Questions.

Timer is the organizational memory layer: a durable record of what a company decided, what it promised and why, captured where the work happens. People ask it in plain language, and the AI systems working for that company read the same record.

Organizations lose knowledge every day. Important decisions disappear, context becomes fragmented, departments become disconnected, and artificial intelligence repeatedly starts from zero because organizational memory does not exist as infrastructure. Timer solves this problem.

Large language models have reached extraordinary reasoning capabilities. The limiting factor is no longer intelligence - it is memory. Organizations now realize AI cannot become operational without persistent organizational context.

Organizational Memory. We believe it will become recognized enterprise infrastructure, alongside databases, cloud infrastructure and identity management.

Organizations accumulate knowledge over decades. Without memory, every employee and every AI system repeatedly reconstructs information that already exists. Memory compounds. Productivity compounds. Competitive advantage compounds.

No.AI assistants become one consumer of Organizational Memory. Timer provides the memory itself.

Yes.Timer searches everything the company keeps, in one place: documents and files, email, meetings and their transcripts, customers, people, projects, tasks and decisions. Type a few keywords or ask a question in plain words, and every answer comes with its source and its date. Pallas, Timer's AI, uses the same search before it answers or drafts anything.

Yes.Timer has a built-in CRM for customers, people, companies and deals. Because it sits on the company's memory, every customer record already carries its emails, meetings, documents and decisions, and all of it is searchable.

Timer does what a knowledge base does, and fills itself. Everything the company knows is kept and searchable in one place, from documents and email to meetings and decisions, without anyone writing articles or filing pages. Search by keyword or ask a question, and every answer shows where it came from.

No.Timer is not a productivity application: productivity applications improve work, while Timer changes how organizational knowledge itself is preserved and reused.

No.Timer makes enterprise software more intelligent by connecting it through shared organizational memory.

Timer is organizational memory an organization can be accountable for: every fact with its source and both of its clocks, every action inside a boundary you set, on the record. Most of this category builds memory for agents and competes on how cheaply a machine can read it.

Not by asking people to write it down. Documentation drives, handover templates and exit interviews all fail the same way: they ask someone to articulate, in their final weeks, context they accumulated over years and no longer notice they hold. Institutional knowledge is captured reliably only when capture is a property of the system rather than a task assigned to a person, so that decisions, reasoning and context are recorded as work happens and remain reachable afterwards.

Treat it as an architecture problem rather than a human resources problem. A departure only destroys knowledge that existed in exactly one place. When decisions, commitments and their reasoning accumulate in organizational memory that the organization owns, a resignation removes a colleague rather than a capability, and the successor inherits the context instead of reconstructing it.

Institutional knowledge is what an organization collectively understands, held mostly in people. Organizational Memory is that same understanding held as infrastructure the organization owns. The first is a condition an organization happens to be in and loses gradually; the second is a layer it can build, govern and keep.

No.Its assumptions are. Knowledge management assumed that people would stop working to record what they knew, that the record would then be searched deliberately, and that documents were the unit of knowledge. Each assumption has failed in practice. What replaces it is not a better repository but a different layer: memory that accumulates automatically as work happens and is consumed by people and AI systems alike, without anyone being asked to file anything.

AI removes the search problem, not the memory problem. A model can summarize any document it is given, which makes retrieval feel solved, but it cannot supply the reasoning behind a decision that nobody recorded. AI raises the value of organizational memory rather than removing the need for it: without memory, every AI system in the organization starts from zero, however capable it is.

No, not in the sense organizations mean. A model holds two things: parameters fixed when training ended, and whatever text fits in its context window right now. The window is working attention, not memory: it is re-supplied on every request, it is paid for on every request, and it is discarded when the session ends. Anything the organization needs the model to know tomorrow has to live somewhere the model can be given it from.

No.A larger window lets a model consider more at once; it does not let an organization accumulate anything. The context is still rebuilt from scratch on every request and still disappears at the end of the session, so the cost rises with the window while the amnesia stays exactly the same. Memory is a question of architecture, not of window size.

Individually, yes; organizationally, no. Agent frameworks give each agent its own store of past interactions and retrieved facts. An organization running a dozen agents therefore ends up with a dozen partial, unreconciled and ungoverned memories, none of which it controls or can move. That is fragmentation presented as a memory solution. The alternative is one governed memory that every agent reads from and writes to.

Retrieval augmented generation is a technique for finding relevant text and placing it in a model's context window at query time. It answers the question of what to retrieve. Organizational Memory answers the prior question of what the organization knows and how that record is accumulated, governed and owned. Retrieval is one way to read from memory; it is not the memory, and an organization that has only retrieval still has no record of why anything was decided.

Only if something outside the model holds the record. Individual products increasingly remember within their own boundaries, which is why the same organization can have an AI system that knows a customer in one tool and has never heard of them in the next. Continuity across tools requires a memory layer beneath them that each system reads from and writes to, owned by the organization rather than by any one vendor.

In practice it is shared between risk, legal, data and engineering, and that division is usually where it fails. Each function owns a policy, a control or a system, and none owns the record of what actually happened. Governance holds when one accountable owner is responsible for the evidence that oversight occurred, not only for the policy stating that it should.

Because the policy is easy and the evidence is not. Writing that a human reviews high-impact recommendations takes an afternoon. Proving that a human did review one, saw what the system saw, and either agreed or overrode it, requires the recommendation, the context, the reviewer and the outcome to be recorded in a single chain as they happen. Most architectures scatter those four things across four systems, and the chain cannot be reassembled afterwards at any price.

A log records transactions: what the system did, and when. An AI audit trail records judgment: what was recommended, on what evidence, who reviewed it, what they changed, and what resulted. Regulated organizations are increasingly asked to evidence the second, and discover that they have only ever built the first.

For three separate reasons, and they do not stand or fall together. Legally, oversight that cannot be evidenced does not count as oversight. Operationally, an organization that cannot reconstruct the chain from recommendation to outcome cannot learn from its own decisions. Commercially, trust in an AI system tracks the ability to explain a specific decision after the fact, not general confidence in the model.

The Human Layer Framework is a five-paper research series proposing Organizational Memory as a new enterprise infrastructure layer connecting human organizations with artificial intelligence.

Publishing first establishes the architectural ideas independently from the product. Research creates intellectual credibility; the product provides commercial validation.

Yes.Timer is built on the Human Layer Framework, five open-access papers published under permanent DOIs between February and June 2026.

Subscriptions: monthly plans for individuals and teams, and enterprise agreements for whole organizations.

Companies of 50 people or more, where what the company knows is spread across people and tools: finance, legal and professional services, education and accelerators, manufacturing, real estate, logistics.

Enterprise complexity creates the strongest demand for Organizational Memory. Infrastructure naturally begins where complexity is highest.

Because Organizational Memory is enterprise infrastructure. Quality of deployment matters more than volume during the early stages.

Day one. Timer reads the history already present in the tools you connect, so the memory is populated before anyone changes how they work. Useful answers inside thirty days, not inside a quarter.

No.It works backwards over what already exists in the tools you connect, which is why it can answer about things that happened before you had it.

No.Timer reads the tools your team already uses. There is no migration, no tagging and no taxonomy to maintain.

They index documents so you can find them again. Timer reads the same documents and answers why one exists, what was decided in it, who approved that, and how much weight it still carries. Retrieval stops at the file. Memory starts there.

No.Timer reads the tools you already use, so nothing has to move on day one. When you do want to move, Timer Stack is a full native layer to work in: documents, sheets, forms, signatures, flows, projects, messages and tasks, all on the same memory. Keep your stack, move onto ours, or run both.

Every fact carries the period it was true in the world, and facts are ranked by the authority of their source, so a signed document outranks something said in a thread. When two facts collide they are arbitrated rather than averaged: one is served as current, the others are marked superseded, and the answer still tells you the point was contested. Every change is recorded, so the memory can be read as it stood on any past date.

Backfill is deliberately unhurried. Current activity is fetched ahead of history, each run works to a time budget and a per-account pace rather than the provider's ceiling, and how far back to reach and how often to run are configurable per connection. A connection that starts failing retries on a widening interval up to a cap, and Timer tells you when one needs reconnecting rather than quietly pausing your data.

Four steps. You take the Human Layer Score, which anyone can do today without an account. You tell us what your organization keeps having to remember. We read it ourselves and talk with you personally. Then we bring in one workflow first, and your memory grows from there in stages, never all at once.

Initially for better operations: better visibility, better AI, less repeated work. Eventually for organizational continuity. The initial pain point sells the product; Organizational Memory keeps customers.

Because memory compounds. Every month the organization becomes smarter. Leaving means abandoning accumulated organizational intelligence.

Timer's commercial launch began in January 2026, growing from one paying organization, onboarded personally, one at a time.

Timer is commercially available, with paying enterprise organizations and growing recurring revenue. Every organization is onboarded personally, because quality of deployment matters more than volume during the early stages.

Because Organizational Memory is enterprise infrastructure. Quality of deployment matters more than volume during the early stages, and every organization is onboarded personally.

Several categories overlap with part of what Timer does. Enterprise search finds documents but not the reasoning inside them. Knowledge bases hold what somebody remembered to write down. Model vendors are adding memory that serves their model, inside their ecosystem. Other companies are now building memory infrastructure directly, which we read as evidence that the category is real. Timer holds decisions rather than documents, keeps when each fact was true and what replaced it, acts on them with a named person approving, and leaves ownership with the organization.

Microsoft owns productivity. Timer builds Organizational Memory. These are different architectural responsibilities: large platforms optimize their own ecosystems, while Timer is the memory layer beneath all of them.

OpenAI builds models. Timer builds memory. Models reason; organizations remember. Organizations should own their institutional knowledge independently of any model provider - models change, memory persists.

Large incumbents optimize existing product categories. Timer builds the memory layer beneath them. If Organizational Memory succeeds, incumbents are more likely to integrate, partner or acquire than to rebuild it themselves.