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Production AI engines: agents, voice, vision, RAG, workflow automation, and predictive models — designed, deployed, and maintained end-to-end.

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Real estate firms, property management companies, and operations-heavy businesses worldwide. Active clients across Dubai, London, Toronto, and Pakistan.

How long from idea to deployed engine?

Proof of concept in 1–2 weeks on your real data. Production engine deployed in weeks 3–6. Self-healing systems and team training included.

How does pricing work?

30-minute discovery call is free. Written audit follows in 1–3 days. Build is project-priced; ongoing partnership is a flat retainer with unlimited servicing.

Do you transfer code and IP?

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What stack do you build on?

OpenAI, Anthropic Claude, Vercel, Supabase, Next.js, React, Tailwind, Stripe, Linear, and GitHub — production-grade tools the team trusts.

Property management · London, UK

Updated 2026-05-18

Regal Realms: tenant-comms voice agent and payout reconciliation for London property management.

Regal Realms London case study: tenant communications voice agent, Booking + Airbnb payout reconciliation engine, and AI-assisted back-office operations.

Impact scorecard

post-implementation

Client

Regal Realms

Region

London, UK

Focus

AI implementation

Problem

Regal Realms runs a London short-let portfolio with bookings flowing through Booking.com, Airbnb, and direct channels. Two operational pains were eating margin. First, tenant and guest communications — check-in instructions, code requests, maintenance issues, late-night lockouts — were arriving 24/7 across email, WhatsApp, and platform inboxes, with no consistent response time. Second, reconciling platform payouts against the company's bank statements was a multi-day monthly task: Booking.com remits net, Airbnb remits per-booking with rolling holds, bank statements arrive in a different shape, and historical exceptions ran into the thousands of pounds at risk.

Solution

consultance.ai shipped two production systems. The tenant-comms voice agent fields inbound guest and tenant messages across all channels, answers the common 80% (check-in codes, wifi, parking, late check-out, basic maintenance), and escalates only the genuine exceptions to a human with full context. The payout reconciliation engine auto-pulls Booking.com and Airbnb statements from Gmail, ingests platform CSVs and bank feeds into a shared Postgres database (over Tailscale, with row-locked OAuth tokens so multi-host runs can't race), and reconciles every payout line against bank credits. Exceptions surface in a dashboard with money-at-risk totals and a P1/P2/P3 triage. The pipeline also writes through to Xero so the books stay clean without manual journals.

Proof points

London property management company with public presence on web and Instagram.
Reconciliation engine ingests Booking.com + Airbnb payouts and matches against bank statements end-to-end.
Shared Postgres + row-locked OAuth so multi-host runs don't race the Xero token refresh.
Voice agent handles tenant and guest comms in production across email, WhatsApp, and platform inboxes.
P1/P2/P3 exception dashboard with money-at-risk surfaced to operations.

View public reference

Outcomes

Booking + Airbnb payouts reconciled automatically against bank statements

Tenant + guest comms handled 24/7 without manual routing

Back-office exceptions surfaced with prioritized money-at-risk view

Xero write-through eliminates manual journal entries

Multi-host runs coordinated without OAuth token races

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