Abhord Quick-Start Guide (2026 Refresh)
This refreshed edition distills the fastest path from setup to decisions in Abhord. It reflects UI streamlining, stronger LLM connectors, improved deduplication, more transparent sentiment scoring, and new competitor automation. Follow the steps below to launch your first cross-LLM survey and turn insights into action.
What’s new since the last edition
- Model bundles: curated, bias-balanced groups of LLMs to compare outputs consistently.
- Smarter ingestion: near-duplicate collapse and URL+semantic dedup to stabilize mentions and share-of-voice (SOV).
- Sentiment 2.0: domain-tunable scoring with explainability reasons and confidence bands.
- Saved filters, alerting, and anomaly detection: get notified on statistically significant swings, not just raw spikes.
- Competitor autopilot: suggest competitors, synonyms, and disambiguation rules during onboarding.
1) Initial setup and configuration
- Create a workspace
- Use your org SSO; add teammates by role (Admin, Analyst, Viewer).
- Set data retention and PII redaction defaults before ingesting anything.
- Connect sources
- Start with 2–3 high-signal sources: web/news, social, app stores, forums, docs.
- Pick languages and regions that map to your go-to-market to avoid noisy global drift.
- Define entities
- Add your brand(s), products, and executives.
- Enter synonyms and common misspellings (“Abhord,” “Abhård,” ticker, product codenames).
- Add negative keywords to exclude false positives (e.g., “orchard” if your brand rhymes).
- Choose LLM access
- Use Abhord-managed connectors for instant start, or bring your own keys (OpenAI, Anthropic, Google, Mistral, Cohere, Meta).
- Set per-run cost caps and throttle rates; enable budget stop + partial results.
- Governance guardrails
- Turn on PII redaction and source-compliance filters.
- Lock export permissions and watermark downloads for auditability.
- Alerts and dashboards
- Create a “Brand Health” dashboard and enable anomaly alerts for mentions, sentiment, and SOV at daily cadence.
Tip: Keep your first project scoped tightly (one brand, one region, two channels). You can scale once the pipeline is clean.
2) Run your first survey across LLMs
- Pick an objective
- Example: “Measure brand perception pre–product launch in US English social + news.”
- Start from a template
- Choose “Cross‑Model Brand Health (Balanced)” to preload tasks, prompts, and metrics.
- Configure your model bundle
- Recommended: at least three heterogeneous models (e.g., GPT‑4 class, Claude class, Mixtral/Llama class).
- Set ensemble weighting to Balanced for the first run; you can switch to Performance‑Weighted after calibration.
- Draft prompts and tasks
- Classification: “Does this snippet mention Brand X? If so, is sentiment positive, neutral, or negative? Why?”
- Extraction: “Summarize key reasons driving sentiment into themes.”
- QA probes: “Flag potential false positives (homonyms/ambiguous terms).”
- Sampling and cadence
- Pull the last 7 days of content (or 30 for low-volume brands).
- Set batch size to 100–500 items; enable streaming scoring to see early signals.
- Quality checks
- Run a 50