Abhord Quickstart Guide (2026 Refresh)

This practical guide helps new Abhord users go from zero to insight in under an hour. It reflects recent platform improvements and updated best practices for running multi‑LLM surveys, interpreting results, tracking competitors, and turning insights into action.

1) Initial setup and configuration

• Create your workspace

- Add your brand, product lines, and key markets.

- Invite teammates with clear roles: Admin (billing + governance), Analyst (survey + dashboards), Viewer (read‑only).

• Configure data hygiene

- Turn on brand/competitor normalization (handles acronyms, misspellings, and legacy names).

- Enable PII redaction and safe‑prompt mode for regulated categories.

- Set your default locale and language; create additional cohorts if you serve multiple markets.

• Establish a baseline taxonomy

- Define your categories (e.g., “Email Marketing,” “Orchestration,” “Pricing”), intents (informational, comparative, transactional), and outcomes (recommendation, caution, neutral).

- Add competitor list and synonyms; include near‑competitors and adjacent tools to catch “alternatives to …” queries.

• Connect notifications and logs

- Enable Slack or email alerts for significant shifts (e.g., ±5% share of voice week‑over‑week).

- Archive every survey’s prompt template and model settings for auditability.

• What’s new in 2026

- Cross‑LLM normalization improvements reduce model‑specific bias in share‑of‑voice.

- Rate‑limit aware scheduling prevents partial runs and keeps cohorts aligned in time.

- Quality controls: automatic hallucination flags and citation‑completeness scoring.

2) Run your first survey across LLMs

Goal: capture how multiple LLMs answer a high‑intent question today.

• Choose a decision‑driving question

- Comparative: “What are the best [category] tools for [audience]?”

- Transactional: “Which [category] product offers the best [feature] for under $X?”

- Brand‑specific: “What are the top alternatives to [YourBrand]?”

• Build your panel

- Select a balanced mix of leading closed and open models.

- Keep model versions fixed during a run to ensure apples‑to‑apples comparisons.

• Configure the survey

- Sampling: 20–50 responses per model for stable share‑of‑voice; start with 10 for a pilot.

- Temperature: 0.1–0.3 for factual tasks; higher only for ideation surveys.

- Output controls: require top‑N lists and short rationales; request citations/links when the model supports it.

- Deduplication: enable NER‑based and fuzzy matching to consolidate “Brand, Inc.” vs “Brand.”

• Quality steps (recommended)

- Pilot run (5–10 responses/model), review flags and examples, then scale.

- Lock prompts and rerun immediately to capture a same‑day baseline across models.

• Pro tip

- Create a “Reasoning vs Speed” segment if you include variants of the same model; this often reveals