Valuation models trained on your real portfolio, deal-close probability scoring, structured extractors from listings and spec sheets, churn prediction and incident classification — trained on your data, not on portal averages.
Portal valuations and generic AVMs assume an average market. Your portfolio has a specific mix of typologies, areas, finish levels and target buyers — and the difference between closing in six weeks or six months sits exactly there. Same story with off-the-shelf CRM lead scoring: it treats a real buyer the same as a Sunday browser.
We build models trained on your history: an AVM tuned to your asset type, deal-close probability scoring per lead, structured extraction from listings and spec sheets, tenant-churn prediction and automatic incident classification by severity. Integrated with HubSpot, Salesforce or Pipedrive without replacing anything.
Valuation model trained on your closed deals, real comparables and non-standard features (orientation, views, refurb quality). Outputs a confidence interval and the supporting comparables — not a single point estimate.
Model predicting the probability a lead will close within 90 days, using portal behaviour, message history, sought typology and financing signals — not only fields the agent typed into the CRM.
Reads spec sheets, dimensioned plans and portal listings and returns structured JSON with surfaces, finishes, fittings and key differentiators — ready for CRM and for consistent commercial materials.
Model trained on historic move-outs, estimating non-renewal probability at 60 days by combining incidents, late payments, communication patterns and local market data — to enable a real retention window.
Reads incoming incidents on any channel, classifies them by trade (plumbing, electrical, appliances…) and severity (critical, urgent, schedulable), and proposes SLA and supplier — ready to route to the property manager.
Listing prices are set using portal AVMs and agent judgement — 8–12% deviation from closing price. The commercial team works every CRM lead the same way; close rate around 4%. Tenant move-outs are spotted only when notice arrives, with no time to retain. Incidents are classified manually each morning.
The custom AVM cuts listing-price deviation to 3–4% and lets the team adjust portfolio pricing with data. Lead scoring focuses commercial effort on the top 20% — close rate climbs to 9%. The churn predictor flags 78% of move-outs 50–60 days ahead, opening a real retention window. Incidents arrive classified and routed to the right property manager.
Mid-market CRM with broad APIs — a natural fit for sales agents and lead enrichment.
CRM with strong adoption among Spanish SMBs — automations and agents at a contained cost.
Email, calendar and SharePoint as channel and context — triage, drafting and RAG over your inbox and files.
It’s the first question we answer. We audit your history — number of closed deals, distribution by typology and area, quality of closing data — before promising any uplift. If the data isn’t there, we say so: sometimes the answer is to enrich with public and licensed sources, sometimes it’s starting with a simpler model and retraining as history grows. No fluff.
The model is an internal pricing, comparables and decision-support tool — it doesn’t replace a lender-approved appraisal for mortgage origination. We document the dataset, the features and model performance, and keep an audit log of every valuation. If you later want to move toward a regulated AVM, we start from the European framework for automated valuation models.
No. We connect to HubSpot, Salesforce, Pipedrive, Idealista and the most common build-to-rent PMSs via API or intermediate connector. Predictions are written into properties of the existing CRM so the commercial team sees them in their usual flow, with no new tool to open.
A model trained on a rising market stops being valid in a flat or falling one. So we set monthly retraining with new closings and continuous drift monitoring against actual market prices. If error rises above the threshold, the model is retrained or, if the magnitude justifies it, we revisit features and architecture. It’s part of the service, not an extra.
We design for privacy from the start, human control, traceability, usage limits, permissioning and documentation. For sensitive processes, we help assess risk and applicable obligations under GDPR and the EU AI Act.
Every engagement is led personally by one of the partners. If there's a fit, you get a personal first read of your case within one business day — not a canned demo.
