Git stores what changed. Jira stores what was planned. SWAPCORE stores why every decision was made — permanently, causally linked, queryable in 3 seconds.
The engineer who knows the answer left 6 months ago. The Slack thread is buried. The ADR was never written. Every AI coding tool starts from zero every session.
SWAPCORE is the layer that sits between all your existing tools and permanently captures the reasoning behind every decision, commit, risk, and deployment — automatically.
Four layers working in concert. Every event from every tool flows down through normalisation, enrichment, and storage — then back up as precision-assembled context for the Architect Agent.
SWAPCORE earns trust incrementally. Stage 1 only observes. Stage 2 acts with approval. Stage 3 operates end-to-end. Each stage is a complete product — not a beta.
The foundation. Passive capture of every commit, ticket, build, and decision from 8+ tools. Permanent memory with mandatory why_text. The Architect Agent answers any question about your codebase history in plain English — with source citations. Zero external writes. Zero workflow change.
The first agents that write to external tools. The PM Agent decomposes natural language requirements into Jira or Azure DevOps tickets. The Dev Agent takes a ticket and produces a pull request. Every external write goes through an unbypassable ApprovalGate — there is no code path that skips it.
The complete autonomous delivery platform. PR merge triggers the full pipeline: Security Agent scans, QA Agent validates, DevOps Agent deploys with auto-rollback. The integration marketplace lets third-party developers extend the tool universe. SWAPCORE becomes infrastructure.
SWAPCORE connects to your existing stack via webhooks, polling workers, SDK embed, MCP server, and REST API. No tool lock-in. Every integration is swappable at the adapter boundary.
Every stage of the software delivery lifecycle — from a developer's first question to a production deployment — flows through SWAPCORE's memory layer. Agents act at each stage, informed by permanent institutional memory.
Watch how SWAPCORE gives a solo developer using Cursor + Claude API persistent memory across 5 weeks of vibe coding sessions. No re-explaining the stack. No forgotten decisions. No duplicate bugs.
Without SWAPCORE, every session starts with re-explaining the stack, re-justifying past decisions, and risking contradictions across sessions.
With SWAPCORE, the developer records 2–3 decisions per session. Cursor fetches the full memory brief automatically. By week 4, the AI is generating code that is consistent with every architectural choice made in week 1.
→ Developer builds: Supabase auth + Supabase PostgreSQL, NextAuth.js, org-scoped RLS policies. Records decisions:
usage_events table with the same RLS pattern you established in Week 1 — auth.uid() IN (SELECT user_id FROM org_members...). Otherwise a billing query could leak cross-org usage data. Use anthropic.messages.stream() with Server-Sent Events on the Next.js API route...→ Cursor referenced Week 1 RLS pattern automatically. Saved 2 hours of debugging a cross-org data leak. Developer records:
org_subscriptions table using the Supabase service role key, not the anon key — because RLS will block it. You established RLS on all tables in Week 1. The webhook runs as a server process outside the user session, so it has no auth.uid() context. Service role bypasses RLS deliberately here. Also — this is a good time to add the Upstash rate limiting you flagged as a risk in Week 2...→ Cursor surfaced the RLS + service role issue AND the open risk flag from Week 2 without being asked. Both would have caused production bugs.
system_prompts table — apply the same RLS policy you established in Week 1: auth.uid() IN (SELECT user_id FROM org_members WHERE org_id = system_prompts.org_id). You have now used this pattern on 4 tables (users, usage_events, org_subscriptions, system_prompts). Worth extracting it into a reusable Supabase policy template so new tables get it automatically. Also — system prompts are injected before the user message, so they count against the token budget. The usage_events counter from Week 2 needs to include system prompt tokens in its calculation...→ Cursor noticed the RLS pattern applied 4 times and suggested a template. It also caught the token counting gap — system prompt tokens were being excluded from billing, which would have caused revenue leakage at scale.
create_org_rls_policy() function (added Week 4). Any new table you create must call this. If you skip it, org data becomes visible cross-tenant./api/generate. Token counting uses input_tokens + output_tokens from the final message response — includes system prompt tokens. Rate limited at 20 req/min per user via Upstash Redis.→ Rahul had full project context in 20 minutes. Every architectural decision from 5 weeks, the RLS pattern, the token counting fix, the service role rule — all surfaced automatically. Zero documentation was ever written.
SWAPCORE connects to Cursor, Claude, Windsurf, and any MCP-compatible AI tool via the Model Context Protocol. The AI fetches project memory automatically before every answer — without any developer action.
The SWAPCORE MCP server exposes 5 tools that Cursor's AI calls automatically. When you ask an architecture question, Cursor calls swapcore_get_context() before generating its response — invisibly, in under 200ms.
Every competitor is locked to one tool, one session, or one slice. SWAPCORE owns the tool-agnostic institutional memory layer across the full SDLC.
| Capability | SWAPCORE | GitHub Copilot | Atlassian AI | Devin / SWE | Datadog | Linear AI |
|---|---|---|---|---|---|---|
| Cross-session memory | ✓ Always | — Never | ◑ Jira only | — Never | — Never | — Never |
| Why decisions stored | ✓ Core feature | — None | — None | — None | — None | — None |
| Tool-agnostic | ✓ By design | ◑ GitHub only | ◑ Atlassian | ◑ Limited | ◑ Observ. | ◑ PM only |
| Causal debug chain | ✓ Full trace | — None | — None | — None | ◑ Metrics | — None |
| Onboarding brief | ✓ Automatic | — None | — None | — None | — None | — None |
| Full SDLC coverage | ✓ All stages | ◑ Code only | ◑ PM + code | ◑ Code only | ◑ Ops only | ◑ PM only |
| On-prem enterprise | ✓ Helm + k8s | — Cloud | ◑ Server | — Cloud | ✓ Yes | — Cloud |
| Data moat compounds | ✓ Daily | — Session | — None | — None | — None | — None |
| Autonomous agents | ✓ Stage 2+ | ◑ Code gen | ◑ Limited | ✓ Code | — None | ◑ PM only |
Priced on team size — not per seat. The value of institutional memory is organisational.
Three founders. Combined decades watching institutional knowledge walk out the door every time a senior engineer resigned.
The first team to deploy SWAPCORE builds a compounding memory advantage. Every week without it is a week of context lost forever.