01
The why is gone
The decision lives in a Slack thread from last spring or a doc nobody links to. The person who made it moved teams a year ago.
ENGINEERING MEMORY
Your engineering decisions, recalled on demand.
Why a thing was built this way, what was already tried, what the runbook says today. Recalled in any AI you use, with the source attached.
Asked from Cursor architecture recall
Engineering asks
Why did we move off the old search?
Latency on faceted queries blew past the SLA, so we moved to the new index in the Q1 platform review.
Source trail
VISIBLE
[1] Platform review · Q1 current
[2] GitHub PR · search migration cited
[3] Linear rollout · indexing scoped
The answer keeps the source trail attached.
SOUNDS FAMILIAR
The context exists. It just does not travel.
Architecture decisions, runbooks, rollout notes, and failed experiments all become useful again when the answer can return with the source attached.
02
New hires re-ask everything
Every onboarding repeats the same questions, because the answers were never recallable in the first place.
03
Re-explaining the stack daily
You paste the same architecture context into your AI assistant for the third time today, just to get a useful answer.
ASK FROM AI
Questions engineering should not answer from memory alone.
Why is MAX_PAGE_SIZE capped at 100?
What did we decide about the auth rewrite?
Why did we move off the old search?
What is the rollback process for payments?
Which database did we standardize on, and why?
WITH MEMORYCROW
The answer returns with source, version, and scope.
Ask in plain language
Ask why a thing is the way it is, and get the decision and the date, right inside the AI you already use.
Cited, so you can trust it
Every answer points to the decision it came from, so engineers verify instead of guessing.
Current, never stale
When a decision changes, save the newer version as current and keep the older decision as history with its source.
Asked from AI
Engineering
Team question
Why did we move off the old search?
Latency on faceted queries blew past the SLA, so we moved to the new index in the Q1 platform review.
Source trail
VISIBLE
[1]Platform review · Q1
CONNECTORS
Start with the tools engineering already trusts.
GitHub Slack Notion Linear Google Docs
FAQ
Engineering questions, answered.
The practical questions engineering leaders ask before they let AI recall architecture and operational memory.
Q1
Which AI tools does it work with?
MemoryCrow is built for MCP-compatible AI clients and workflows. The goal is one shared memory layer that can be recalled from the AI surfaces your team authorizes.
Q2
What should engineering teams save first?
Start with architecture decisions, runbook changes, incident learnings, rejected approaches, rollout notes, and the reasoning behind important PRs.
Q3
Does this replace our docs, repo, or issue tracker?
No. Those stay as the source of record. MemoryCrow stores the durable decision or operational memory and keeps the source trail close to the answer.
Q4
How does it handle decisions that changed?
Save the newer decision as current and leave the older memory as historical context. Engineers can see what changed instead of getting two competing answers.
Q5
Can private engineering notes stay private?
Yes. Save memories to personal, workspace, or group scopes depending on who should be able to recall them.
Start with one memory
Give every AI the same trusted memory.
Start the trial, connect one source, and ask the next question from Claude, ChatGPT, Cursor, or any MCP client. If MemoryCrow cannot prove it, it will not guess.
- 7-day trial
- Unlimited recall
- Permission-safe by default