Dwelly raises £69M to buy and modernize U.K. letting agencies with AI
London-based Dwelly secured £69 million ($93 million) to speed up its acquisition of independent letting agencies and streamline operations with AI. The round includes £32 million in equity led by General Catalyst, with Begin Capital and S16VC joining, plus a £37 million debt facility from Trinity Capital.
Founded by former Uber and Gett operators, the company keeps local brands intact, folds in its operating system, and centralizes the back office. The aim is simple: buy customer bases, compress admin work, and raise service quality without bloating headcount.
The market gap they're attacking
The U.K. lettings market is highly fragmented: around 20,000 firms manage roughly 5.5 million rental properties. By Dwelly's estimates, that represents more than £100 billion in annual rent and £10 billion in agency commissions-yet the top 100 firms control less than 30% of the market.
That fragmentation makes organic growth slow. Tenancies run about three years on average, so landlords rarely switch agents. "Structurally, finding a landlord is pretty hard organically," said cofounder and chief product officer Dan Lifshits. The roll-up model is, in his words, "tactically buying a customer base."
How the model works
Dwelly has acquired 10 agencies so far. It keeps the local team and brand, then plugs in its AI-driven workflows across lettings and property management.
Rather than sell software to third parties, the team chose to own the full P&L. "If you sell the software, you get 1.5%-2% of the P&L of the agency. If you own an agency and deliver the full end-to-end service, you get 100% of the P&L," said CEO Ilia Drozdov.
What the AI actually does
- Lettings: Automates tenant comms, background verification, and offer management. Where a typical branch might see one or two offers, Dwelly says it averages 10 validated offers within three days.
- Leasing speed: Time to find a tenant cut from roughly three weeks to under two.
- Property management: 24/7 chatbot triage, automated follow-ups with contractors, and tighter SLA tracking. Reported resolution times down from ~50 days to ~20, with a target of 10.
The goal isn't to replace staff. Most branch employees sit behind screens managing email and calls-prime targets for automation. "People, with all the good intentions to deliver an outstanding service, are underwater with all the admin stuff," Drozdov said. "We help to reduce that workload and let them do what they really love to do."
Funding, scale, and targets
Dwelly now manages more than 10,000 properties-placing it among the U.K.'s top 15 letting agencies in under two years-and says it oversees over £200 million in gross rent. Headcount is just under 300 today, with roughly 40 in product, engineering, and analytics; leadership expects to exceed 1,500 as acquisitions close.
The target for year-end: 50,000 properties under management, which would put the company in the U.K.'s top five by size. Longer term, the team is eyeing Western Europe (with France likely first) and, later, the U.S.
Investor view
General Catalyst partner Zeynep Yavuz framed the bet as converting thousands of analog, branch-level processes into scalable software that improves tenant experience, landlord economics, and agency efficiency at the same time.
Practical takeaways for brokers and property managers
- Acquisitions as demand gen: If churn windows are rare, buying portfolios can be cheaper and faster than marketing to cold landlords.
- Standardize first, then automate: Define one workflow per process across branches before you plug in AI-especially for viewings, referencing, renewals, arrears, and maintenance.
- Measure what matters: Track offers per listing, time-to-let, renewal rate, arrears aging, first-response time, and days-to-resolution for work orders.
- Keep the local brand: Community trust is hard to earn and easy to lose. Centralize the back office; keep front-of-house local.
- Use AI where humans stall: Tenant comms triage, contractor follow-ups, ID/credit checks, and SLA nudges are low-risk, high-leverage starters.
- Change management beats tooling: Train branch staff on new playbooks, not just new software. Assign one owner per KPI.
If you're building your own "AI-enabled roll-up" playbook
- Pipeline: Target agencies with clean books, recurring management fees, and repeatable processes. Avoid edge-case heavy portfolios at the start.
- Integration: Migrate data day one, freeze bespoke tools, and ship a standard ops stack across email, CRM, ticketing, and payments.
- Compliance: Bake privacy, AML/KYC, and data retention into workflows-especially around referencing and payments.
- Operating cadence: Weekly KPI review, monthly process audits, and quarterly portfolio pruning to keep quality high.
Want a structured path to implement similar workflows in your shop? Explore the AI Learning Path for Real Estate Brokers.
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