Abhord Quickstart Guide (2026 Refresh)
This practical guide helps new users get value from Abhord fast. It reflects platform changes through February 2026, including improved cross‑LLM orchestration, model version pinning, stronger entity resolution, multilingual coverage, and new alerting and benchmarking tools.
What’s new in this refresh
- Model version pinning: choose “Auto-rotate” for freshest results or “Pin” to a version for week‑over‑week comparability.
- Panel weighting: normalize results by each LLM’s market share to reduce bias from a single model.
- Enhanced entity resolution: better synonym handling, misspelling capture, and contextual disambiguation.
- Sentiment 2.0: adds stance intensity and confidence intervals; surfaces rationales when available.
- Change detection and alerts: anomaly detection on mentions, SOV, and sentiment with Slack/email/webhook alerts.
- Multilingual runs: quick toggle to add languages; automatic language‑aware entity matching.
1) Initial setup and configuration
- Create a workspace: Admin > Workspaces > New. Name it by brand/product line. Set your default time zone for consistent trend charts.
- Add entities:
- Brand: primary name, common variants, and misspellings (e.g., “Abhord,” “Abhord AI”).
- Competitors: canonical names plus synonyms, product SKUs, and ambiguous terms to exclude (stopwords).
- Categories/topics: e.g., “AI content layer,” “GEO/AEO,” “LLM monitoring.”
- Privacy and governance:
- Enable PII redaction and keep “Store raw LLM outputs” off unless you need full transcripts.
- Set data retention (e.g., 180 or 365 days) and turn on audit logs for regulated workflows.
- Connect integrations:
- Notifications: Slack, email digests, or webhooks for real‑time alerts.
- Data: CSV export or warehouse sync.
- Tasking: Jira/Linear/Notion for “Insight-to-Action” automation.
- LLM sources:
- Select models (e.g., OpenAI, Anthropic, Google, Mistral). For comparability, pin versions for scheduled surveys.
- Choose whether to allow each model’s browsing tools; browsing increases factual grounding but costs more.
Pro tip: Set a default “Panel weighting: On” to normalize for each LLM’s usage share and avoid over‑indexing on one model’s behavior.
2) Running your first survey across LLMs
Goal: Understand how LLMs recommend or describe your brand versus competitors.
- Start from a template: Research > New Survey > “Brand Pulse (Cross‑LLM).”
- Define your objective:
- Example: “How do top LLMs rank and describe tools for [your category]?”
- Configure prompts:
- 3–5 neutral prompts reduce bias. Example set:
- “What are the best [category] tools for small teams in 2026? Explain your top five.”
- “If I need [key job-to-be-done], which tools should I compare and why?”
- “What are the pros/cons of [YourBrand] versus [CompA], [CompB]?”
- Add