Guides ·
Company memory explained: how AI answers from your own records
Company memory is a single, searchable body of your company’s own records, such as emails, documents and conversations, that an AI assistant uses to answer questions. Instead of answering from general knowledge, the AI first finds the relevant records and then answers from them, and in Seesa every answer shows its sources. The result is an assistant that can tell you what was agreed, what changed and what is still open, from your own information and with the access each person already has.
The problem it solves
Most of what a company knows is scattered. The conversation is in email, the detail is in a document and the decision from last month is in a chat thread. A general-purpose AI assistant can write fluently, but it knows nothing about any of that unless someone pastes it in.
Company memory closes that gap. Seesa connects to the places that hold your company’s data and builds one memory from them, so a question such as “why is the invoice still unpaid?” or “what did we agree?” can be answered from the relevant records.
How an answer is built
The exact machinery varies between products, but the general pattern is the same. First, your records are brought in and organised so they can be searched by meaning as well as by keyword. Seesa’s pricing page refers to this when it sizes storage: it counts the source data and the embeddings, which are the searchable representations built from that data.
Then, when someone asks a question, the system finds the records that look relevant. The model reads those records and writes an answer from them. Because the answer is built from identifiable records, it can point back to them. In Seesa, every answer shows its sources, so you can follow the records behind it and check before relying on a brief or a recommendation.
Where the records come from
Seesa connects to supported systems and brings in your history. Gmail and Microsoft 365 mail and calendar connections are available, as are Google Drive, Google Sheets and Slack. For a CRM, ERP or specialist record system, the connection or import route is assessed before you order.
A logo is not a guarantee. Seesa confirms the connection or import route, readable content, available actions, permission requirements and deployment status for each source. Standard setup includes the historical import and supported connections, and your team can add and manage supported connections in settings afterwards.
Why permissions matter
A memory that anyone can search is a problem. If it holds the finance files, the person asking about a launch should not be able to read them just because they are in the same memory.
In Seesa, every answer respects who is asking: the same memory gives different views. Each person’s access governs what their Seesa can use, and sharing an answer does not have to mean sharing the records behind it. Where another person’s protected information is needed, they can review the answer they share with you.
What it makes possible
Once the records are in one place, Seesa can do more than answer questions. It can gather the context for a meeting, an update or a follow-up and prepare a draft for review. Your feed brings useful work together, ready for your review. Your team decides what changes and what happens next.
- Before an account meeting: what has changed on this account since the last one, with a brief drawn from the latest conversations.
- Before a decision: what moved on the launch, and what is still waiting.
- During customer follow-up: what was promised, and what remains open.
A worked example
Seesa’s film follows a fictional outdoor clothing company, Tarnhollow. The sales lead, Hannah, learns that a launch will slip unless an air-freight slot is booked by noon the next day. She asks what it would cost, and Seesa answers from the company’s own documents in seconds, with its sources: eighteen and a half thousand pounds, all in.
The money is the finance director’s call, so Seesa prepares his answer from the cash position and the bank facility. He checks it and releases it. Hannah sees the answer, never the finance files. Afterwards, it is all in the memory for next time. The example is fictional, but it shows the three ideas together: an answer from records, with sources, under per-person access.
What it does not do
Company memory improves the starting point, not the judgement. AI-generated outputs can be wrong and should be reviewed before being relied on. That is why showing sources matters: they let a person check the answer against the record.
Nor does a memory replace professional responsibility. In regulated work such as property, accountancy and insurance broking, Seesa prepares information for the people responsible; they remain responsible for advice, deadlines and decisions.
Where the memory lives
In a cloud AI service, the memory would sit in the provider’s infrastructure. In Seesa, models, company memory and agent work run on your premises, and the company memory is stored on your installation. Connections to your existing systems still allow Seesa to work with the sources you authorise.
To see what the memory looks like in use, start with the features page, which shows how answers, groundwork and routines fit together.
Questions, answered.
What is company memory?
One memory built from the records your company holds, such as email, documents and conversations, which Seesa uses to answer questions. Every answer shows its sources.
Can everyone see everything in it?
No. Each person’s access governs what their Seesa can use, and the same memory gives different views to different people.
Can the answers be wrong?
AI-generated outputs can be wrong and should be reviewed before being relied on. Seesa shows the sources behind an answer so you can check them.