Find people with your problem

Most monitoring starts from a name you already own. That works once people are saying it. Before then — a product nobody has heard of, a name you picked last week — the feed is empty and the tool looks broken. This guide builds the other kind of query: one that starts from what people say when they have the problem you solve, and reads back only the mentions worth answering. Nothing here is a separate product. It is POST /v1/queries with different terms, and GET /v1/mentions with two filters.

1. Search for the sentence, not the name

A brand query looks for one token. A discovery query looks for the phrases people type when they are stuck:

{
  "name": "Brand monitoring pain",
  "globalCriteria": {
    "market": "global",
    "terms": [
      "how do I track brand mentions",
      "alternative to Brand24",
      "tired of manually searching reddit"
    ],
    "match": "contains"
  }
}

terms are OR’d into one upstream call, so several phrasings of the same complaint cost one poll, not three. match: "contains" rather than "word": these are phrases, and people do not type them the way you wrote them.

Write the phrases in the words your customers use, not the words you use. “Brand monitoring” is your category; “I keep missing when people mention us” is their problem.

2. Poll the places people describe problems in prose

Five sources carry that kind of sentence. Enable them together:

{
  "sources": [
    { "source": "reddit", "enabled": true },
    { "source": "hackernews", "enabled": true },
    { "source": "stackoverflow", "enabled": true },
    { "source": "lemmy", "enabled": true },
    { "source": "x", "enabled": true }
  ]
}

Each enabled source is one watch slot and one charge per poll — see Credits.

3. Switch the readings on

A phrase query catches everyone who used the phrase, and most of them are not prospects: someone answering, someone quoting, someone complaining about a different tool. Two readings sort them, and they are what step 4 filters on.

{
  "sentimentEnabled": true,
  "aiEnabled": true
}

sentimentEnabled reads every mention as positive, neutral, negative, question or mixed. aiEnabled also writes the intent — what the item is trying to do: purchase_intent, comparison, question, complaint, praise or other. Both cost credits per classified item. Without them, step 4 has nothing to filter on.

4. Read back only what needs you

GET /v1/mentions filters on both readings. Each is repeatable, and repeated values are OR’d:

curl "https://api.nephia.cc/v1/mentions?intent=complaint&intent=question&sentiment=negative" \
  -H "Authorization: Bearer $NEPHIA_API_KEY"

That reads: someone with a problem or a question, who is unhappy about it.

Ask	Query string
People describing a pain	`?intent=complaint`
People asking how to do it	`?intent=question`
People weighing tools	`?intent=comparison&intent=purchase_intent`
The unhappy ones, whatever they are doing	`?sentiment=negative`
What nothing has read yet	`?sentiment=unread`
Only the two forums that matter to you	`?source=reddit&source=hackernews`
Only what people actually reacted to	`?engagement_min=10`
A hundred likes on X, fifty score on Reddit	`?engagement=x:likes>=100&engagement=reddit:score>50`

Repeated values of one key are OR’d; different keys are AND’d. So ?source=reddit&source=hackernews&sentiment=negative&intent=complaint reads: on Reddit or Hacker News, negative, and a complaint. source, sentiment and intent are all repeatable. unread is the wire spelling of “not yet classified” — a real filter value, not an absence. It is how you find what your readings have not caught up with.

Filtering on sentiment, intent, engagement_min or engagement reads the polled stream only. Items kept from a one-off Explore run carry no readings and no counters, so they cannot satisfy those filters and are left out rather than shown misleadingly.

Cutting the mentions nobody reacted to

engagement_min keeps mentions with at least that many interactions — likes, replies, reposts, comments or score, whichever the Source reports. It is the noise gate for a busy Source: on a typical month of X mentions, roughly seven in ten have no interactions at all, so ?engagement_min=1 alone removes most of what you would otherwise page through. Three things it does not do, each of which will otherwise surprise you:

It never counts views. A view is not an interaction, and on the Sources that report one it is an order of magnitude bigger than every other counter — including it would make this a filter on reach.
It leaves out mentions with no counters at all, rather than treating them as zero. RSS items and AI answers report no audience, and mentions recorded before 4 September 2026 predate the field. None of them were measured, so none can satisfy a threshold. ?engagement_min=0 is therefore narrower than no filter: it asks for everything that was counted.
The numbers are captured when we collect a mention, and never refreshed. A post caught two minutes after it went up is measured with the counters it had then. So a threshold reads against your most recent mentions, which are the ones with the least time to have earned anything.

A number that means one thing on one Source means another elsewhere: five points on Hacker News is a story that landed, five likes on X is nothing. That is what engagement below is for.

Asking a different question of each Source

engagement_min thresholds one number for everything, and the sum it thresholds is lossy: a post with a hundred replies and no likes clears engagement_min=100 exactly as a post with a hundred likes does, and those are not the same mentions. It was liked and it was argued with is the distinction a sum erases. engagement is a rule, and it is repeatable:

<source|*>:<metric><operator><number>

curl -G https://api.nephia.cc/v1/mentions \
  -H "Authorization: Bearer $NEPHIA_API_KEY" \
  --data-urlencode "engagement=x:likes>=100" \
  --data-urlencode "engagement=reddit:score>50"

Metrics are likes, replies, reposts, comments, score and views, plus total for the same interaction sum engagement_min reads. engagement_min=10 is exactly engagement=*:total>=10.
Operators are >=, >, =, < and <=. The number is whole and may be negative — Reddit and Lemmy net downvotes out, so ?engagement=reddit:score<0 is a real question: the mentions that were argued with.
* stands for any Source no named rule already covers. Pair it with total — ?engagement=*:total>=25 is “at least 25 interactions, wherever it was said”, which is engagement_min with an operator of your choosing. A named counter under * is legal but rarely what you mean: *:likes>=100 drops every Source that does not count likes, by the rule below.

Two rules about the scope, and both are the kind you discover by noticing rows missing:

A Source no rule names passes. ?engagement=x:likes>=100 narrows X and leaves Hacker News exactly as it was. Without that, every rule would also be a Source filter in disguise — and ?source= already exists for that. A named rule overrides * for its own Source, and several rules on one Source are ANDed.
A metric that was never counted satisfies nothing — < included. This is the engagement_min rule above, one level down and sharper: YouTube reports no likes at all, so ?engagement=youtube:likes<10 returns none of your YouTube mentions rather than all of them. Only a metric the Source actually reports can be filtered on.

Send engagement or engagement_min, never both: they are two spellings of one filter, and a request carrying both is a 400. Every mention comes back with both readings on it, so you can sort further without a second call:

{
  "id": "…",
  "source": "reddit",
  "title": "Tired of manually searching for our name every morning",
  "sentiment": "negative",
  "intent": "complaint",
  "url": "https://reddit.com/…",
  "occurredAt": "2026-09-01T08:12:00.000Z"
}

5. Qualify each one with your own question

The readings say what an item is. To ask whether it is your problem, give the query an agent step — one instruction and a schema, applied to every item it catches:

{
  "aiStep": {
    "instruction": "Decide whether the person behind this item is describing the problem your product solves, and how badly it is hurting them. Judge only from what the item itself says.",
    "schema": {
      "problem_fit": { "type": "boolean" },
      "urgency": { "type": "enum", "values": ["low", "med", "high"] },
      "current_workaround": { "type": "string", "maxLength": 120 }
    }
  }
}

The answers ride on each mention under agent, spelled with your own field names:

{
  "agent": {
    "problem_fit": true,
    "urgency": "high",
    "current_workaround": "a saved search and a calendar reminder"
  }
}

current_workaround is the one with commercial value beyond triage: what someone names as their current answer is what your reply has to be better than. See AI for the schema’s field types and what an agent pass costs.

6. Get told instead of polling

Do not poll /v1/mentions on a timer. A query delivers what it catches to a webhook, and every mention arrives with its readings already on it — so the filter that reads well in step 4 becomes a condition in your own handler.

What this does not do

It finds people describing a problem. It does not find customers, and it does not tell you they want to buy — a problem_fit of true is a reading of one post, not a qualified lead. Treat the output as a list of conversations worth joining, and expect to discard some of it.

Guides

Sources

Integrations

API Reference

Find people with your problem

1. Search for the sentence, not the name

2. Poll the places people describe problems in prose

3. Switch the readings on

4. Read back only what needs you

Cutting the mentions nobody reacted to

Asking a different question of each Source

5. Qualify each one with your own question

6. Get told instead of polling

What this does not do

​1. Search for the sentence, not the name

​2. Poll the places people describe problems in prose

​3. Switch the readings on

​4. Read back only what needs you

​Cutting the mentions nobody reacted to

​Asking a different question of each Source

​5. Qualify each one with your own question

​6. Get told instead of polling

​What this does not do

1. Search for the sentence, not the name

2. Poll the places people describe problems in prose

3. Switch the readings on

4. Read back only what needs you

Cutting the mentions nobody reacted to

Asking a different question of each Source

5. Qualify each one with your own question

6. Get told instead of polling

What this does not do