scalable AI agent memory
Manage business context at scale—without feeding agents more noise.
siift creates an evidence-weighted business memory that elevates current, high-confidence information & filters stale or irrelevant context before it reaches people and AI agents.
You: Give the pricing agent what it needs.
siift: Using 9 validated pricing signals and the active ICP. Early survey guesses and superseded segments were withheld.
↗ 9
- Active ICPvalidated · 11 signalshigh weight
- Pricing evidencecurrent · 9 signalscurrent
- Launch assumptionssuperseded · kept in historysuppressed
task-ready context7 records included · 23 filtered
- 01
evidence-weighted business memory
- 02
automatic relevance filtering
- 03
one context layer for many agents
more memory can mean less signal
Bigger memory can create a bigger context problem.
Chats, documents and agent logs accumulate faster than anyone can maintain them. Without a way to distinguish current evidence from old assumptions, every new workflow inherits more contradictions, duplicated facts and irrelevant history. siift scales memory by managing influence—not merely storage—so the most credible context rises and everything else stops competing for attention.
Scalable memory is not infinite storage. It is disciplined weighting, filtering & reuse.
how it works
Let context grow without letting noise take over
Capture the business once, update what it believes as evidence changes and give each workflow only the context it can use.
- 01
Capture context as structure
siift organizes assumptions, decisions, evidence, feedback and results around the business areas they affect, replacing scattered conversational memory with a visible system.
- 02
Reweight as the business learns
Validated signals strengthen some context while contradictory feedback weakens or supersedes other claims. History remains available without dominating current work.
- 03
Serve the right context on demand
The executive filter selects the smallest useful context set for the agent, person or task—reducing manual prompting, repeated documentation and bloated retrieval.
memory designed to scale
Keep the business current across every agentic workflow.
siift separates storage, truth and relevance so business memory becomes more useful—not merely larger—over time.
- 01 / weight
Dynamic confidence weighting
Increase or reduce the influence of context as validation, traction, decisions and feedback change what the business can reasonably trust.
- 02 / time
Current vs. historical context
Preserve why the strategy changed while preventing superseded assumptions from quietly shaping new advice, plans or automated work.
- 03 / filter
Task-aware executive filter
Prepare the relevant slice of business context for each intent instead of sending every agent the same oversized master prompt.
- 04 / see
Visible context maps
Find, inspect and reuse organized business intelligence without digging through chatbot histories, shared drives or undocumented agent memory.
- 05 / reuse
Multi-agent memory layer
Let research, product, marketing and operations agents work from one underlying business model while receiving different task-relevant views.
- 06 / share
Shared, scoped business context
Make the same source of truth available across the team while keeping each collaborator and workflow focused on what it actually needs.
why siift
Scale agentic work—not the context burden.
A larger context window can hold more text. It cannot decide which business claims are current, credible or relevant. siift manages that layer first.
basic AI memoryMore chats, files and embeddings accumulate indefinitely
with siiftBusiness context is organized around decisions and dependencies
basic AI memoryEarly guesses and validated evidence appear equally credible
with siiftConfidence and evidence change how much each claim influences work
basic AI memorySimilarity search returns text that sounds related
with siiftIntent, stage, evidence and dependencies shape what gets included
basic AI memoryPeople revise documents, prompts and agent instructions by hand
with siiftConnected context reweights when the underlying business changes
basic AI memoryEvery added agent creates another version of the business
with siiftMany agents reuse one coherent memory through scoped views
when to use it
For businesses whose context cannot fit in one conversation
- 01long-running work
Build across months without starting over
Carry current strategy, evidence and decision history into future work while suppressing the context that no longer deserves influence.
- 02agent orchestration
Coordinate specialist agents coherently
Give research, product, sales and operations agents the right slice of one business instead of letting each reconstruct its own reality.
- 03growing complexity
Add products, markets & people without context chaos
Keep granular business areas connected as the company expands, so useful memory scales beyond one founder or one agent thread.
frequently asked
Scalable AI agent memory, answered
What growing businesses should know before storing more information for more agents.
What is scalable AI agent memory?
Scalable AI agent memory preserves useful information across tasks and time without forcing every agent to process everything the system has stored. siift adds business structure, evidence weighting and task-aware filtering so memory can grow while the context supplied to each workflow stays focused.
How is siift different from a vector database or basic RAG system?
Vector retrieval usually finds text based on similarity. siift also models what the information means to the business, how confident it is, whether it has been superseded and which decisions or tasks depend on it. Those layers help determine influence—not just retrieval.
How does dynamic context reweighting work?
New evidence is connected to the assumptions and business areas it affects. Strong validation can raise confidence; contradictory results can weaken or supersede an earlier claim. Future agent context reflects the updated weight while the history behind the change remains visible.
How does scalable memory prevent context rot?
siift separates current knowledge from stale or historical material, lowers the influence of superseded assumptions and filters for task relevance. That reduces the chance that a growing context set makes later AI work less focused or internally inconsistent.
Does siift delete old business information?
Not simply because it becomes less relevant. Older context can remain in history with lower influence, preserving decision rationale and learning while keeping it from competing equally with current evidence.
How does scalable context support team alignment?
People and agents can work from the same underlying business model instead of private chat histories and separate prompts. Alignment is a downstream benefit; the primary function is maintaining current, reusable and appropriately filtered context at scale.
let the business learn once
Give every agent less context—and more signal.
Build scalable business memory that stays current, visible & ready for the work at hand.