.Deferred for MVP (2 pilot hospitals). Must add before any regulated hospital onboards.
2 decisions recorded1 risk flaggedPROMPT_SESSION auto-captured
W2
Claude AI triage — cross-hospital leak prevented
Week 2 · Thursday
Dev
Adding Claude AI symptom triage. Patient describes symptoms, Claude returns severity level and specialist recommendation. Need to log each triage for records.
SWAPCORE
Week 1 RLS isolation pattern found. Injecting into Cursor context automatically.
Cursor
With SWAPCORE context: The triage_logs table MUST use the RLS policy from Week 1. Without it, Hospital A's query returns Hospital B's patient data — a HIPAA violation. Also: Claude's triage output contains patient symptom text. Store it encrypted, not plaintext.

SWAPCORE prevented a cross-hospital data leak Cursor could not have caught without memory of Week 1.
1 decision (encrypted triage logs)risk still open
W3
Stripe billing — open risk surfaced and closed
Week 3 · Monday
Dev
Adding Stripe subscription billing. Monthly plan per hospital. Need webhook to update subscription status.
SWAPCORE
3 records injected: RLS isolation pattern · encrypted triage logs · open risk flag (no audit log).
Cursor
Stripe webhook must use Supabase service_role key — RLS blocks server-side writes with no auth.uid() context. Also — you flagged "no audit log" as a risk in Week 1. The billing webhook is the right moment to start one: log every subscription change with org_id, event_type, timestamp. That closes your open risk.
1 decisionopen risk closed ✓
W4
Priya joins — full brief in 20 minutes, zero docs written
Week 4 · New engineer
Priya
I just joined MediBook. Give me a full brief before I build the appointment reminders feature.
SWAPCORE
Onboarding brief across 24 records — assembled in 190ms.
Cursor
MediBook brief for Priya:

Critical — RLS isolation: Every table uses create_org_rls_policy(). Skip it on appointment_reminders and patients from Hospital A receive Hospital B's reminders — HIPAA violation.

Claude AI triage: All patient symptom text stored encrypted. Reminder content from Claude should also be encrypted at rest.

Stripe webhooks: service_role key only — RLS blocks standard keys from server processes.
24 records · 4 weeks · zero docs written20-min onboarding ✓
24
Memory records
6
Decisions saved
2
Violations prevented
20min
New dev onboarding
The data moat

After 90 days, the switching cost
becomes insurmountable.

The causal decision graph cannot be reconstructed retroactively. Every day you run SWAPCORE, the graph deepens. A competitor cannot replicate 90 days of your team's engineering reasoning by acquiring a general-purpose memory API.

01
The decision graph compounds
Every architectural choice links to the commits it produced, the incidents it caused, and the AI prompts that referenced it. This graph is built in real time. It cannot be faked, imported, or reconstructed from your existing data.
02
The AI failure map is unique to you
After 6 months of MANUAL_CORRECTION records, SWAPCORE knows exactly where Claude and Cursor fail for your codebase. This is not a general insight — it is specific to your team. No competitor can see it, replicate it, or sell it.
03
The switching cost is informational
Financial switching costs can be overcome. Informational switching costs cannot. Abandoning SWAPCORE means losing the causal graph, the AI failure map, and the risk history actively preventing incidents today.
How the moat builds — the 90-day curve
Day 1
First decisions captured
Git, Jira, MCP connected. First why_text records written automatically.
Week 3
Causal chain forms
Decisions link to commits. Incidents link to risk flags. The graph begins.
Month 2
AI failure patterns emerge
MANUAL_CORRECTION records reveal where Claude gets it wrong on your stack.
Month 3
Moat is real
The graph cannot be reconstructed. Switching cost is now informational.
Competitive landscape

Built for a gap
nobody else fills.

Mem0, Cognee, Letta, and Supermemory are excellent tools for their use cases. None of them store engineering reasoning. None build a causal decision graph. None map where AI fails on your codebase.

Capability SWAPCORE Mem0CogneeLettaSupermemory
Stores engineering WHY✓ Mandatory
Causal decision graph✓ Auto-built◑ Entity graph only
AI failure map✓ Auto-captured
Primary userEngineering teamApp developersApp developersAgent buildersIndividuals
Team-level compounding✓ Core value✗ Per user✗ Per agent✗ Per agent✗ Per user
Dev tool integrations✓ 40+ adapters◑ SDK only◑ Limited◑ Limited◑ Some
MCP for Cursor / Claude✓ Auto-capture◑ Plugin◑ Plugin◑ Plugin
Data moat compounds✓ Daily
◑ = partial  ·  ✗ = not available  ·  ✓ = core capability
The SWAPCORE platform

Start with memory.
Unlock delivery.

SWAPCORE is the institutional memory layer you install today. MemFlow is the autonomous delivery suite built on that memory — where memory flows between every agent — coming 2026.

Available now — free tier
SWAPCORE

The institutional knowledge graph for software teams. Installs in 10 minutes. Zero workflow change. Connects to your existing tools and captures the why behind every engineering decision — permanently.

  • Mandatory why_text on every record — enforced at database level
  • Causal decision graph built automatically from your tools
  • MCP integration for Cursor and Claude — auto-captures every AI prompt
  • Architect Agent — plain English queries with source citations
  • AI failure map — where Claude and Cursor get it wrong on your codebase
  • 20-minute onboarding brief for every new developer
  • 40+ tool integrations — GitHub, Jira, Linear, Slack and more
Install free — 10 minutes
enables →
Coming 2026
MemFlow

The autonomous delivery suite where memory flows between every agent. Six agents — PM, Dev, Security, QA, DevOps, Architect — all informed by SWAPCORE's institutional memory. No agent ever starts from zero.

  • PM Agent — requirement to stories, memory-informed decomposition
  • Dev Agent — ticket to PR, cannot repeat rejected patterns
  • Security, QA, DevOps — agents informed by incident history
  • Every agent reads from and writes back to SWAPCORE memory
  • Requires SWAPCORE — memory is the foundation
Join the waitlist
The founders

Built by engineers
who felt the pain.

SS
Chief Executive Officer
Shashank Sonawane
Product vision and go-to-market. Deep experience in B2B developer tools with a sharp focus on the institutional knowledge problem that costs engineering teams weeks of productivity every year.
GA
Chief Technology Officer
Gaurav Ashtikar
Platform architecture and AI systems. Designed the four-layer memory system, the causal graph engine, and the tool-agnostic adapter architecture so no customer changes their workflow to get value.
KS
Chief Financial Officer
Kishor Shelke
Financial strategy and investor relations. Structures the land-and-expand GTM motion — SWAPCORE free tier as the Trojan horse, MemFlow as the expansion once the data moat is established.
Get started

Stop losing
your why.

Every week without SWAPCORE is a week of engineering reasoning lost forever. The causal graph cannot be reconstructed retroactively. Start building it today — free, 10 minutes, zero workflow change.

Free for up to 5 developers · No credit card required · Installs in 10 minutes
Coming 2026
MemFlow — the autonomous delivery suite where memory flows between every agent. Built on SWAPCORE. Every agent informed by your institutional memory from day one. Join the MemFlow waitlist →
HTMLEOF wc -l /mnt/user-data/outputs/swapcore.html && wc -c /mnt/user-data/outputs/swapcore.html