Real Estate Software Solutions

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

🚨 Meta Update: You Can Now Book Appointments Directly From Facebook Lead Ads This is one of the most useful lead generation updates Meta has introduced in a long time. Here is how it works: A scheduling widget from a supported booking provider appears on the thank-you page immediately after a lead submits an Instant Form. Users can view available time slots, select a date and time, and confirm an appointment without ever leaving the Facebook app. Contact details from the form are automatically transferred to the booking widget, so leads never have to re-enter their information. Setup requires no coding. Advertisers simply paste a scheduling link, select "book time" as the call to action, and publish. The system auto-detects the calendar provider automatically. Supported platforms right now: • Calendly ✅ • HighLevel ✅ • HubSpot coming in early August 🔜 Why this matters for lead generation advertisers: The gap between form submission and appointment booking has always been a challenge for service-based advertisers. With embedded appointment booking, you eliminate that gap entirely. The booking experience appears immediately after form submission, right when the lead's interest is at its peak. For anyone running lead ads for clinics, salons, real estate, legal services, or any appointment-based business, this removes the biggest conversion killer, the delay between lead capture and actual booking. Full global availability expected in October 2026.

Continuous Agent Optimization: Agents that launch with great accuracy don't always stay that way. It's a challenge we hear about from developers. Our team has been working closely with customers running agents in production, and one pattern keeps coming up: agents that perform well at launch don't stay that way. Models evolve, user behavior shifts, and quality quietly degrades in ways that are hard to spot and harder to fix. Most teams are still relying on manual cycles of reading traces, guessing fixes, and deploying without systematic validation.       🚀 We solved that with new recommendations, batch evaluations, and A/B tests now available in preview on Amazon Bedrock AgentCore. Customers like NTT DATA and Nomura Research Institute are already seeing the impact. As Masashi Shimizu from Nomura Research Institute put it: "What took weeks of manual prompt iteration is now a repeatable cycle with AgentCore."     Here's how these capabilities work to complete the quality improvement cycle for agents:     🟠 The recommendations capability analyzes production traces and evaluation outputs generated by AgentCore to create optimized system prompts and tool descriptions tailored to your specific workload.    🟠 Batch evaluations test recommendations against a pre-defined dataset and report aggregate scores, catching regressions on cases you know matter. Teams can wire this into CI/CD pipelines, so no configuration change reaches production without passing their known-good cases.       🟠 A/B testing lets teams run controlled comparisons between agent versions through AgentCore Gateway, splitting live production traffic at the percentage you configure and reporting results with confidence intervals and statistical significance. When the data gives you confidence, promote the winner.     Every recommendation requires your approval before it ships.     Here’s the detailed breakdown on these new capabilities for optimizing agent performance: https://lnkd.in/gdrAixnc

The most dangerous clauses in vendor contracts aren’t the ones you fight over. They’re the ones you skim past—(em dash mine 😑) the “standard” terms that seem harmless until they explode. Just ask Morgan Stanley. Overlooked contractual gaps turned a vendor’s mishandling of client-data-bearing equipment into hundreds of millions in fines, settlements, and penalties for Morgan Stanley. I have identified some top of mind examples: #1: The Subcontracting Black Hole Most vendor contracts include innocent-looking language like: "Vendor may engage subcontractors as necessary to perform services." The problem: You have zero visibility into who's actually handling your sensitive data or critical operations. What Morgan Stanley missed: Their vendor subcontracted the actual data destruction to an unqualified third party. The fix: • Require prior written approval for all subcontractors • Mandate the same security/compliance standards flow down • Include right to audit subcontractors directly • Cap subcontracting to specific, pre-approved functions #2: The Liability Cap Loophole Standard cap: "Vendor's liability limited to fees paid in preceding 12 months." The hidden trap: This covers the vendor's mistakes but not the regulatory fines, customer lawsuits, and reputational damage you'll face. What to negotiate: • Separate caps for different types of damages • Higher caps for data breaches and regulatory violations • Unlimited liability for gross negligence and willful misconduct • Minimum insurance requirements that match your actual risk exposure #3: The Termination Cost Surprise Innocent clause: "Upon termination, vendor will assist with transition for 30 days." The trap: No mention of data extraction, migration costs, or knowledge transfer requirements. Real example: A SaaS company switching CRM vendors discovered "transition assistance" meant read-only access to export screens. Manual data extraction cost $47K in consulting fees. Protection strategies: • Define data export formats and timelines • Cap termination assistance fees • Require knowledge transfer documentation • Include escrow provisions for critical operational data #4: The Change Order Cash Grab Standard language: "Any modifications require mutual written agreement." The hidden cost: No controls on pricing for change orders or scope creep. Pattern I see: Vendors lowball initial proposals then recover margins through change orders priced at 200-400% markup. The armor: • Cap change order pricing as percentage of original contract value • Require detailed justification for scope changes above set thresholds • Include right to third-party validation for major change orders • Build in quarterly spend reviews with automatic triggers The point is, most "standard" vendor contracts are written to protect vendors, not you. Don't let your "standard" vendor agreement become someone else's cautionary tale. Dig deep. #VendorManagement #ContractReview #RiskManagement

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

Hundreds of tech tools. Countless logins and passwords. Real estate owners are overwhelmed. With hundreds of technology tools at their fingertips, multifamily owners are increasingly hesitant to embrace yet another point solution with: • Another login and password • Another annual fee • Another data stream • Another training requirement Three categories are emerging with the solution: 1. The PMS app store: • Open ecosystems for verified property tech solutions • Developer-friendly APIs for seamless tool integration • Curated selection of solutions across maintenance, smart devices, and operations AppFolio's Stack offers 50+ verified integrations, balancing choice with quality control. 2. Vendor management platforms: • Centralized oversight of all property vendors (tech and physical) • AI-powered contract analysis to identify cost overlaps • Crowdsourced reviews and recommendations for vendor selection Revyse helps owners consolidate vendors and typically saves $200/unit/year through spend optimization. 3. Integration hubs: • Workflow automation across property management tools • No-code platform for connecting multiple solutions • Custom branded resident experience builder Venn connects 150+ applications, letting operators create automated workflows and white-label experiences. But we’re still in the early days. For context: While AppFolio offers 50 curated tools, HubSpot has 1,700 and Salesforce has 5,100. The future isn't about more apps. It's about making existing tools work better together. What's your experience with PropTech fatigue? Share below. For the full deep dive, check out the link in the comments.

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

If your agent runs for 10 minutes, you need to know what happened at minute 3. High-performing teams don’t just log outputs. They trace steps. For long-running agents, you need: 🔍 Step-level execution logs 🧠 Intermediate reasoning checkpoints 🛠 Tool invocation metadata 📊 Token consumption visibility ⏱ Latency per action Without tracing: 1. You can’t debug hallucinations. 2. You can’t explain decisions. 3. You can’t detect drift. 4. You can’t prove compliance. Observability turns agents from magic into machinery. If your only metric is “final output quality,” you’re blind to systemic fragility. Would you ship a distributed system without tracing? Then why ship agents without it? #AIEngineering #Observability #AIOps #AgentSystems #Tracing #ProductionAI #SystemReliability #ModelMonitoring #LLMOps #EnterpriseAI

In countries where trust takes longer to build (as is the case of most Asian markets), the most effective approach I’ve found is to bring real business to the table without expecting anything in return. If someone seems valuable, introduce them to a client, a partner, or an investor. Don’t ask for a favor or a cut. Just deliver. If they choose to reciprocate, that’s a green flag. If they don’t, that’s fine too because the point isn’t immediate return. It’s accelerating trust. All other forms of relationship-building, e.g., dinners, drinks, small talk, are way less valuable in comparison to this. Nothing builds goodwill like showing you can make people money while operating with integrity.

Everybody wants to talk about using AI Agents, but how many understand what it takes to truly build and maintain them? AI Agents, like any ML model, requires monitoring post-deployment. But AI Agents are different than traditional AI models in that many industry AI Agents are built using APIs trained by third party companies. This means monitoring both during and after deployment is critical. You'll need to monitor things like usage relative to the rate limit of the API, latency, token usage, and how many LLM calls your AI Agent makes before responding. You'll even need to monitor failure points at the API level as bottlenecking and region availability can bring your entire AI solution down. Tools like Splunk, DataDog, and AWS CloudWatch work well here. They help you track these metrics and set up alerts to catch issues before it affects your AI Agent build. LLM usage costs take far too many companies by surprise at the end of a POC. Don't be that company. Monitor closely, set thresholds, and stay on top of your AI Agent's performance and costs.