01
Retain what matters
Save reusable knowledge while skipping temporary, duplicate, or low-value details.
Predictive memory infrastructure for AI
AI learns continuously.
Its memory should too.
MemorySafe gives AI a decision layer for what to retain, protect, replay, and forget—before useful knowledge disappears or low-value memory takes over.
Agent private beta · waitlist open
Continual-learning pilots · conversations open
02
Keep control visible
Review, protect, and delete memories instead of treating storage as a black box.
03
Govern within limits
Estimate vulnerability, respect bounded memory, and record why each decision was made.
How the product is organized · two paths
Built for AI that needs continuity.
One path governs what an AI assistant remembers about people and work. The other governs replay memory while a model continues learning.
A
Private beta · waitlist open
MemorySafe for AI Agents
A local governed-memory layer designed to keep useful context reusable, make stored memories visible, and let the user review or delete them.
Join the beta list →CL
Pilot partnerships
MemorySafe for Continual Learning
A memory-governance policy designed to balance value, vulnerability, redundancy, and capacity when models learn from changing data.
Discuss a pilot →How MemorySafe works
Make memory a decision.
MemorySafe evaluates incoming memory before limited capacity is allocated, then makes the action and reason inspectable.
01
Assess
Consider value, estimated vulnerability, redundancy, and available capacity.
02
Apply policy
Decide what should be retained, protected, replayed, merged, or forgotten.
03
Record the reason
Keep governance actions visible and auditable instead of silently changing memory.
52-second founder overview
English audio · embedded captions
Product evidence
Built on measurable
memory performance.
MemorySafe combines visible agent-memory controls with a continual-learning policy evaluated across paired held-out benchmarks.
0.702
combined AUPRC · MemorySafe
0.763
rare-class recall · best of six
12.9 s
per seed · matched reservoir
20
paired held-out seeds · 52–71
Governance controls are visible.
The MemorySafe dashboard brings automatic selection, protected memories, duplicate prevention, local storage, and review and deletion controls into one visible system.
Strong rare-class retention at the same buffer.
Across 20 paired held-out seeds, MemorySafe reached 0.702 ±0.042 AUPRC versus 0.687 for reservoir, using the same 500-sample buffer.
View benchmark methodology
PneumoniaMNIST was evaluated as a five-task class-incremental stream. All six policies used the same 500-sample buffer, backbone, schedule, task order, and paired seeds 52–71; only sample selection varied. The 95% confidence interval for the AUPRC difference versus reservoir was −0.004 to +0.033, and the margin over MIR remained significant after Holm correction (p = 0.024). These research results establish the benchmark foundation; real-world pilots are the next validation stage.
Program memberships
Member of NVIDIA Inception, Google for Startups Cloud, and AWS Activate.
awsACTIVATE MEMBER
Priority pilot domains
Where forgetting
has consequences.
MemorySafe is designed for systems where rare, valuable knowledge must survive continuous change.
Medical AI
Rare clinical cases can be overwritten as models absorb new data.
Prioritize vulnerable, clinically important samples.
Fraud Detection
Emerging fraud patterns are rare before they become obvious.
Retain valuable anomalies as behaviour changes.
Edge AI
Strict storage and compute limits make every retained sample matter.
Allocate bounded memory intentionally.
Robotics
New environments can interfere with previously learned skills.
Protect fragile capabilities during adaptation.Benefit & ROI simulator
What could MemorySafe change for your system?
Explore how governed memory can affect time, replay volume, and operating value using your own assumptions.
Simple team example · change any number
Preferences, project details, or recurring instructions.
At 50%, 10 minutes of repeated explanation becomes about 5. Adjust the scenario to match your workflow.
Annual value scenario
Your inputs
Current repetition time / year
—
Time potentially returned / year
—
Illustrative annual value
—
Want to calculate ROI too?
Pricing is not final. ROI appears only when you provide your own estimate.
ROI using your estimate
Illustrative value minus estimated cost
Net annual value
—
Illustrative ROI
—
Simple payback
—
How this works
Team time today = people × weekly minutes × 52. Potential time returned applies the selected scenario. Illustrative value multiplies those returned hours by the hourly value.
Uses your inputs for planning. Actual results depend on workflow, setup, model costs, and answer quality.
Technical planning example · keep any value you do not know
This changes only the use-case wording—not the calculation.
A replay sample is one saved training example reviewed again later; 500 is an example.
Use dataset or storage information; 256 KB is an example.
Check training or deployment logs; 200 is an example.
At 30%, 500 becomes about 350. Adjust this assumption to explore the replay strategy that fits your system.
Annual replay scenario
Your inputs
Modeled replay data not processed / year
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Modeled replay runtime avoided / year
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Illustrative gross annual value
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ROI using your estimate
Gross value minus estimated investment
Net annual value
—
Illustrative ROI
—
Simple payback
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How this scenario is calculated
Samples not replayed = current buffer − modeled buffer. Annual data applies sample size, runs per day, and 365 days. Runtime assumes duration falls in direct proportion to buffer reduction.
Use this planning model to compare scenarios. Deployment results depend on workload, buffer policy, and model quality.
Local control
Your memory policy stays inspectable.
The governed-memory database is stored locally. MemorySafe is designed to make stored knowledge and governance actions reviewable rather than invisible.
L
Local database
Governed memory records are stored on the user’s device.
C
User controls
Review, protect, and delete memories from the product interface.
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Auditable reasons
Retain and evict actions can carry a visible reason.
Connected AI services may process selected context under their own terms. “Local” describes the MemorySafe governed-memory database, not every connected service.
Founder & CEO
Carla Centeno
Neuroscience-informed founder building memory infrastructure for AI, inspired by human memory and grounded in continual-learning research.
LinkedIn →Leadership & advisory
Built with operational and startup experience.
Financial discipline, company-building strategy, technical perspective, and commercial execution.
Head of Operations
Larissa Centeno
Finance leader bringing cost discipline, risk governance, and structured growth strategy.
LinkedIn →
Strategic Advisor
Ankit Mishra, MBA
Startup operator, AI strategist, and venture-capital professional with 13+ years of experience.
LinkedIn →
Sales Director
Grégoire Gervais-Vachon
Bilingual commercial leader connecting financial-services needs with governed AI-memory solutions.
LinkedIn →Choose your path
Give AI a policy for what it remembers.
Join the Agent beta or explore a continual-learning pilot built around your system and goals.