Match listings to buyer intent, not generic search text
Reala uses attached lead context to apply hard constraints, rank by preference fit, and explain every recommendation with clear reasons and visible tradeoffs.
Reala uses attached lead context to apply hard constraints, rank by preference fit, and explain every recommendation with clear reasons and visible tradeoffs.
Lead-driven defaults
Attached lead context becomes the default search profile for vague listing requests.
Hard constraints first
Price, location, property type, and minimum requirements filter invalid options before ranking.
Explainable ranking
Each listing includes score, fit reasons, missing must-haves, and clear risk flags.
Honest confidence
When profile data is incomplete, results still appear with visible missing fields to tighten next.
Deterministic matching before agent interpretation
Reala first produces a normalized lead profile and ranked match set, then the managed agent explains tradeoffs and prepares the next shortlist move behind approval.
- 1Normalize lead context into a search profile using requirements, notes, and shortlist interaction signals.
- 2Apply hard filters, then rank surviving listings with soft preference scoring and shortlist-fit weighting.
- 3Prepare recommendations, rebuttal notes, and follow-up drafts using match explanations and visible profile gaps.
Higher-signal listing conversations
- Vague asks resolve into lead-aware ranked options by default.
- Top picks and comparison sets are grounded in explicit buyer-fit logic.
- Shortlist reactions become ranking input for the next recommendation cycle.
- Listing suggestions stay consistent across chat, leads, and listings workspace.
Confidence without false precision
- Missing requirement fields are surfaced instead of hidden.
- Hard constraints are never overridden by engagement signals.
- Managed agent explains and refines after deterministic retrieval.
- Outbound follow-up remains approval-gated.
- Normalize lead context into a search profile using requirements, notes, and shortlist interaction signals.
- Apply hard filters, then rank surviving listings with soft preference scoring and shortlist-fit weighting.
- Prepare recommendations, rebuttal notes, and follow-up drafts using match explanations and visible profile gaps.
Bring one buyer profile and test the ranking live
Use a real lead scenario to see how hard filters, soft scoring, shortlist signals, and missing-data prompts shape the final match set.