Abhord Quickstart Guide (Refreshed 2026 Edition)
What’s new in this edition
- Broader model coverage and regional variance: Leading LLMs now return more region-specific and safety-filtered answers. Recommendation: segment surveys by market (e.g., US, UK, DE) and compare results side-by-side instead of averaging globally.
- Higher weight on cited sources: Models increasingly privilege concise, well-structured, and reputable citations. Recommendation: prioritize updating your docs, comparison pages, and third‑party profiles with machine-readable facts and clear claims-evidence pairs.
- Answer caching by assistants: Repeatedly asked commodity questions may return cached or templated answers. Recommendation: rotate prompt variants and include time-bound qualifiers (e.g., “as of February 2026”) in survey questions.
- Stricter brand-neutral responses: Some models avoid explicit recommendations. Recommendation: track indirect mentions and “category-fit” language, not just direct brand calls.
1) Initial setup and configuration
Goal: align Abhord with your brand entities, priority topics, and target markets.
- Create your workspace
- Add brand entities (official name, product lines, common misspellings, ticker if relevant).
- Set default locales/languages you serve.
- Invite collaborators with clear roles (Owner, Analyst, Editor). Enable SSO if available.
- Connect canonical sources
- Your website: homepage, product pages, docs/knowledge base, pricing, FAQ, newsroom.
- High-trust third parties: Wikipedia/Wikidata, GitHub or app store listings, analyst reports, review sites.
- Provide canonical facts (founded year, locations, feature lists) in structured formats where possible (schema.org, JSON-LD, or clean tables).
- Define competitor set
- Add 5–10 direct and adjacent competitors with aliases and product names.
- Tag each competitor to a category (e.g., “enterprise email security” vs “SMB email security”) to enable cluster-level share-of-voice.
- Configure model panel
- Select the LLMs and assistant surfaces you care about (e.g., general assistants, search-integrated chat, code- or design-focused models).
- Assign weightings by your audience share (traffic,