How Market-Making Analysts Organize Funding, Unlock, and Community Signals Before a Meeting

Signals considered

The funding announcement and investor information can be cross-checked
A specific TGE, token launch, or unlock date appears
The unlock schedule may create concentrated sell pressure
Reviewable discussion exists around community activity and industry reputation

At 2:40 on Wednesday afternoon, an analyst at a market-making firm closes his laptop and prepares for the weekly meeting. Before it begins, he needs to give the team one conclusion: are the two market-making candidates added this week worth moving into due diligence?

The conclusion should be supported by data: the project’s funding, unlock schedule, community authenticity, and industry reputation. In practice, that information is scattered across thousands of messages in more than ten Telegram groups. He remembers that he “saw it somewhere,” but cannot name the source.

The meeting begins at 2:50. Ten minutes. He has only ten minutes.

This is normal in market making: the window is measured in weeks, and a wrong decision is extremely expensive—taking a bad project costs real money and industry reputation—yet the information required for the decision is buried in unorganized group conversations. He used to rely on memory and luck. If he remembered, he could give a reasoned answer. If he did not, all he could say was, “Let me check.”

Today is different. Instead of opening the group list, he opens the TOP Prospect workspace. The prepared records place the two candidates’ relevant discussions, sources, times, and context together. He reviews them in eight minutes and writes two conclusions in the meeting notes: one project enters due diligence; the other pauses until the token economics are verified.

Two minutes later, he walks into the meeting with a judgment whose sources he can explain.

This article follows one person: an analyst at a market-making firm and what changed after he began using TOP. It shows what the product organizes, ranks, and preserves—and what it never does for him. Judgment, due diligence, and approval remain human work.

NOTICE: The team, group messages, and business conditions in this article are composite illustrations used to demonstrate the product workflow. They do not represent a real customer, conversation, contract, revenue result, or conversion outcome.

Before: Three Problems That Made Him Nervous Before Meetings

Problem one: all the information is in groups, but nobody organizes it.

A market maker evaluates many things: whether the funding is real, who the investors are, whether the unlock schedule creates sell pressure immediately after launch, whether the community is genuinely active or filled with bots, and whether anyone in the industry has discussed the project privately. These facts are scattered across discussions in more than ten groups, and nobody organizes them for the team. Before the meeting, he answers “is this project worth market making?” with statements such as “I think I saw a funding announcement somewhere” or “someone in one group seemed to criticize the token economics.” If he remembers, he can support the answer. If he does not, he can only say, “Let me check.”

Problem two: a wrong judgment costs money and reputation.

A market maker is not an ordinary sales organization. Taking a bad project puts its own inventory and market reputation at risk, and everyone in the industry can see which projects it served. He cannot guess when deciding whether a project deserves due diligence. But refusing to guess is different from having organized information to review. He wants to verify the facts, but verification means thousands of messages, and he cannot reread every one.

Problem three: the window is measured in weeks, and a missed window is gone.

Market-making demand appears two to four weeks before TGE. Projects do not wait while he researches slowly. If his judgment takes too long, they are already speaking with another market maker. He often finishes the research only to discover that the project has signed elsewhere. The issue is not insufficient care. He is careful too slowly.

Together, these problems mean: he wants to make a serious judgment, but the information is unorganized, time is short, and the window does not wait.

After: Three Things the Product Does for Him

First: Separate Project-Discussion Groups From Peer Groups So the Information Sources Are Right

He reorganizes the monitoring sources. TOP Prospect only processes groups he actively connects and is authorized to access. Groups where projects gather—Web3 project discussions, funding communities, and unlock or token conversations—become information sources. Market-maker peer groups become market observation, used for industry context but excluded from the project-evaluation queue.

The result: important information used to disappear inside peer conversation; now only five or six information sources remain, and every item deserves more attention. Funding, unlock, and community developments that affect a market-making decision appear in the project ecosystem, not in peer groups.

The corresponding product capability is monitoring-source management. You choose the groups; the system does not join groups for you. Information sources and market observation remain separate, and peer groups do not enter the evaluation queue.

Second: Discussions About One Candidate Become an Evidence-Backed Record

The “what do we know about this project?” picture he once assembled from memory now looks like this in the system:

Field	Content
Business category	Potential market-making project (evaluation)
Related discussion	Funding announcement / unlock schedule / community activity / industry reputation, each with original text and source
Source	Multiple connected groups · sender IDs
Time	Original timestamp for each message (UTC+8)
AI score	A 0–100 ranking score and “High Priority / Important / General” label calculated from factors such as the base score, signal strength, importance, number of matched keywords, recency, and repeated mentions
Rationale	Related discussions about the project are grouped together, with the source and time attached to every item
Status	Pending Evaluation

This record solves his first problem. “I think I saw this somewhere” becomes “I saw it in these groups and these messages, and the source and time are here.” He can follow the links to the original messages instead of reconstructing the picture from memory. The score only tells him which project to review first. He still makes the judgment, but he finally has organized material to review.

The corresponding product capability is extraction rules + lead organization. You define what counts as a project signal worth attention in your business language, such as funding, unlocks, TGE timing, or community growth. Keywords perform coarse retrieval, AI assesses intent, and related discussions about the same project are organized together. The product organizes and ranks; a person returns to the original messages to judge the facts.

Third: Give Every Candidate a Status So Evaluation Progress No Longer Depends on Memory

He now assigns each candidate a status: New Lead → Pending Follow-up → Followed Up → Converted (terminal), or Invalid (reversible). When he marks one invalid, he adds a short reason—“funding doubtful,” “unlock creates immediate sell pressure,” “bot community,” or “signed elsewhere.”

At month-end, those invalid reasons become the most valuable data. The team can immediately see which project signals eventually proved empty and should be excluded, and which signals later reached contracting and deserve more weight. He adds doubtful funding and unreasonable unlocks to the evaluation penalties, while giving more weight to the combination of recognized investors, a reasonable unlock schedule, and a real community. The following month’s candidates look more like projects he would take into a meeting.

The corresponding product capability is status flow. The team updates status manually. The product does not read direct messages and does not know whether a contract was signed; it records the status selected by the team. Who performs due diligence and how the committee decides remain human choices.

One Complete Path: From Two Messages to Two Conclusions in the Meeting

Follow his actual handling process from beginning to end.

On Monday, two messages appear in two different groups:

Group A: “XX Project completed a $5 million funding round with XYZ among the investors. The official team says the token launches in Q1.” Group B: “XX Token unlocks 30% next month. Will the sell pressure be too high? Has anyone market-made this project?”

System assessment: Both groups match “funding / token launch / unlock / market making,” and both point to the same project name. The system organizes the related discussion around the project: the funding announcement from Group A, the unlock discussion from Group B, community activity, and industry reputation, each with source and time. Semantic assessment identifies a real funding development and a genuine sell-pressure risk from the unlock, making the project worth evaluation.

He opens the record: The original text, time, and source from both groups are present, with links back to the messages. He spends ten minutes reviewing the project’s related discussions and reaches two preliminary judgments: the funding is real—the investor can be verified—but the unlock schedule creates sell-pressure risk through a concentrated 30% release.

Before the Wednesday meeting, he writes two conclusions in the system:

Project one (verified funding + reasonable unlock): mark Pending Follow-up and recommend due diligence
Project two (doubtful funding + immediate unlock pressure): mark Pending Verification and recommend checking the token economics first

Two minutes later, he walks into the meeting and says:

“I recommend due diligence for Project One: $5 million in funding, investor XYZ is verifiable, and the unlock schedule is reasonable. Pause Project Two: 30% unlocks in one release, creating high sell pressure. I will finish verifying the token economics next week and return with a conclusion.”

Every judgment has a source. When his manager asks how the funding was confirmed, he answers directly: the funding announcement in Group A plus the investor’s website, both recorded with provenance. That is a completely different meeting experience from saying, “Let me check.”

Two weeks later, Project One enters formal due diligence and the status changes to Converted—not signed, but accepted into the team’s evaluation process. Verification confirms a real token-economics issue with Project Two, so it is marked Invalid with the reason recorded.

During review, he records one rule: The combination of recognized institutional funding, a reasonable unlock schedule, and a real community almost always indicates a project worth evaluating for market making. Concentrated unlocks plus doubtful funding receive an immediate penalty. The unlock schedule is a harder judgment input than community size because it directly affects whether market making can be profitable.

Return to the Question: What Did the Product Actually Do for Him?

He did not change industries, teams, or standards. He only changed where he spent his time:

	Before	After
Finding information	Reconstructs “I think I saw this somewhere” from memory	Reviews organized records with sources and timestamps
Meetings	When he cannot answer, he says, “Let me check”	Every conclusion has provenance and survives questions
Catching the window	Finishes research after the project signs elsewhere	Enters evaluation when the project first leaves traces
Result	Wants to judge seriously, but the information is unorganized and time is short	The same analyst gives an evidence-backed conclusion in ten minutes

But the product never does three things for him:

It does not decide whether the project is good. The AI score only ranks. It determines which project appears first; whether the project deserves market making can only be decided after his due diligence.
It does not contact the project. The product does not read direct messages or send messages automatically. Contact remains his work.
It does not approve the project. He changes the status manually. The product does not know what the market-making committee ultimately decides.

The product organizes and ranks. Judgment, due diligence, and approval remain human work. That organization changes him from someone who answers from memory into someone who answers from records. He no longer has to feel uncertain before the meeting.

Ten Minutes Before the Meeting, His Manager Asked, “Is This Project Worth Market Making?” He Used to Answer From Memory. Now He Answers From Records