AI brand sentiment management & answer correction
Taptwice Media is India’s best AI brand sentiment management company, based out of Delhi NCR. We control what AI engines say about your brand — weekly mention tracking across 8 AI engines, 24-hour misrepresentation SLA, and corrective placements in authoritative sources.
You cannot edit LLM weights — but you can change the sources they retrieve from
Every generative AI answer about your brand is assembled from the retrieval corpus: Wikipedia, news wires, trade press, Reddit threads, LinkedIn posts. When the corpus contains wrong, stale or hostile content, the LLM cites it confidently.
We cannot reach inside ChatGPT or Claude and rewrite their weights. What we can do is change the sources those models retrieve from — publish corrections in authoritative places, monitor retrieval-shift, and re-benchmark until the accurate narrative wins.
This is reputation engineering for the AI-answer era. Different retrieval layer, different levers, different response times than traditional ORM.
What actually goes wrong, and how we respond
Every engagement includes a threat model tuned to your category. These are the most common risk patterns.
Confident hallucinations
The LLM states something about your brand that is plainly wrong — a fabricated founder name, a wrong funding figure, a fictional product. We flag within 24 hours and route correction.
Stale facts
Your 2021 pricing, your previous office address, your old CEO — LLMs over-index on older corpora. We update Wikipedia, wire archives and trade-press records so fresh facts overtake stale ones.
Negative review amplification
One angry Reddit thread becomes the LLM’s summary of your brand. Counter by seeding balanced context and placing positive authoritative content in sources LLMs weight higher.
Competitor conflation
The engine mixes up your brand with a similarly-named competitor. We fix by tightening entity graph disambiguation across Wikidata, Google Knowledge Graph and LinkedIn.
Category misplacement
You sell B2B SaaS but the LLM describes you as an e-commerce brand. Usually entity-graph confusion. Fixed by Wikipedia + Wikidata statement refinement.
Silence
Worst case: the LLM has no clear source, so it says nothing about you — or worse, hallucinates. Fixed by establishing AEO foundations and seeding authoritative corpora.
Weaponised misinformation
Competitor-driven FUD or hostile edits to Wikipedia / Crunchbase. We maintain edit-history surveillance and execute counter-placements in authoritative sources.
Regulatory / compliance risks
Financial claims, medical claims, regulated vertical language — LLM summaries can expose compliance risk. We audit language across indexed sources quarterly.
The eight-step correction playbook
Every flagged event runs through this loop. Every incident is documented.
- Continuous mention tracking
Weekly prompt runs across 8 AI engines capture every mention of your brand. Every answer is logged, classified, diffed against last week.
- Sentiment classification
A tuned Claude pipeline classifies each mention (positive / neutral / negative / misleading / factually wrong) and clusters topics so patterns surface.
- Flag + triage
Within 24 hours of a misrepresentation appearing, we flag it with source attribution, impact estimate, and a recommended correction path.
- Trace retrieval source
Identify which source the LLM used — a bad Reddit thread, a stale wire, a Wikipedia revision, a competitor blog. Correction targets the source, not the LLM.
- Draft corrective placement
Write the counter-content: a Wikipedia update with proper sourcing, a wire release with correct facts, a trade-press clarification, or a founder-authored LinkedIn post.
- Place in authoritative corpus
Publish through our distribution channels — GlobeNewswire, ANI, Wikipedia, trade press. Placements are prioritised by retrieval weight, not convenience.
- Monitor retrieval shift
Re-benchmark the priority query weekly until the corrected narrative replaces the wrong one in the AI answer. Typically 2-6 weeks.
- Close the loop + document
Every incident is documented in your incident log: what happened, what was placed, how long retrieval-shift took. Builds the playbook for the next one.
What proactive reputation engineering looks like
| Real sentiment control (what we run) | Reactive monitoring | No monitoring | |
|---|---|---|---|
| Mention tracking | Weekly across 8 AI engines, sentiment-classified | Quarterly manual checks | None — discovered by customers |
| Hallucination detection | Within 24 hours of appearance | When someone complains | Never |
| Correction channel | Authority corpus (wiki, wires, trade press) | Public response / Twitter | Ad-hoc |
| Retrieval-shift timeline | 2-6 weeks typical | Unknown | Unknown |
| Crisis response | 24h SLA, 7-day execution | Best-efforts | Panic |
| Documentation | Full incident log with attribution | Partial, in emails | None |
| Proactive seeding | Yes — before negative narrative solidifies | No | No |
| Compliance audit | Quarterly review of indexed claims | Not tracked | Not tracked |
What we use to monitor, flag and correct
Rankscale
Weekly mention tracking + sentiment capture across 8 AI engines.
Claude sentiment pipeline
Tuned classifier for sentiment + topic clustering + misrepresentation flagging.
Wikipedia surveillance
Edit-history monitoring, hostile-rewrite detection, reference integrity checks.
Wikidata editor tools
Statement refinement, entity disambiguation, source attribution.
GlobeNewswire + ANI
Authoritative correction-placement channel through Taptwice Global.
Trade-press relationships
Search Engine Land, Economic Times, YourStory, Inc42 for category clarifications.
Slack + CRM webhooks
Instant alerts to your team + auto-ticketing for each flag, with 24h SLA enforcement.
Incident-log runbook
Every misrepresentation event documented with root cause, correction placed, and retrieval-shift outcome.
Sentiment control questions
Can you actually change what ChatGPT says about my brand?
Indirectly but reliably. We cannot edit LLM weights — but we change the sources LLMs retrieve from. Authoritative corrections placed in Wikipedia, news wires and trade press shift retrieval-augmented responses within 2-6 weeks.
What if there is no misinformation — just silence?
That is the more common case. When an engine has no clear source, it either hallucinates or stays silent. Fixed by our AEO foundations plus authority distribution.
How fast can you respond to a live crisis?
24-hour SLA from flag to corrective brief. 7 days to execute the placement (press release, wiki edit, trade-press quote). For true emergencies we can move faster — scoped case by case.
Do you monitor all 8 AI engines for mentions?
Yes — ChatGPT (GPT-5 family), Claude (Opus 4.7 / Sonnet 4.6 / Haiku 4.5), Perplexity, Gemini, Copilot, You.com, Grok, and Google AI Overviews. Weekly cadence minimum.
What counts as a “misrepresentation” worth flagging?
Factual errors (wrong numbers, wrong names, fabricated quotes), category misplacement (described as the wrong type of business), competitor conflation, weaponised negative framing, and stale pricing / status facts.
How do you prove retrieval shifted?
We re-benchmark the affected query weekly and log the AI answer before/after. Once the corrected narrative replaces the wrong one, we close the ticket and document the timeline.
Will you defend against hostile Wikipedia edits?
Yes — we maintain edit-history surveillance on your Wikipedia page, detect hostile rewrites, revert or contest them, and maintain COI disclosure discipline so legitimate edits stick.
Can you help with regulated verticals (finance, pharma, healthcare)?
With scope. Regulated vertical language requires pre-approved claims, compliance-reviewed placements, and conservative wording. We will scope a bespoke retainer with your compliance lead in the loop.
Is this different from reputation management?
Related but AI-native. Traditional ORM focuses on Google SERPs, review sites and press. Sentiment control focuses on what LLMs say — a different retrieval layer, different levers, different response times.
What about negative reviews on G2 / Capterra / Trustpilot?
We track review-site sentiment as input signal, and recommend platform-specific responses. Corrections in those platforms follow their own rules — we do not manipulate reviews.
How often do incidents typically happen?
Varies by brand maturity and category. Early-stage brands often see 1-2 major misrepresentation events per quarter. Mature brands with cleaner entity graphs see 1-2 per year.
How is this priced?
Bundled inside Growth ($1,999), Scale ($3,999) and custom retainers. Standalone sentiment-control retainers available for larger brands — see pricing.
The rest of the engagement
Not sure where to start?
Talk to us. We will look at your brand, benchmark where you stand across AI engines, and map the shortest path to getting cited — usually a 20-minute call or a WhatsApp thread.
