Monthly masterminds, weekly updates, and networking with coliving operators worldwide.
We’ll build the agent for inquiry triage, maintenance routing, dynamic pricing, community matching, renewal predictions, investor reporting, lead qualification, compliance tracking.
Custom AI agents for coliving operators. Built around your stack, owned by you, priced at token cost. No platform lock-in, no per-seat SaaS tax, no AI-shop-learning-coliving-on-your-budget.
/ 002 · what you don’t pay for
You pay for the build, the tokens, and (optionally) monthly optimization. That’s the whole stack.
Last updated: May 2026
in coliving operations & technology
coliving operators advised
property management systems built from scratch
countries we've shipped systems into
Every coliving operator runs the same hidden tax. A community manager spends 40% of their week on inquiry triage and ticket routing. A founder spends 8 hours a month stitching the investor report from four systems. A portfolio manager looks at occupancy quarterly because weekly reporting is too expensive to produce manually. The whole industry under-invests in operations leverage because the obvious answer, “hire more people”, doesn’t scale at coliving’s margins.
AI agents are the leverage. Not the “ChatGPT-wrapper-on-your-FAQs” kind, those have been around for two years and don’t move the needle. We mean agents that read your PMS data, talk to your residents in their language, write to your CRM, dispatch your vendors, and surface the patterns your team is too busy to see. The ROI is measured in hours saved, RevPAB points gained, and renewals recovered.
The bottleneck has stopped being “can AI do this?” The bottleneck is “who knows coliving operations well enough to point AI at the right problem?” That’s where we come in.
Already exploring AI more broadly? Read our overview of AI & automation in coliving for the educational deep-dive, this page is for when you’re ready to actually build something.
We’ve grouped what we build into four families. Inside each, the agents are custom, built around your stack, your data, your decisions.
Agents that handle the daily ops load, inquiry triage, tour scheduling, move-in coordination, maintenance ticket routing, lease admin, deposit handling.
Agents that compress the funnel, qualify leads, schedule tours, follow up cold leads, generate listing content, monitor competitor pricing, source backlinks.
Agents that put your numbers to work, dynamic pricing, occupancy forecasting, P&L automation, investor reporting, anomaly detection on revenue and costs.
Agents that make community programming scale, matching residents, programming events, monitoring sentiment, surfacing churn signals, multilingual support.
/ 003 · use cases
Concrete, not theoretical. Every one of these has been built or scoped with a real operator. Pick the one that’s costing you the most right now, we’ll start there.
The problem
Operators with 100+ inquiries per month lose 30-50% of leads to slow response times. After-hours and weekend leads are the worst.
What the agent does
A 24/7 agent that answers availability, pricing, and policy questions, books tours directly into your calendar, and escalates only the qualified ones with full context.
Typical impact
Response time from hours to seconds; lead-to-tour conversion up 25-40% in the first 60 days.
The problem
Resident maintenance tickets land in a shared inbox, get misclassified, and bottleneck on whoever is online. Emergency tickets sit overnight.
What the agent does
An agent that reads the ticket, classifies urgency (emergency / high / standard), routes to the right vendor by trade and location, and confirms with the resident, all in under a minute.
Typical impact
Time-to-dispatch drops from 6-24 hours to under 5 minutes for emergencies; vendor SLA disputes drop sharply.
The problem
Most operators set rent quarterly. By the time you re-price, you've left 5-15% of revenue on the table, or stalled occupancy at over-priced rooms.
What the agent does
An agent that pulls daily occupancy, competitor prices, lead velocity, and seasonality, then proposes per-room price adjustments with a confidence score. You approve or let it auto-apply within guardrails.
Typical impact
RevPAB lift of 8-15% within 90 days; occupancy stabilises within target band.
The problem
Operators discover residents are leaving 30 days before move-out, when the renewal play is already lost.
What the agent does
An agent that scores every active resident weekly using ticket history, payment patterns, community engagement, and survey responses. Flags at-risk residents 60-90 days early with suggested retention plays.
Typical impact
Renewal rate up 8-15 percentage points; ~$4,200 in turnover cost saved per recovered resident.
The problem
Random room assignments produce conflict-prone houses. Operators who match by hand can only do it for the first few; portfolios over 50 beds fall back to chance.
What the agent does
An agent that runs structured intake (interests, schedules, lifestyle), scores compatibility against current residents, and proposes optimal placement, with operator override.
Typical impact
Conflict tickets drop 30-50%; average tenure lengthens; NPS climbs 10-15 points.
The problem
Monthly board decks eat 6-10 hours of operator or analyst time, and 90% of it is data copy-paste from PMS, GA, and the bank.
What the agent does
An agent that pulls from PMS + payments + analytics on a schedule, generates the standard board deck (occupancy, RevPAB, NOI, lead funnel, runway), and ships it to the investor portal or email list.
Typical impact
10+ hours per month back, every month; investors get reports on day 1, not day 12.
The problem
International residents (LatAm, Southern Europe, Asia) prefer their native language. Operators staff for English and lose conversion at the language boundary.
What the agent does
A concierge agent fluent in EN/ES/PT/DE/FR/HI (or any locale you specify) that handles inquiries, FAQs, and onboarding across channels with full operational context.
Typical impact
International lead conversion up 20-40%; resident satisfaction lifts across non-English-native cohorts.
The problem
Most operators have a 1,000-10,000 contact list of cold leads that go untouched for months. CRM nurture campaigns die after 2-3 emails.
What the agent does
An agent that segments cold leads by intent stage and runs personalized multi-touch sequences across email + WhatsApp, handing off hot replies to your team in real time.
Typical impact
5-12% of cold leads reactivate; average CAC drops because the leads were already paid for.
The problem
Lease renewals, deposit returns, and compliance deadlines (HMO licence renewals, fire-safety certs, insurance) slip through the cracks. Each miss is a fine waiting to happen.
What the agent does
An agent that tracks every regulatory and contractual deadline per property, triggers reminders, drafts notices, and surfaces compliance gaps before they bite.
Typical impact
Near-zero missed renewals or compliance lapses; one $5,000 averted fine pays for the agent for a year.
The problem
Slack and WhatsApp resident channels are full of churn signals, frustration about wifi, cleaning, neighbours, that operators read too late.
What the agent does
A monitor agent that classifies signals from your resident channels into themes (infrastructure, community, comms, etc.), trend-graphs them, and pages you when a theme spikes.
Typical impact
Operator time spent firefighting drops 30-50%; complaints get fixed at the cluster level rather than the ticket level.
The problem
Sourcing local vendors (cleaners, handymen, F&B partners) for a new market is slow manual work. Most operators reuse known names instead of finding better fits.
What the agent does
An outreach agent that profiles vendors against your spec, drafts intro emails, runs reference checks, and surfaces a ranked shortlist with rate quotes.
Typical impact
Vendor sourcing time drops from weeks to days; better vendor fits at lower rates.
The problem
New community managers take 4-8 weeks to be productive because operational knowledge lives in heads, Slack threads, and outdated Notion pages.
What the agent does
An internal-facing agent trained on your SOPs, past tickets, and resident handbook. Staff ask 'how do I handle X' and get the right answer with the right context.
Typical impact
Onboarding window halved; consistent operational quality across properties.
Don’t see your workflow? That’s the point, every coliving operation has its own. The 12 above are starting points. Tell us the workflow on a 30-min discovery call , we’ll come back with whether AI is the right tool, and if it is, what it costs to build.
Four phases. No mystery. Discovery to deployed in 3-10 weeks depending on scope.
We sit with your team, usually 1-2 working sessions, to map the workflow you want to automate, the systems it touches (PMS, CRM, comms, IoT, accounting), and the decision points where a human stays in the loop.
Deliverable: Workflow blueprint + agent scope document + rough cost estimate.
We choose the model (GPT-5, Claude, Llama on-prem, driven by your data sensitivity and cost target), design the integration plan, and define the guardrails. You see the architecture before we write a line of code.
Deliverable: Architecture doc, model + tooling choices, security & data plan.
We build in 2-6 weeks depending on scope. We pilot on a single property or a single team, instrument every interaction, and iterate weekly with your real operators in the room.
Deliverable: Working agent, instrumentation dashboard, weekly iteration log.
We deploy portfolio-wide with monitoring, alerting, and a kill switch. We optimize prompts, tool calls, and routing monthly based on real production traffic. You own the agent, the data, and the IP.
Deliverable: Production-grade agent, monitoring stack, monthly optimization log.
Three engagement tiers. No per-seat SaaS subscriptions. No token markup. You always know what the next dollar pays for.
Operators with one clear workflow to automate (lead triage, maintenance, reporting)
What you get
1 agent, 1 workflow, full integration with up to 3 systems
Timeline
3-5 weeks from kickoff
Cost
Build: from $4,500. Tokens: pass-through at cost (typically $50-$400/month per agent).
Scope this tierOperators bundling 3-5 agents covering inquiry, maintenance, renewal, reporting
What you get
3-5 agents, shared knowledge layer, unified dashboard, 5+ system integrations
Timeline
6-10 weeks from kickoff
Cost
Build: from $14,000. Tokens: pass-through at cost (typically $300-$1,500/month total).
Scope this tierMulti-property operators (50+ beds) with portfolio-wide AI strategy
What you get
Full agent suite, custom integrations, dedicated optimization, monthly strategy reviews
Timeline
Ongoing engagement; first agents live in 4-6 weeks
Cost
Custom. Build retainer + monthly optimization. Tokens at cost.
Scope this tierTokens are the unit AI providers charge by. A typical resident inquiry costs $0.01-$0.05; a maintenance classification costs under $0.01; an investor report compile costs $0.10-$0.40. We pass these costs through at the exact rate we get from the provider, no markup. Most operators see total monthly token costs of $50-$1,500 across their full agent stack, depending on volume. If your math changes (volume spikes, model upgrades), we tell you before it changes your bill.
We build against your existing systems, not a parallel platform you have to migrate to. If it has an API or a webhook, we can integrate.
JumboTiger (EC editor's pick, coliving-purpose-built, AI pre-wired), HostAway, Cloudbeds, Mews, Guesty, Hospitable, your custom PMS, and any system with a REST API or webhook
HubSpot, Salesforce, Pipedrive, Zoho, Notion, plus any spreadsheet or database you'd like the agent to read or write
WhatsApp Business, Slack, email (Gmail/Outlook), SMS via Twilio, Intercom, Crisp, in-app chat
Smart-lock platforms (RemoteLock, igloohome, August), thermostats, sensor stacks, energy meters
Stripe, Xero, QuickBooks, Wise, bank-feed integrations for reconciliation and payment workflows
GA4, Looker Studio, Metabase, Sheets, custom data warehouses (Postgres, BigQuery, Snowflake)
Custom or in-house systems? We’ve built integrations into 5+ proprietary PMSs ourselves. Send us your stack list and we’ll come back with an integration plan in the discovery call.
Most AI shops are great at AI and learning coliving on your budget. Most coliving consultants are great at strategy but outsource the technical build. We’re both: 11+ years operating and consulting coliving (60+ operators, 14+ countries), and 5+ property management systems built from scratch. We don’t start the discovery call by asking what coliving is.
That matters because the gap between an AI agent that works and one that doesn’t is rarely the AI itself, it’s the operational context. The agent that’s great in a demo but terrible in production usually fails on three things: it doesn’t know the resident lifecycle, it doesn’t know the operator’s escalation rules, and it doesn’t know which integrations actually have clean data. We’ve been on the operator side of all three.
What you get in the discovery call: an honest assessment of whether AI is the right tool for the workflow, a rough cost estimate within the call, and a list of two or three other operators we know who’ve solved (or tried to solve) the same problem. No sales pitch, no NDA gates.
Single-agent build timeline. Multi-agent bundles run 6-10 weeks; portfolio programmes are ongoing.
We map the workflow, surface the data sources, and agree on whether AI is the right tool. You get a rough cost estimate by the end of the call.
Detailed scope doc, integration plan, model and tooling choices, security review. You sign off before we write code.
We work on a private repo with weekly demos. By end of week 2 you usually have a working prototype against staging data.
Live agent on a single property or team. Real traffic, instrumented, with kill switch. We iterate based on what residents/operators actually do.
Prompt tuning, guardrail adjustments, integration polish. We measure against the success metrics defined in discovery.
Roll out to remaining properties with monitoring + alerting. Handoff documentation, monthly optimization schedule (optional), or full handoff if you want to take it in-house.
Each of these is the “manual” version of an agent we can build for you. Run them, see the value, then we automate.
What an inquiry-funnel agent could fix.
Manual version of a dynamic pricing agent.
What a portfolio-monitoring agent would track weekly.
Inputs that feed a community-matching agent.
Inputs that feed a sentiment-monitoring agent.
What a marketing-ops agent could automate end-to-end.
Every interaction with an AI agent, a resident message, a maintenance classification, a price recalculation, consumes 'tokens' (the unit LLMs charge by). A typical resident inquiry costs $0.01-$0.05 in tokens. We pass these costs through at the rate we get from the model provider (GPT-5, Claude, etc.), no markup. Most operators see token costs of $50-$1,500/month across all their agents combined, depending on volume.
Build: a one-time fee for designing, building, integrating, and deploying the agent (from $4,500 for a focused single-agent build; $14,000+ for a multi-agent bundle). Operating: token costs (pass-through, no markup) + an optional monthly optimization retainer if you want us to keep iterating. Many operators run agents independently after the initial build with only token costs going forward.
Yes, fully. You own the agent code, the prompts, the integration glue, and all the data flowing through it. We hand over the repository at handoff. No platform lock-in, no proprietary runtime you have to keep paying for. If you ever want to take the agent in-house, you can.
We use enterprise-tier LLM providers (Anthropic, OpenAI, Google) on data-processing agreements that prohibit training on your data. For especially sensitive workflows (tenant PII, payments, compliance), we can deploy open-source models (Llama, Mistral) on your own infrastructure or a private cloud. Every agent ships with audit logging, role-based access controls, and a kill switch.
Every agent is built with guardrails: confidence thresholds, scope limits, and human-in-the-loop checkpoints for high-stakes decisions (price changes above a threshold, deposit returns, lease terminations). We instrument every interaction so you can audit reasoning, prompt history, and tool calls. When mistakes happen, and they do at first, we tune fast, usually within 24-48 hours.
Single-agent builds: 3-5 weeks. Multi-agent bundles: 6-10 weeks. We usually have a working prototype in week 2 and pilot it on a single property in week 3-4. Full portfolio deployment is gated on your operational readiness, not our build speed, most operators want a 2-3 week pilot before going wide.
We choose the model that fits the workflow, usually GPT-5 / Claude Opus / Sonnet for high-reasoning agents, smaller models (Haiku, GPT-5-mini, Llama) for high-volume, low-complexity tasks. If you have an existing API contract (e.g., enterprise GPT-5 commitment), we'll use it. If you need on-prem or private cloud, we'll architect for that.
No. Most operators we work with have zero in-house AI expertise. We design every agent to be operator-runnable: clear dashboards, plain-language settings, and a 'pause' button. If you do have a technical team, we hand them the codebase so they can extend without us.
Yes, and we strongly recommend it. Most successful AI agent rollouts in coliving start with one well-scoped agent (usually inquiry triage or maintenance routing), prove ROI in 60-90 days, then expand. We've seen 'big-bang' rollouts struggle; phased rollouts almost always succeed.
Anything that's truly judgement-heavy with low volume, major contract disputes, eviction decisions, brand-defining content creation. We'll be honest in the discovery session: if an agent isn't the right tool, we'll tell you. The goal is leverage where it works, not AI for AI's sake.
Depends on the agent. Inquiry triage typically pays back in 60-90 days through faster lead response + reduced staffing load. Renewal predictors pay back on the first 1-2 recovered renewals (~$4,200 each). Dynamic pricing usually pays back in the first month from RevPAB lift. We model expected ROI per agent in the discovery session and re-measure quarterly.
Two reasons. First, we know coliving, the workflows, the systems, the resident dynamics, because we've operated and consulted 60+ coliving spaces ourselves. A generalist shop will spend the first 2 months learning what we already know. Second, we've built 5+ property management systems from scratch and run $2M+ of marketing for coliving brands. We're not learning coliving on your budget.
Trusted by 60+ operators & proptech companies
30-minute discovery call. No NDA. No slide deck. We come back with a scope and a rough cost estimate. You decide whether to proceed.
