Abhord Quickstart Guide (2026 Refresh)

Welcome to Abhord—the GEO/AEO layer that shows how large language models (LLMs) and AI answer engines talk about your brand, products, and competitors. This refreshed edition adds updated workflows, new best practices, and highlights recent platform improvements.

What’s new in this edition (March 2026):

• Expanded model coverage and regional/language lenses
• Stronger mention de-duplication and entity normalization
• Confidence bands on sentiment and share-of-voice (SOV)
• Survey templates by use case (brand, product, category, feature)
• Action playbooks and Slack/email digests

1) Initial setup and configuration

1) Create your workspace

• Add your organization, brand(s), and product lines.
• Set primary market(s), languages, and time zone for reporting.
• Invite collaborators; assign roles (Admin, Analyst, Viewer).

2) Define entities

• Brand and product names, common misspellings, and abbreviations.
• Competitor list (direct and adjacent). Include product families and codenames.
• Synonyms and shorthand (e.g., “Abhord GEO,” “Abhord platform”) to improve recall.

3) Connect sources

• Publish your official domains (www, docs, blog, pressroom) and social handles.
• Optional: connect your knowledge base or changelog to power “what changed” analyses.
• Verify ownership for higher trust weighting.

4) Choose models and engines

• Select the LLMs/answer engines you care about (global and region-specific).
• Set sampling sizes per model. Start balanced, then weight by your audience mix.

5) Alerts and digests

• Enable weekly SOV and sentiment digests to Slack/email.
• Turn on anomaly alerts (sudden sentiment drop, competitor surge, claim drift).

Pro tip: Use the “Brand Essentials” setup template to pre-fill entities, competitors, and tracking rules in under 10 minutes.

2) Running your first survey across LLMs

Goal: establish a baseline of how models currently describe you versus competitors.

1) Pick a template

• “Brand Overview” (recommended first run) or “Product Comparison.”
• Each template includes pre-tested prompts that mimic real user queries.

2) Scope your scenario prompts

• 6–10 prompts that reflect buyer intent:

- “What is [Brand/Product]?”

- “Best tools for [use case].”

- “Compare [Brand] vs [Competitor] for [audience].”

• Include a few long-tail, natural-phrasing variants to avoid prompt overfitting.

3) Select models and locales

• Choose at least 4–6 major LLMs/engines.
• Add one non-English locale if you operate globally; gaps often surface here.

4) Configure run settings

• Sampling size: 20–50 runs per prompt/model for a stable baseline.
• Freshness window: 30–90 days for “recency-aware” engines.
• Hallucination guardrail: enable citation-check weighting for grounded results.

5) Launch and label

• Start the survey. When complete, spot-check 10–