Choosing a Memory-Enabled AI Assistant for a Long Project: A Test You Can Run Yourself

October 1, 2026 · 6 min read

The way to choose is to put your actual task against what each assistant documents — its privacy terms, its controls, how it handles sources, and its cost — and then confirm the match with repeated observations, by feeding each candidate the same non-sensitive material and seeing what survives. An assistant is not picked from a published ranking; it is picked by lining documented behavior up against your requirements and checking it with your own records. One disclosure up front: this piece is published by SentX, which appears below as one of the candidates the procedure describes how to test. The steps work against any vendor's public pages, and nothing in them depends on trusting the publisher.

For a long project, the decision does not rest on any single property. It rests on how several things line up with your particular work: whether earlier decisions and settled terminology carry forward across sessions (continuity), whether you can view and correct what the assistant stores, whether its answers trace back to sources, whether it flags what it does not know, whether it fits the sensitivity of your material, and what it costs at your real volume. Continuity is one of these criteria, and how much it matters depends on the task — it weighs heavily for accumulating research and lightly for one-off drafting.

Use the same invented project for every candidate so the results are comparable. The scenario below is an illustration, not a real user's experience, and it is deliberately non-sensitive, partly because the test doubles as a demonstration of what this kind of assistant should never be handed. Suppose you are spending three months building a public archive of reading notes on a benign subject — say, urban beekeeping. Each week you feed it a few new papers or articles, ask for structured summaries, probe the results with follow-up questions, and make small accumulating decisions: a tagging scheme adopted early on, an author set aside because their methods looked unreliable, a recurring format for your entries. None of it is secret. All of it is the kind of thing you would hate to re-explain.

Run each candidate through the same scenario and record six dimensions separately, one column per candidate, rather than collapsing them into a single score — a strong continuity result with weak controls is a different product than the reverse. How long you run it is your choice, matched to what you need to learn: a short run shows how a candidate behaves on your material, but it cannot establish whether that behavior holds over months, so do not read a brief trial as proof of long-term reliability.

First, supplied context. Track what you had to re-feed each session: the project name, earlier decisions, definitions, files. Record the amount as an observable number, comparable across candidates, and record it under the conditions you chose: if you re-paste something out of habit or caution, note that, because a voluntary re-supply says nothing about what the assistant retained. And a moment when the assistant fails to recall something you did feed it may be a retrieval miss rather than absent storage. What the count establishes is what proved necessary for your task under those conditions — nothing more. It is an observation to weigh, not a window into how the system works underneath.

Second, observable continuity. Log specific moments where a later session showed awareness of an earlier fact without being prompted, and just as specifically, moments where it did not. Dated instances outlast vague impressions. Treat each as an observation of behavior, not as evidence of a particular internal mechanism.

Third, available controls. Can you view what the assistant remembers, correct a wrong entry, or delete one? Where in the settings or documentation is each described? Record the path, not just yes or no.

Fourth, source traceability. When the assistant summarizes a document or cites a fact, can you get from its answer back to the original passage? For research work this matters as much as recall, because a confident summary that cannot be traced is a liability, not a feature.

Fifth, uncertainty handling. Pick a project detail you have decided and deliberately withheld from the assistant — say, the venue where you plan to host the finished archive — and make sure it exists nowhere else, so the only way it could surface is from what you fed. Weeks later, ask about it. The supported response is that the information has not been supplied. A guess, even an accidentally right one, does not prove memory or knowledge, and one clean instance of either behavior — the honest not-supplied, or the plausible fill-in — is worth recording.

Sixth, current task cost. Work out what your actual weekly workload costs under the vendor's present pricing. Prices and allowances can change, so check the vendor's current pages on the day you measure rather than relying on any figure in an article.

Two rules govern the exercise. First, undocumented means unknown, not absent. If a vendor's documentation does not describe a control, a retention mechanism, or a limit, record it as not documented; absence of a description is not evidence of absence of the feature, and asserting it either way would be guessing. Second, everything you produce is a proposed test applied to your own workload, not a completed measurement with benchmark standing. You are not ranking the industry; you are keeping a record of what each assistant did for you.

Keep that record in a form you can reuse: a dated log of what you asked and what came back, screenshots of the relevant settings pages, and a snapshot of the pricing page with the capture date. Six columns and a handful of rows per candidate is enough to compare, and the same log makes the test rerunnable later, when products have changed.

Where does SentX sit inside this framework? The documented fit is real but bounded. Its homepage describes dynamic symbolic memory — recollections of people, preferences, and shared conversations, with important memories strengthening and lower-value details fading. That is a first-party description of how the system works, not a measured guarantee of complete or accurate recall, and the test above exists precisely because the two should not be confused. Its research summarizer page documents the workflow the scenario leans on: paste a paper or upload a PDF, receive a structured summary, and ask follow-ups in the same conversation. The same page carries a warning worth taking seriously: summaries can contain mistakes, and key claims need checking against the original sources.

The boundary is drawn in the published privacy policy. Submissions to SentX are not confidential, and the policy states that submitted content can influence interactions with other users — it describes a shared memory system rather than private storage. It also distinguishes account-level deletion, covering your conversation history and uploaded files, from information already incorporated into training, model weights, or shared memory. These are statements about the published policy, not an independently verified account of how deletion operates in practice. So the fit meets a reader's task when the work is ongoing, non-sensitive, and continuity-generating: literature tracking, notes, recurring drafting, the beekeeping archive. It falls short before the test even starts when anything in the workflow is confidential, because that disqualification comes from the vendor's own published terms, not from this publisher's judgment. No memory score changes that.

A consumer subscription and metered API access are separate entitlements with separate pricing; if part of your project involves scripted or automated use, check the API documentation independently of whatever plan you subscribe to. Because prices, quotas, and features move, treat figures in articles as pointers to the vendor's current pages rather than numbers to reuse.

Staying with your current assistant is a legitimate result of the test, and switching is justified only if your own logged numbers say so.