Abhord Quickstart Guide (January 2026 Refresh)
This refreshed edition reflects the 2025–2026 shifts in the LLM landscape and new Abhord workflows that make Generative Engine Optimization (GEO/AEO) more reliable and repeatable.
What’s new since the last version
- Dynamic model pools: run surveys across rotating sets of leading LLMs (e.g., GPT-, Claude-, Gemini-, Llama-/Mistral-class) with automatic fallback when a model is unavailable.
- Improved entity resolution: canonicalize brand, product, and competitor variants; reduce duplicate mentions and hallucinated aliases.
- Time-aware metrics: trendlines and “SOV Δ” (share-of-voice change) with week-over-week and month-over-month baselines.
- Neutral-drift sentiment calibration: better separation of neutral vs. mixed sentiment, especially for safety-heavy models.
- Scheduling and alerts: time-zone aware runs, Slack/Teams webhooks, CSV/Parquet exports.
1) Initial setup and configuration
Goal: create a clean project, define what you’re measuring, and choose the models that will answer.
- Create a workspace and project
- Workspace = your organization. Project = one campaign or product line.
- Name conventions: use YYYY-Project-Region (e.g., 2026Q1-Widgets-US) to keep releases easy to compare later.
- Connect data inputs (optional but recommended)
- Add your canonical sources: product pages, docs, FAQs, press pages, spec sheets or a public sitemap. This improves attribution and reduces hallucination when models “recall” your brand.
- Define entities
- Add brand and product names, including common misspellings and acronyms (e.g., “Acme Ultra,” “AcmeUltra,” “AU”).
- Add competitors with their aliases and product lines. Tag each entity category (brand, product, feature, persona).
- Choose your model pool
- Select a balanced mix: at least one OpenAI-, Anthropic-, Google-, and open-source family model.
- Set per-model caps (responses per question) to avoid over-weighting a single model.
- Enable dynamic fallback so runs proceed even if a model rate-limits or goes offline.
- Configure governance
- Set data retention, PII scrubbing, and answer-length limits.
- Assign roles: Owner (billing/limits), Analyst (queries/dashboards), Viewer (read-only).
Pro tip: Lock a “baseline configuration” (entities + model pool + prompts) before campaigns. Version it when you change any of the three.
2) Run your first survey across LLMs
Goal: ask consistent questions across models and capture how the generative web “talks about” you vs. competitors.
- Start with a survey template
- Use “Brand + Competitor SOV” or “Feature Discovery.” These include vetted prompt frames and