Tian Lan

Engineering teams produced pilots but slow enterprise deals, partly because engineers feared AI was coming for their jobs.

• Sales teams were unpleasant to work with, and the sales-enablement software market was already strong.
• Healthcare offered the opposite profile: broken systems held together by collaborative people, slow AI adoption, and users who said they wanted AI and would pay for it.
• Their conclusion: money follows improving people’s lives.

In a one-year stealth retrospective, Tian Lan describes the sales market as “very bro-ish, competitive, and performance-driven” and says healthcare workers “are so nice and pleasant to work with,” giving the team “a head start in a healthcare niche” they would not have had elsewhere.

Before moving into healthcare, the team spent a single year building for two other targets: engineering teams, then sales teams. The post does not say what the product did for either group.

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They argue that domain knowledge, real-world data, and relationships with the people served can matter as much as chasing each new AI trend, and that cutting-edge AI combined with deep vertical expertise is where the next wave of impactful companies will emerge.

• Their test for the agent economy is whether it improves human lives; otherwise, it is another tech bubble.
• They name caregiving as one of the most overlooked infrastructure challenges.

The accelerator onboarding post states, “If the AI agent economy is to succeed, it should ultimately improve human lives,” and closes by asking for AI ops agents, AI QA agents, and AI training infrastructure. The team did not get into YC but uses YC AI tools.

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AI-written sourcing outreach can lack authenticity, and a large share of candidates never get seen under AI filters.

• Tian Lan argues for broader, multi-dimensional screening that surfaces high-potential people and adds human touch, rather than rejecting anyone who does not clear an automated pass.
• The approach connects to the job simulations Peak Mojo offers talent teams.

The New York Tech Week recap credits Byron Kendal with teams filling roles in as little as 14 days by automating initial screening, and Stacy Johnson Robnett and Lisa Moore with raising concerns that AI sourcing outreach lacks authenticity. It highlights “the 90% who never get seen” and contrasts a “just reject them” approach with Peak Mojo’s use of AI to engage that group through multi-dimensional screening and a dynamic leaderboard.

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A guest on the same panel framed the shift as a consolidation into four roles.

• Lan says this could move hiring away from managing a hundred open positions at once.
• Companies will need new ways to spot real doers in an AI-augmented workplace.

The New York Tech Week recap reports Byron Kendal’s “4 roles” framework — Builder, Accelerator, Sales, and Customer Success. Tian Lan’s observation about builders and growth comes from the Seattle Tech Week panel, which also covered a lean workforce, AI fluency as a hiring baseline, and openness to failing fast on AI tools.

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The work is personal: they watched their grandfather live with dementia and saw the fear, guilt, and uncertainty families carry when no one notices a loved one’s needs. That framing shapes the design: a wearable, privacy-first edge-AI system intended to give home-care agencies continuous visibility into safety, routines, and changing care needs, and help families stay connected between visits.

The Techstars Demo Day post reports deployments with senior homes and selected families, analysis of more than 2,000 hours of audio and location data, and a production build on track for the following quarter. A later post shows the first factory-made devices and describes the shift from building to deploying them with home-care partners in real homes.

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Start here to understand their decision logic before reading product or accelerator posts.

Tian Lan explains two market pivots before landing on healthcare.

• Engineering teams were interested but worried AI was coming for their jobs, so enterprise deals closed slowly.
• Sales teams were worse: “very bro-ish, competitive, and performance-driven,” and the sales-enablement software market was already strong.
• Healthcare was different: workers were collaborative, systems were broken but held together by people, AI penetration was low, and users said they wanted AI and would pay for it.
• They say no one else was doing what Care Mojo was doing, and that the niche matched their personality and desire for social impact.

For brands planning a campaign

A health-tech partner can ask Tian Lan to discuss buyer constraints and market entry, not just features. An HR-tech partner should note their critical view of sales teams and sales tooling; a recruiting-product collaboration is a better fit than a sales-enablement pitch.

Start here for the accelerator context and a concrete list of partnership needs.

Tian Lan announces joining the Techstars Workforce Development Accelerator and describes the first global onboarding in New York.

• They contrast San Francisco’s agent-tool focus with founders applying AI to healthcare, supply chains, construction, and other real-world industries.
• The program’s theme is how technology can unlock human potential.
• They say their mission is applying advanced AI to caregiving, one of the most overlooked infrastructure challenges.
• They praise cohort founders for domain knowledge and real-world data, argue the next wave of impactful AI companies will combine cutting-edge AI with deep vertical expertise, and say they are looking for AI ops agents, AI QA agents, and AI training infrastructure.
• They also note they did not get into YC but use YC AI tools.

For brands planning a campaign

AI infrastructure vendors can respond directly to the named needs in AI ops, QA, and training. Accelerator or ecosystem partners can use the caregiving mission as the hook for a collaboration, avoiding generic agent-economy framing.

Start here for the clearest mission and product description in the set.

Tian Lan uses a Techstars Demo Day post to summarize what Care Mojo built in 13 weeks: an idea turned into an AI hardware prototype, real user pilots, a manufacturing partner, and a production build targeted for Q3.

• They give a personal reason for the work: watching a grandfather live with dementia and seeing families carry fear, guilt, and uncertainty when no one notices a loved one’s needs.
• Care Mojo is described as wearable AI for in-home dementia care that helps home care agencies understand dementia behaviors, support caregivers, and act as 24/7 dementia care partners.
• The post claims deployment with senior homes and selected families, and analysis of 2,000+ hours of audio and location data.

For brands planning a campaign

A dementia-care or home-care agency partnership can focus on behavior insight, caregiver support, and family communication. A speaker invitation should draw on the dementia-care specifics; generic AI-in-healthcare talks would waste the distinctive evidence.

Start here for the manufacturing milestone and the intended real-world use.

Tian Lan shows the first factory-made Care Mojo devices, noting that three months earlier they were prototypes on desks.

• They thank team members and shift the emphasis to deployment: devices will go with home care partners into real homes to help care teams understand what happens between visits, families stay connected, and older adults live safely and independently.
• The post marks a move from building to deploying.

For brands planning a campaign

A pilot or deployment partnership can focus on real homes and care-team workflows. Hardware-focused partners can use this post as evidence of production readiness; software-only partners should expect the conversation to include devices and in-home operations.

Start here to assess their operational detail and supply-chain awareness.

Tian Lan describes working with manufacturing partners in Shanghai on product design, hardware architecture, firmware, component selection, testing, manufacturing readiness, and the roadmap to a first production batch.

• They emphasize that a hardware-enabled AI company needs tight collaboration across software, hardware, supply chain, and user needs.
• Care Mojo is described as a privacy-first edge AI system that gives families and home care agencies continuous visibility into seniors’ safety, routines, and changing care needs.

For brands planning a campaign

Hardware, manufacturing, or edge-AI partners will find concrete operational language and a production roadmap. HR-tech or software-only partners should weigh this different center of gravity when judging fit.

Start here to see their recruiting-side expertise, which predates or parallels Care Mojo.

Tian Lan recaps an AI in Recruiting panel with Brett Martin, Stacy Johnson Robnett, Byron Kendal, and Lisa Moore.

• The post cites a team filling roles in as little as 14 days by automating initial screening, notes concerns that AI sourcing outreach lacks authenticity, and describes Byron Kendal’s “4 roles” framework: Builder, Accelerator, Sales, and Customer Success.
• It raises the problem of the 90% of candidates who never get seen under AI filters, contrasting a “just reject them” approach with adding a human touch.
• At Peak Mojo, they say they let that 90% engage with AI through multi-dimensional screening and a dynamic leaderboard.

For brands planning a campaign

An HR-tech company can invite Tian Lan to speak or advise on AI screening, authenticity, and candidate experience, using Peak Mojo’s leaderboard approach as a concrete angle. The post also offers a balanced frame: automation speed alongside the risk of discarding good candidates too early.

Start here for the recruiting philosophy connected to Peak Mojo.

Tian Lan summarizes an all-women Talent Acquisition Power Panel at Pioneer Square Labs during Seattle Tech Week.

• Takeaways include a lean workforce as the new mindset, with Beth Kester-Warner saying lean means leveraging technology and retaining high performers; AI fluency as a hiring baseline, with Deb Nielsen asking whether candidates can use ChatGPT, Copilot, or other tools and stay current; and openness to experimenting with AI tools even when one fails, illustrated by Annie Rihn’s story of a failed AI platform and a CEO’s response: “I love the idea of failing fast and moving on.”
• They add that engineering plus product is morphing into “builder,” while marketing plus sales is morphing into “growth,” reshaping career ladders.
• The panel also mentions collecting favorite interview questions for AI intro conversation training and offers Peak Mojo job simulations to surface “true doer talent.”

For brands planning a campaign

An HR-tech or talent-assessment partner can use the job-simulation offer and AI-fluency framing in a webinar or advisory engagement. The post also shows how Tian Lan translates a live panel into practical hiring implications.

One post reports a hiring team filling roles in as little as 14 days through automated initial screening, while other panelists warn that AI-written sourcing outreach feels inauthentic.

• A second recap covers a proposed merge of job categories into builder, accelerator, sales, and customer success, and asks what happens to candidates filtered out before a human sees them.
• Peak Mojo's stated response is multi-dimensional screening, job simulations, and a dynamic leaderboard intended to surface high-potential people who would otherwise be discarded.

Panel recaps that name each speaker, quote or paraphrase their position, and connect the discussion to what Peak Mojo offers.

For brands planning a campaign

An HR-tech or recruiting vendor gets a ready-made debate to attach to: speed from automation versus losing good candidates to filters.

• The posts supply language a partner or co-panelist can use, including the four-role framing and the 90-percent-never-seen problem.
• A collaboration can continue an argument Tian Lan is already making in public rather than introduce a new pitch.

Examples: Source 1 · Source 2

The product aims to help home-care agencies understand dementia behaviours, support caregivers, and stay continuously aware of a person's safety and routines.

• The work is personally motivated by watching a grandparent live with dementia and by the fear, guilt, and uncertainty families carry.
• Reported milestones include analysing more than 2,000 hours of audio and location data from pilots with senior homes and selected families, and positioning home-care agencies as the primary institutional buyer.

Product-milestone posts that pair a personal story with operational details such as pilot partners, hours of data analysed, and production timing.

For brands planning a campaign

Age-tech, health-tech, and caregiving organisations can work with a concrete, mission-led narrative that names the intended buyers (home-care agencies) and beneficiaries (families and older adults). Privacy-first design and edge processing give partners a specific technical angle, such as data handling in the home, rather than a general wellness message.

Examples: Source 1 · Source 2 · Source 3

Care Mojo went from an idea to an AI hardware prototype, user pilots, and a scheduled production build during a 13-week accelerator.

• A later post announced the first factory-made devices, roughly three months after the units were prototypes on a desk.
• A trip to Shanghai with manufacturing partners covered product design, hardware architecture, firmware, component selection, testing, and manufacturing readiness.

For brands planning a campaign

This is the strongest fit for manufacturing, supply-chain, edge-AI platform, or component partners.

• The posts show an appetite for outside input on production scaling and a habit of publicly crediting named team members and partners, which suits joint technical content or a supplier story.
• Ask about fit before pursuing purely software, media, or consumer-marketing placements; the posts do not address those categories.

Examples: Source 1 · Source 2 · Source 3

The stated reasoning combines commercial headroom, cultural fit, and impact, with money described as a byproduct of improving people's lives.

The post names team members who stayed through the pivots but gives no detail on what the earlier sales-focused product did. Ask Tian Lan for more detail before describing that abandoned direction.

For brands planning a campaign

A brand considering a founder-story, go-to-market, or advisor placement gets a candid operator who publicly gives unflattering reasons for strategic decisions.

• That candour suits content about market selection and failure.
• Vendors in sales enablement or engineering tooling should weigh that the story also explains publicly why those markets were left.

Examples: Source 1

The test for the agent economy, the posts suggest, is whether it improves human lives. The posts follow a recurring shape: recap an industry panel or announce a milestone, credit named speakers, mentors, and teammates, then close with a concrete request to a specific audience.

None of the seven posts documents a paid brand partnership, sponsorship, or affiliate arrangement. They show participation in panels and accelerators, but do not say whether paid commercial formats are used. Ask Tian Lan directly about availability and terms for paid partnerships.

For brands planning a campaign

A content or event partner can expect a recap-and-lesson format with named attribution rather than a standalone product pitch.

• The positioning stresses applied AI in unglamorous verticals over agent-economy hype.
• The posts close with requests directed at customers, manufacturing partners, home-care agencies, and investors.

Examples: Source 1 · Source 2 · Source 3

Ask them to clarify their ownership or employment relationship with the business.

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Ask Tian Lan to clarify their relationship to the business.

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Tian Lan's Care Mojo material addresses home care agencies and dementia care at home. Stephen Tweed interviews founders and CEOs across the top tier of home care and argues that a caregiver-first culture determines who can grow.

Tweed writes as an educator and consultant on corporate culture, leadership retreats, and how culture is communicated to prospective employees; his posts end with open questions for his audience. Tian Lan writes as the operator of a hardware and AI company, reporting pilots, data volumes, and production timelines.

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Oberoi's exploration post examines how senior living and elder care brands position themselves, including dementia and memory care as a must-have rather than an add-on—the same buyer world Tian Lan builds for.

Oberoi is a brand strategist whose method is comparative essays and frameworks on positioning across sectors such as DTC and senior living. Tian Lan reports milestones from inside a company building its product, embedding the marketing argument in the build story rather than treating it as a separate subject.

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Tian Lan recaps panels on automated screening, time-to-hire, and the 90 percent of candidates AI filters never surface. Carlson shares lessons from building talent acquisition functions and argues that recruiting should be measured as a revenue driver.

Carlson writes from inside the hiring function about structure, workforce planning, metrics, and hiring-manager discipline.

• Tian Lan's recruiting posts recap conferences and offer vendor-framed commentary on what Peak Mojo offers.
• They are one strand among hardware and caregiving posts, not the whole body of work.

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Their posts describe several months of interviews with founders and CEOs in the top tier of the home care industry about strategic planning, leadership and board retreats, and company culture.

• They make a specific argument this audience can debate: providers are shifting from a customer-first to a caregiver-first culture because caregiver recruiting and retention, not sales and marketing, is the real growth constraint.
• Tweed also teaches corporate culture in a graduate communication and business leadership program, making them a fit for a workshop or advisory engagement with operators.
• Pick Tweed over Tian Lan when the buyer needs someone who can discuss culture, recruiting, and retention as business problems for care providers, without an AI product attached.

Tian Lan's posts do not cover AI screening, recruiting automation, or recruiting software, so a campaign demonstrating AI capability would lack an anchor.

• The writing is question-led commentary designed to prompt discussion.
• No service description, fee, or partnership format is published; ask about terms and willingness to work with a vendor.
• Ask for audience size, engagement, and composition figures before booking.

Examples: Source 1 · Source 2

Their posts offer a five-part framework a hiring vendor can borrow directly: structure enables speed, workforce planning beats requisition management, metrics should drive decisions and show results, hiring managers are the biggest variable, and employer brand starts inside the business.

• A second post makes a revenue case for recruiting: hiring speed affects revenue velocity, time-to-productivity affects customer outcomes, and requisition count, time-to-fill, and cost-per-hire are necessary but insufficient measures.
• That makes Carlson the sharper choice for an HR tech company arguing that recruiting should be measured against business outcomes, not merely automated.

They never discuss AI screening, candidate-screening mechanics, or automated sourcing, so an AI-in-recruiting campaign would need to supply its own technical argument and demonstration.

• Their posts contain no caregiving or health care material and point to no product.
• Ask about availability, rates, and partnership terms before proceeding.

Examples: Source 1 · Source 2

Their posts analyse how positioning changes by sector, contrasting impulse-driven DTC purchase pages with retirement and senior living, where families make considered decisions on someone else's behalf.

• Brands in the latter category typically lead with four or five functional must-haves, including dementia and memory care, alongside differentiators such as support for families living apart.
• A second post argues that differentiation erodes when categories compete only on function, using Miss Mosa and Nykaa as contrasting examples.
• Oberoi also states that they help founders with strategy and positioning, making them a fit for a health tech company explaining its product to families and care operators.

The posts are openly exploratory: they describe sectors they are still working through and ask for introductions to people in them.

• Ask for client results before making a decision.
• The material contains no recruiting, hiring, or AI technology detail, so a buyer seeking AI-in-recruiting credibility or caregiving hardware insight will find neither.
• Ask for reach, audience composition, and paid-collaboration terms as well.

Examples: Source 1 · Source 2