Social Research
A continuously running room of AI specialists watching your brand across every platform, every format, every signal — and surfacing the few that matter.
Three structural limits keep traditional tools from doing the work brands actually need.
More than half of social content is now image, video, and audio. Logos appear in TikToks that never tag the brand. Sentiment is carried in a tone of voice, not a sentence. Text-only listening sees less than half the conversation — blind by design.
12,400 mentions this week with net sentiment +23% is a description, not an answer. The team still has to sit in a room and figure out what to do. Counting is not analysis. Analysis is not a decision.
By the time a sentiment dashboard turns red, the story has moved. Press has called. Customers have screenshot. Damage is no longer prevention — it is repair. Most tools are reactive by definition.
A branding agent has clustered the week's mentions by attribute
and persona. A marketing agent has named the two campaigns that
outperformed and the one that quietly underperformed. A customer
experience agent has flagged a complaint cluster up 400%
week-over-week. A competitive agent has noticed that a rival
is being credited for a feature you actually own.
You don't read a dashboard. You read a brief. When something
matters, you are told. When something is about to matter, you
are told earlier.
The brief view — top headline ("This week, in one read"), 3–4 prioritized findings written as plain sentences, each with provenance (source, volume, evidence link), a "what to do" recommendation, and a small chart per finding.
UI screenshot mockup of a "brief view" dashboard for an AI brand-sensing tool. Clean minimal SaaS interface, light theme with brand colors electric-blue (#1e90ff) and vivid-indigo (#5b3df5). Top bar: brand name "Acme Coffee", date "Week of May 12", agent count "5 specialists", a small "Live" pulse indicator.
Main pane: a vertically stacked "this week's brief" — 4 finding cards in chronological order of importance. Each finding card has:
- a small role-tag pill in monospace ("BRANDING", "CRISIS", "MARKETING", "COMPETITIVE"), color-coded
- a one-sentence headline in Space Grotesk medium (e.g., "Complaint cluster about delivery times grew 400% — investigate now", "Spring campaign outperformed forecast by 28%")
- two or three lines of evidence with small inline chips ("212 mentions · TikTok + X", "Confidence: high", "vs. competitor: −12%")
- a small sparkline or bar chart on the right
- a subtle "Open evidence →" link
Right rail: a vertical "agents at work" panel listing 5 specialist agents with status dots ("scanning", "verified finding", "watching"). Bottom: a faint divider with "Next sync in 14 min".
Generous whitespace, JetBrains Mono for monospace tags, Space Grotesk for body. 16:10 aspect. Avoid: real brand names, stocky dashboard clichés, gauges, speedometers, busy gradients.
A single LLM asked to summarize a brand will average everything into a paragraph. Average is not useful. Amicus runs a swarm of specialist agents over the same observation layer. Each one looks at the data through a different lens. Agents share evidence, they disagree, and their outputs converge into a brief — not a single number.
Attribute drift, perception clusters, positioning relative to category — how the brand is understood, not just how often it's named.
Engagement DNA, campaign reception, audience response patterns — which content actually moves the room, and why.
Pain points, journey-stage detection, satisfaction signals — extracted from real language, not survey approximations.
Gap analysis, counter-positioning, attribute capture — what story rivals are winning that you should not be losing.
Anomaly detection, emotion-intensity scoring, virality-trajectory modeling — early warnings before a story breaks.
Want the full metrics framework? Twenty-eight metrics across three tiers — from industry-standard foundations to multimodal and anticipatory metrics that turn analytics into decisions.
Read the metrics guide →Most of what is said about a brand on social media today is not written. Amicus reads text, image, video, and audio as one continuous signal — so the conversation is fully heard, not half-captured.
The result is a sensing surface that catches conversations text-only tools miss entirely — and that grades sentiment from how something is said, not just what is said.
Two research modes covering the questions brands actually ask: how is my topic being discussed across the internet, and how does this specific audience actually think and talk.
Best for — brand health monitoring, campaign tracking, category landscape, crisis surveillance.
Best for — competitor profiling, target-community study, partnership briefings, audience deep-dives.
A consumer-goods brand cares about price, quality, and service. A technology brand cares about innovation, reliability, and ecosystem. A luxury brand cares about heritage, exclusivity, and craft. The same listening template cannot serve all three. Amicus lets each brand define its own dimensions — the slices along which conversation is scored and tracked over time.
The dashboard is designed for the business that signs in — and for the role of the person reading it. Same underlying signal, presented in the form each role actually needs.
A single executive read. No analyst needed to translate it. Direction, not detail.
Where the positioning is landing, where it is slipping, and which rival is making ground on which dimension.
Hours-ahead warnings, counter-narrative tracking during controversy, influencer reach and authenticity maps.
Real audience language, real journey signals, real pain extraction — not survey approximations.
Brand health brief, category share of voice with directional context, strategic-risk early warning. Signal, not spreadsheet.
Attribute map vs. competitors updated weekly, persona-level campaign reception, white-space identification.
Anticipatory crisis alerts in hours, counter-narrative tracking during controversy, influencer authenticity and reach mapping.
Audience persona clustering from social text, customer journey stage detection, pain point and win/loss extraction at scale.
Category landscape with white-space topics, audience persona briefs ready to insert into a deck, competitor narrative audit.
Always-on background sensing across every platform you track. The observatory does not sleep.
Crisis and trend signals delivered before they peak — early warning, not post-mortem.
Written summaries that name the action, not just the data. Read it and decide; don't read it and assemble.
Image, video, and audio surfaced alongside text — the half of the conversation other tools miss.
The slices that matter to your brand, not someone else's. Schema is configured per business, not fixed.
Every signal is traceable to source. Every alert is auditable. The observatory is built to be trusted.
Traditional social listening counts what's already loud. Amicus reads what's about to matter — across every modality, with a panel of specialists, in a form ready to act on.
| Traditional social listening | Amicus Social Research | |
|---|---|---|
| What it sees | Text mentions only | Text, image, video, and audio as one signal |
| What it produces | Dashboards with metrics | Briefs with decisions named |
| When it alerts | After the trend is visible | Before — based on early signals and emotion intensity |
| How it analyzes | One model averaging everything | A swarm of specialist agents reading different lenses |
| How it categorizes | One template for everyone | Custom schema per brand |
| What it costs to use | Analyst hours to interpret | Built to be read directly by the role that needs it |
An AI boardroom for serious thinking — a panel of specialists who debate, verify, and converge on a decision.
Open product →An AI observatory for brand sensing — continuous multimodal listening, surfaced as decision-ready briefs.
You are hereAdditional Amicus rooms are in development for the parts of work where careful thinking still matters more than fast answers.
Talk to us →Always-on multimodal sensing across every platform you track — never a snapshot, always a live read.
Every finding traces back to source signal. Every alert is reviewable. The observatory is built to be trusted.
Your categories, your roles, your alert thresholds — the engine is shared, the room is personal.