recallFind the needle
Surface the one record that matters in years of account history — the promise made on a call last spring, buried under thousands of touchpoints.
memseekai memory infrastructure
Your records say $40k $25k.
Your records say $40k $25k.
Memory serves what's true now.
Not a database. Not a vector store.Not another RAG wrapper.
Memseek is memory for AI agents. It turns the records you already have — CRM, calls, email — into the context your agent actually needs: the few facts that matter right now, current, dated, and traceable to the source. Nothing is ever overwritten, so you can always see what changed and why.
the full history~101k tok
CRM · Feb
Budget approved: $40k
call notes · May
Budget cut to $25k this year
email · Jun
Wants SSO before they sign
note
Prefers email over calls
support
Billing complaint — resolved
marketing
Opened the newsletter
served to the agent
Budget is now $25k this year
cites call notes · May
Blocker: needs SSO before signing
cites email · Jun
Reach out by email, not calls
supersedes the old $40k
~101,000tokens served
Works with
CRM
Email & chat logs
Call notes & transcripts
Support tickets
Documents
Databases & vector stores
entry 01how it works
Three steps between your records and your agent.
01 · capture
Give it what happens
CRM notes, inbox, calls, tickets, docs — the raw stream of an account. Memseek takes it from there; your systems of record stay in charge.
02 · reconcile
It keeps the story straight
Every fact stays dated and cited. When new evidence contradicts the old — a budget cut, a new decision maker — the current truth wins and the history is kept.
03 · serve
Your agent gets a brief
At query time: the few records that matter, current and sourced, at ~30× fewer tokens than dumping the raw history into the prompt.
entry 02why it isn't rag
Retrieval finds what looks similar.
Memory knows what's true now.
Reliable memory is about context selection, not context volume. That's the part a vector search was never built to do.
plain RAG vector search
- ✕Returns chunks that look similar — no idea which is current.
- ✕The old $40k budget and the new $25k surface side by side; the model guesses.
- ✕Dumps the whole account history into the prompt to be safe.
- ✕Re-derives a different answer on every call.
- ✕Sources and dates fall off along the way.
memseek context harness
- ✓Selects the few records that matter for this question.
- ✓Knows what changed and what's superseded — serves the current truth.
- ✓Resolves conflicts across sources into one coherent picture.
- ✓Compact context — ~30× fewer tokens than the full history.
- ✓Every item stays dated and linked to its source.
entry 03what's in the kit
One layer. The jobs a vector store can't do.
Memseek isn't a single trick. The same layer recalls the right record, reasons across time, resolves contradictions, forgets on request, and shows its work — the parts real memory needs that search alone can't give you.
temporalKnow what changed
Track what was superseded and what still holds, so the agent quotes this year's budget — not last year's approved number.
time travelRewind to any date
Ask what the account looked like last quarter and get last quarter's answer. The past is never overwritten — it stays there to query.
conflictResolve disagreements
When the CRM and the latest call notes say different things, reconcile to the current truth instead of handing the model a contradiction.
cross-sourceConnect the evidence
Link related facts across the CRM, inbox, call notes, and tickets into one picture — multi-hop, not single-shot.
provenanceShow its work
Every fact it serves stays attached to a dated, verbatim source you can open and verify — a traceable claim, not a guess.
erasureForget on command
Delete a person or a source and every conclusion drawn from it goes too — right-to-be-forgotten that actually reaches the derived facts.
reviewPropose, then approve
The agent drafts memory updates; a human signs off before they go live. Nothing self-deploys behind your back.
entry 04watch the hard parts work
The intelligence, not the plumbing.
Distilling patterns, catching contradictions, and reasoning across time are the parts teams get wrong. Here's each one, live — on a real account.
digest
Distills signals into insight
Reads many small moments across calls, emails, and notes — and writes one higher-level, cited takeaway.
digest · cites 4 signals
"Loop in the CFO early — that's where this deal moves."
Deal stalled 3 weeks until the CFO joined the thread
CFO asked for the ROI summary directly
Fastest replies always came from finance
Discount approved same day the CFO was cc'd
conflict
Catches facts that collide
When new evidence contradicts the old, it's flagged — and the current, cited fact wins.
Signs off: Dana — VP OpsJan
Signs off: Priya — CFOMar
⚡ superseded — resolved
served nowPriya (CFO) approves the deal · cites call notes, Mar
temporal
Knows what held, and when
Ask what the account looked like last quarter — the history is preserved, not overwritten.
Jan
Apr
Jul
the account, as of then
Starter — 5 seats
entry 05public benchmarks
Better context. A fraction of the tokens.
These test exactly the failures agents hit today: finding the right thing in a long history, handling updates and contradictions, and connecting evidence across sources.
0%
recall & reasoning accuracy
LongMemEval-S · long-conversation memory
0%
picks the latest valid fact
LongMemEval-S · after facts change over time
+0 pts
exact match vs. dense retrieval
MuSiQue · multi-hop QA
+0 pts
exact match vs. dense retrieval
2WikiMultiHopQA · multi-hop QA
Context per question
≈ 32× fewer tokens
Full history
Memseek
On inexpensive, self-hostable models — no indexing-time LLM calls, no frontier-model crutch. A production pilot then measures correction rate and downstream task success on your data.
entry 06flexible outputs
Ask once. Get the shape you need.
The same query-time layer returns whatever your application is built to consume — no second pipeline.
context
Raw evidence
The relevant, current records with dates and sources attached.
[ {id, text, date, src},
… ]answer
Direct answer
A resolved answer to the question, with its citations.
"Budget is $25k this year" cites: [call-notes · May]
profile
Update-ready profile
A current, keyed view of the account you can drop into a prompt or store.
stage: negotiation blocker: needs SSO signs_off: Priya (CFO)
skill
Playbook digest
A distilled how-we-do-it for a recurring play — renewals, escalations, handoffs.
renewal_playbook: steps: […] pitfalls: […]
entry 07put it to work
Craft the right context for any agent.
Whatever your agent does, Memseek hands it the context it needs — current, cited, and compact. A few of the shapes teams ship:
- ›Sales & CS copilots — a pre-call brief: what changed, who signs off, open blockers.
- ›Personal assistants — a self-updating profile of each user's preferences and corrections.
- ›Support agents — one reconciled history across tickets, email, and calls.
- ›Autonomous agents — observe, reflect, and recall what matters right now.
agent.py
# connect to your memory workspace mem = Memseek.from_env() acme = mem.entity("account:acme") # ask for a current, cited briefing — not the raw history brief = await acme.brief( "what should I know before the Acme renewal?", ) brief.text # stage, budget, blocker, who signs off… brief.citations # [call-notes · May, email · Jun] brief.tokens 3200 # not 101,000
entry 08buy vs. build
The same reason you don't hand-roll your own Postgres.
A real context layer is substantial engineering most teams don't solve well. The question isn't whether you could build it — it's whether that's the best use of your team.
◐
Value on day one
Better context out of the box, faster than an internal build, on the records you already have — no months of research and evaluation.
◔
Reviewed like a migration
Your memory design is a few files your team can code-review and ship like a schema migration — and a bad change fails at deploy, not in production.
✓
You can see it working
It shows exactly what context was served, and every output traces back to the precise inputs that produced it. No black box.
entry 09straight answers
The questions teams actually ask.
Isn't this just RAG?+
No. RAG retrieves text that looks similar. Memseek selects the right, current context, resolves contradictions, keeps every source and date, and serves ~30× fewer tokens. On public benchmarks it beats standard dense retrieval by +7.5 to +10.8 points — and it does it without indexing-time LLM calls.
Does it replace my CRM?+
No. Your CRM stays the system of record. Memseek works alongside it — and email, call notes, and support tickets — to give your agent the current, reconciled picture that no single system holds on its own.
Is it a database? Does it replace my stack?+
No. Memseek isn't your system of record and it isn't a warehouse — your CRM, databases, and vector stores keep doing their jobs. It's the memory layer in front of your agent: a current, cited, compact picture of each account, instead of raw history stuffed into every prompt.
Does it need an expensive frontier model?+
No. The results above are on inexpensive, self-hostable models, with no indexing-time LLM calls. Performance and low cost — not one at the expense of the other.
What do I actually get back?+
Whatever shape your app consumes: raw context, a direct answer, an update-ready account profile, or a playbook digest — all from the same query-time layer.
Can I run it privately / on my own infra?+
Yes. Private and local deployment is a first-class path for teams where data control, latency, or infrastructure constraints matter.
Can I delete a customer's data?+
Yes — and it's thorough. Erase a person or a source and Memseek removes the conclusions derived from it too, following the trail of citations. That's right-to-be-forgotten that reaches the derived facts, not just the raw record.
What does it cost to run?+
Little. It runs on inexpensive, self-hostable models with no indexing-time LLM calls, and every reasoning step declares its token and time budget up front — so there are no runaway loops and no surprise bills.
How do I know it's actually helping?+
Evaluation is built in: it shows what context was served for every request and whether it improved the outcome, so a pilot can prove lift on correction rate and task success on your data.
entry 10get started
Turn your records into the memory your agent should use now.
Point it at the CRM, inbox, and call notes you already have and serve better context from the first integration. Bring your own model — cheap and self-hostable is the point.