Connect brand signals, source evidence, and the people responsible for responding.
The operating problem
Brand teams can collect thousands of mentions while still missing the incident that needs an owner. We build a shared operating view across approved sources, brands, products, and review queues, with domain support from Ophanix where the engagement calls for it.
See the mechanism
Every mention leaves with an owner.
Monitoring is only useful if each signal lands somewhere accountable. Step through six sample mentions to see how a label, a confidence level, and an approved source decide where each one goes.
Mention triage · illustrative data
Page comment1 / 6
Where can I buy the 5 kg pack in bulk near Chattogram?
Purchase intentHigh confidence→ Sales lead
A buying signal with a location, sent to the regional owner as a lead. A lead is not a confirmed sale.
Logged0
Reporting only
Sales lead1
Regional sales owner
Purchase intent
Grounded answer0
Approved source cited
Human review0
Named reviewer
Incident0
Brand owner, response deadline
Sample mentions, not client data. Each one gets a label, a confidence level, and a destination with an owner. Low confidence and unsupported claims go to a person.
What we build with you.
01
Signal intake
Permitted platform and news connections, normalization, source links, language tags, and access-scoped storage.
02
Classification & review
Sentiment and purchase-intent classification, confidence thresholds, labelled evaluation samples, and human escalation.
03
Grounded responses
Product answers constrained by approved source material. Missing or conflicting evidence routes to an accountable reviewer.
04
Operational reporting
Brand and group views with drill-down to underlying evidence, response ownership, and measurements linked to your CRM or support records.
What the research says
Coverage, accuracy, and refusal are design decisions.
Platforms decide what can be monitored. EU rules now open some non-public data from the largest platforms, but only to researchers vetted by a national Digital Services Coordinator.1 Meta's research library is limited to academic and not-for-profit organisations and to certain public accounts.2 We scope coverage to the accounts you own and the sources you are permitted to use, and we state what falls outside it.
Bangla remains a low-resource language for sentiment analysis. In the BLP-2023 shared task the best system reached a micro-F1 of 0.73, and the neutral class was the hardest to identify.3 So we measure accuracy per language and per task on your own labelled sample before quoting any figure.
Generative models can state false content with confidence. NIST calls this confabulation and recommends verifying the sources and citations in outputs.4 Replies are composed only from approved material, and a question with no matching source goes to a person. Synthetic-media flags get the same treatment: NIST notes that detectors often work only on the generators they were trained on and can wrongly flag human content.5
Where brand-safety labels are needed, we map them to a shared vocabulary such as the IAB Tech Lab Content Taxonomy instead of inventing one per project.6
For technical reviewers
The routing rule is short enough for a brand manager to review. Thresholds come from a labelled sample for each language and task, never copied from another language.3
route.ts · triage rule (simplified)
export function route(m: Classified, kb: KnowledgeBase): Route { // Below the threshold for this language and label, a person decides. if (m.confidence < THRESHOLD[m.lang][m.label]) { return { to: "review", owner: reviewerFor(m) }; } switch (m.label) { case "health_claim": return { to: "review", owner: "claims-signoff" }; // named sign-off, always case "coordinated_claim": return { to: "incident", owner: brandOwner(m.brand), due: slaFor(m.brand) }; case "purchase_intent": return { to: "sales", owner: regionOwner(m.district) }; // a lead, not a sale case "product_question": { const hits = kb.search(m.text, { brand: m.brand, approvedOnly: true }); if (hits.length === 0) return { to: "review", owner: reviewerFor(m) }; // no source, no answer return { to: "answer", owner: "brand-care", cite: hits.map((h) => h.id) }; } default: return { to: "log" }; }}
A useful starting point
Your first engagement.
Pilot one brand, a bounded set of sources, and one response workflow before expanding coverage.
Brand and product list
Approved data access and product claims
Named reviewers and escalation rules
Sources
Evidence and further reading
Primary sources for the standards and practices referenced on this page. They describe the field, not Verne's own results.
Meta Content Library and API (opens in a new tab)Meta Transparency Center · updated 30 April 2026Eligibility is limited to academic and not-for-profit organisations, and data covers defined categories of public content.
03
BLP-2023 Task 2: Sentiment Analysis (opens in a new tab)Hasan, Alam, Anjum, Das, Anjum · ACL Anthology (BLP-2023) · December 2023Bangla is low-resource for sentiment analysis; the best system scored 0.73 micro-F1 and the neutral class was hardest to identify.
Content Taxonomy (opens in a new tab)IAB Tech Lab · last updated 11 February 2026A common language for describing content, used for contextual targeting and brand safety.
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