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Sales, Marketing & Ad Tech

Brand Reputation Monitoring API

The complaint that becomes a problem next week is already being made somewhere this week, in words nobody searched for.

Social listening platforms
PR and communications software
Customer experience vendors
Agencies

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

Monitoring tools are good at volume and bad at meaning. They count mentions, run a sentiment classifier that struggles with sarcasm and domain language, and present a dashboard where a sharp rise in negative mentions is visible without any indication of what it is about. The communications team then spends the morning reading raw mentions to find out, which is the work the tool was supposed to do.

The genuinely useful signal is thematic and early. A specific complaint appears in a handful of posts — a billing change nobody explained, a shipment delay in one region, a confusing update to an app. At that stage it is twenty mentions in different words, invisible to keyword alerts and lost in aggregate sentiment. A week later it is a thousand mentions and a journalist asking for comment.

The constraints here are mostly about sources and people. Platform terms vary from permissive to explicitly forbidding automated collection, and what may be collected is often narrower than what may be redistributed. Mentions are also written by individuals, which makes this personal data processing in most jurisdictions. A defensible build runs on permitted sources, aggregates above a threshold, and resists the temptation to profile authors.

Who buys this

Social listening platforms, PR and communications software, Customer experience vendors, Agencies

Category: Sales, Marketing & Ad Tech

What the API returns

What comes back

Mention volume by source, with collection permissions documented per source
Sentiment with domain-adapted classification and an explicit uncertain class
Emerging theme detection: clusters that are small but growing unusually fast
Complaint taxonomy mapped to your own categories rather than a generic one
Topic trend series with change-point detection rather than daily deltas
Share of voice against a competitor set, from the same collection
Author-level aggregation only above a minimum cohort, with no individual profiles
Escalation signals: amplification rate, journalist and large-account involvement

Endpoints

Method	Path	Returns
GET	/v1/mentions	Mentions with sentiment, theme and source metadata
GET	/v1/themes/emerging	Clusters growing faster than their baseline
GET	/v1/themes/{id}/trend	Volume and sentiment trend for one theme
GET	/v1/share-of-voice	Relative volume against a competitor set
POST	/v1/taxonomy	Define or update your complaint and topic taxonomy
GET	/v1/escalations	Themes showing amplification characteristics

Request and response

illustrative

Request

{
  "brand": "Example Industries",
  "window": { "from": "2026-10-08", "to": "2026-10-22" },
  "sources": ["review_sites", "forums", "news", "permitted_social"],
  "include": ["emerging_themes", "sentiment", "escalations"],
  "min_cluster_size": 8
}

Response

{
  "brand": "Example Industries",
  "window": { "from": "2026-10-08", "to": "2026-10-22" },
  "volume": { "total": 4218, "change_vs_prior_window": 0.11 },
  "sentiment": { "positive": 0.41, "negative": 0.29, "neutral": 0.22, "uncertain": 0.08 },
  "emerging_themes": [
    {
      "theme_id": "thm_0912",
      "label": "Unexplained change to invoicing cadence",
      "mentions": 34,
      "growth_vs_baseline": 7.8,
      "first_seen": "2026-10-17",
      "sentiment": "negative",
      "sources": ["forums", "review_sites"],
      "example_phrasings": ["billed twice this month", "invoice date moved without notice"],
      "escalation": { "amplification_rate": 0.21, "large_account_involvement": false }
    },
    {
      "theme_id": "thm_0908",
      "label": "Positive mentions of calibration turnaround",
      "mentions": 61,
      "growth_vs_baseline": 2.1,
      "sentiment": "positive"
    }
  ],
  "share_of_voice": { "Example Industries": 0.31, "Northbridge Mfg": 0.44, "Calder Group": 0.25 },
  "privacy": "Aggregated at theme level. No author profiles. Clusters below 8 mentions suppressed."
}

Where the data comes from

Public review sites and forums	Where permitted by terms; much of the most specific complaint language appears here rather than on social platforms
Licensed social data access	Official APIs and licensed firehose access where available — collection and redistribution are separately licensed and both have to be checked
News and trade publication feeds	Licensed or openly syndicated, depending on the publisher
Your own support tickets and survey responses	Optional and internal; complaint themes almost always appear here before they appear publicly
Your product and brand taxonomy	So themes map to categories your communications and product teams already use
Deliberately excluded sources	Platforms whose terms forbid automated collection, and any source requiring authenticated access to private content — excluded rather than worked around

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Theme clustering by embedding similarity, which catches a complaint expressed twenty different ways where keyword alerts do not
Emergence detection on growth relative to each theme's own baseline, not absolute volume, so a small fast-growing cluster surfaces before a large stable one
Domain-adapted sentiment classification, since general-purpose models misread industry language, product names and sarcasm at a high rate
An explicit uncertain class rather than forcing every mention into positive, negative or neutral — forced classification is where sentiment dashboards lose credibility
Minimum cluster thresholds before a theme is reported, both for statistical reasons and to avoid surfacing individual people's posts as findings
No author-level profiling: aggregation is by theme and source, and we would not build a per-person history
Bot and coordinated-activity detection, so an inauthentic campaign is flagged rather than reported as public sentiment
Classifier validation against human-labelled samples per source, with accuracy reported by source because performance varies a lot between a review site and a forum

What it changes for the business

Earlier warning

Theme-level emergence detection surfaces a specific complaint while it is dozens of mentions rather than thousands.

Less manual reading

Clustered themes with example phrasings replace the morning spent reading raw mention feeds.

Product feedback with a route

Complaint themes mapped to your own taxonomy land with the team that can fix the underlying cause.

Reporting that holds up

Per-source classifier accuracy and suppression thresholds make the numbers defensible in a client or board review.

How it fits into your product

How it embeds

REST endpoints for mentions, themes and trends, feeding your existing dashboards
Emergence and escalation webhooks into communications alerting and on-call rotations
Taxonomy configured per client, so themes map to their categories rather than a generic set
Optional ingestion of internal support tickets alongside public mentions, kept in your environment
White-label delivery with suppression thresholds and privacy notes carried into client output
Documented retention windows and per-tenant separation for DPA and procurement review

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 12 weeks

Discovery and data review

Weeks 1–2

We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.

Architecture and prototype

Weeks 3–4

You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.

Production build

Weeks 5–10

Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.

Integration and handover

Weeks 11–12

We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

How accurate is the sentiment classification?

It varies by source, and we report it per source rather than as one number. Review text with a star rating is easy; forum posts with sarcasm, in-jokes and product jargon are much harder. The uncertain class exists so the model can decline rather than guess, and we validate against human-labelled samples from your own sources during the prototype so you see real accuracy before committing.

Which platforms can you actually collect from?

Those whose terms permit it, which is fewer than the list most listening tools imply. Some platforms offer licensed access, some forbid automated collection outright, and several permit collection for analysis but not redistribution of the content itself. We map the position per source during discovery and build so excluded platforms are visibly absent rather than silently approximated from elsewhere.

Does this process personal data?

Yes — public mentions are written by identifiable people, and in most jurisdictions that makes this personal data processing with a lawful basis required. The design responds by aggregating at theme level, suppressing clusters below a threshold, keeping no author profiles and holding content for a documented retention window. We would decline to build per-person monitoring or author dossiers, which is where this category reliably goes wrong.

Can you monitor private groups and closed communities?

No. Anything requiring authenticated access to content not public is out of scope, regardless of what it might reveal. That is a limit on coverage and we would rather state it than imply a completeness the sources do not support.

What about coordinated or inauthentic activity?

Detected and flagged where the patterns are visible — account age, timing clustering, near-duplicate text. Detection is imperfect, so flagged volume is reported separately rather than deleted, letting your team judge. Treating a brigading campaign as organic sentiment is the most expensive error this kind of tool makes.

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