What are you building? Drop your AI product below
by•
Doing one of these because I get more from reading what others are building than from any newsletter.
I'll start.
Building AI Hive, an enterprise AI agent platform that helps mid-market and enterprise teams get from AI pilot to production in weeks instead of quarters. The hard problem we keep solving: compliance, model flexibility, and the lack of in-house AI talent on the customer side.
Product Hunt page: https://www.producthunt.com/products/ai-hive
Your turn. Drop:
- What you're building
- Who it's for
- The hardest part you're solving right now
Will read everything and upvote what resonates.
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Replies
What I'm building: PublishAI — AI writing tools for authors and self-publishers. Generates book descriptions, title ideas, Back Cover and Amazon keywords in seconds.
Who it's for: KDP self-publishers and indie authors who hate staring at a blank page trying to write their book listing copy.
Hardest part right now: Getting discovered with zero ad budget as a solo founder from Nigeria 🇳🇬. Relying 100% on organic — IH, PH forums, and word of mouth.
Free to try: kdp-tool-nu.vercel.app
PH listing: https://www.producthunt.com/prod...
@imran_isah thanks for sharing this, Imran, the use case is super specific and that's a good thing.
the blank page problem for book listing copy is real, and KDP authors are chronically underserved by AI tools that try to do everything. zero ad budget + 100% organic is tough but PH and IH are actually the right communities for this, because the people here understand the grind and tend to share things that genuinely help them. will check out the tool.
@imran_isah Tried it. The book description output understood the KDP context in a way generic AI tools don't, which is exactly what you said you were going for.
The specificity is genuinely your moat here. KDP authors have a very particular set of requirements, and tools that try to serve all writers at once always miss the Amazon-specific stuff. Keep it narrow.
@imran_isah Love this idea. I'm thinking of publishing a joke book for Tom's Shit Jokes in the near future so I'll check this out!
@imran_isah Really glad it landed the way I hoped it would, honestly. Feedback like this back is the best kind of confirmation that the direction you're already leaning into is the right one.
Keyword validator + category finder + A+ content generator as the next roadmap sounds spot on for KDP sellers. That's the workflow where authors actually feel the pain daily, and getting all three under one tool with tight scope is going to be way more useful than another "AI writing assistant" trying to do everything. Rooting for you on this one.
What you're building
SYZY — a co-founder matching platform with skill-based profiles, mutual-match chat, and free entry tools (Founder Type Quiz + Equity Calculator) that feed into a waitlist/beta funnel.
Who it's for
Serious builders looking for a co-founder by complementary skills — people who want a real build partner, not generic networking.
The hardest part you're solving right now
Pre-launch conversion: users get value from the free tools but hesitate to give an email or sign up before matching is live. You're working on the “why now, not on launch day?” moment and proving value before the full platform opens.
And also we're opening 50 invite-only beta spots before launch. Join early: priority matching, verified badge, and your first month free.
@syzy thanks for your comment
The "why now, not on launch day?" problem is one of the harder ones to crack. Free tools that give value upfront usually solve discovery, but the signup friction sits somewhere else entirely. What's your current read on why they hesitate? Like, is it "I'm not ready to be matched yet" or more "I don't trust this will work"? Those need pretty different fixes.
For us with AI Hive, we hit a similar wall early on enterprises would play with demos but not commit until they saw a real production case from their industry. Adding case studies with hard numbers (not just "X% improvement" vague stuff) moved things more than any onboarding flow tweak. Not sure if that maps to your situation but curious what you're testing right now.
@nolan_vu Thanks for the insights, Nolan! The "why now" friction is definitely the biggest nut to crack here.
From what I’m seeing, it’s a mix of both, but it leans heavily towards the "I don't trust there’s enough liquidity yet" side. Founders are hesitant to fill out a profile if they feel they'll just hit a ghost town or waiting room.
To combat that, I'm currently experimenting with a few things:
Value before signup: Testing a way for users to see blurred or anonymized "active requests" (e.g., “An AI dev in Berlin is looking for a non-tech co-founder”) before they even hit the register button, so they know the supply side is real.
Micro-communities: Narrowing the initial launch focus to specific niches (like Web3 or AI bootstrapers) to artificially create that density faster.
Love the case studies angle you mentioned for AI Hive. For a matching platform, I guess our "case studies" will have to be early successful intros.
Curious, when you guys were tackling the enterprise demo-to-commitment wall, did you use any specific triggers to prompt them for that production data, or was it purely a sales/outreach play?
@syzy
The blurred active-requests idea is smart, it's basically showing proof of liquidity without giving away the thing people are paying to unlock. I am curious on how far you blur it though, enough to tease but not enough for someone to just guess who it is?
Micro-communities first makes sense too. Density beats breadth early on, a thin pool of 500 spread across industries feels deader than 50 people all in Web3.
On your question: no clever trigger on our side, it was mostly direct outreach. We'd ask prospects point blank what they needed to see in production to justify moving past pilot, then build the case study around exactly that metric, not a generic "look how great this is" pitch. Worked better than anything passive.
For you, the equivalent might be asking your first 20 signups directly what would've made them register a week earlier. They'll probably tell you something you haven't tested yet.
@syzy I'd love to see more products focused on reducing digital clutter. Most tools help us create more files and content, but very few help us maintain order as everything accumulates over the years.
@syzy Hi! I'd love to participate in beta program if it's still available.
@achille82 thanks for your sharing, once your product is launched I can give a quick review though. Hope that you can do the same for AI Hive since we all built AI tools to support clients
The "reasons first, then outputs a real document" framing is the right instinct. Most people don't actually want more chat, they want something they can hand to someone else without reformatting it first.
The long-document reliability problem you mentioned is a real wall though. Once you're generating multi-chapter technical specs, the failure mode isn't usually "wrong answer," it's losing internal consistency across sections, like chapter 3 contradicting an assumption from chapter 1.
Curious how you're handling that: are you generating the whole document in one pass, or chunking it section by section with some kind of state tracked between chunks? That choice probably has a big effect on cost too, which matters a lot given the affordability angle you're going for.
Electrician building the tool you needed on the job is a strong origin story, that's the kind of detail that's hard to fake.
@achille82 thanks for your interest and taking time for both your Nexia product and our AI Hive
Splitting generation by section first then merging is exactly the move I'd expect to work better than one giant pass. Makes sense the merge tool itself is the bottleneck now, that's usually the unglamorous part nobody wants to build but everything depends on it.
Honestly the Expert tier pricing thing resonates a lot. We went through the same math with on-prem deployments, more context and consistency just costs more to run, no way around it without quietly cutting corners on quality.
Good point on the ecosystem split too, never thought of it that framed that clean way until you said it. We're plumbing for orgs, you're plumbing for the document itself basically lol.
Congrats on the June 17 launch btw, going to check it out properly this week and leave a real review, not just an upvote and run.
@achille82 hi! Are users mainly asking for a polished first draft quickly, or for something they can review and improve over a few passes/iterations?
@achille82 Thanks. Sounds like the Expert version is really about getting closer to the right result from the start, even if it needs more context and compute behind it
@achille82 It would be nice to stay in touch. What would be the best place - email, LinkedIn, or another channel?
What I built: Takivo.ai - An AI native workplace communication platform.
Who it's for: Small to mid sized tech companies
Hardest part right now: Getting discovered or being seen.
Link: https://www.producthunt.com/products/takivo
@johnsongill Thanks for sharing Takivo, workplace communication is such a crowded space but "AI native from the ground up" for small to mid sized tech teams is a specific enough angle to actually stand out.
Getting discovered is the classic problem, and honestly I don't think there's a shortcut, it's usually a mix of showing up in niche communities where your exact ICP hangs out and being patient with organic growth. What does the AI native part actually change day to day for a team using it, compared to just bolting AI onto Slack?
If you're up for it, would love your feedback on AI Hive as well, an automation platform I'm building. A review would genuinely help: https://www.producthunt.com/products/ai-hive
Thanks Nolan, and agreed on discovery, there really isn't a shortcut, just showing up where the right people already are and staying patient with it.
On the AI native question, the real difference is who does the remembering. Bolt a bot onto Slack and someone still has to think to summon it and write the follow-up. Built in from the ground up, the system's already reading along and pulling the decisions and tasks out as they happen, so the 11pm call nobody logged doesn't quietly vanish. Happy to take a proper look at AI Hive too and leave you some honest thoughts.
@johnsongill glad that you share the common thought with me though
And Damn, "who does the remembering" is such a clean way to put it, that's genuinely the sharpest answer I've gotten to that question so far. The 11pm call nobody logged not vanishing part hits different too, that's the exact scenario that made us build the memory layer the way we did.
@nolan_vu thank you so much for your kind words. How's it going so far?
@johnsongill What you've built looks really handful, saying this as a former Pm hah
Are early teams using Takivo instead of a specific tool, such as Slack or a project tracker, or do they keep their existing stack and use Takivo to pull tasks, decisions, and follow ups together?
@daria_brown Hahah I can understand.
They are using Takivo completely :)
@johnsongill This would be interesting to discuss in a bit more detail. Is there a better place to stay in touch?
@daria_brown Sounds great, drop me your LI and I'll add you up.
Or you can look me up as Johnson Gill on LI.
I'm building agendo - booking software for salons, clinics, and studios, except it actually handles the stuff a receptionist would.
The thing that kept bugging me: small businesses lose bookings constantly just because nobody's there to answer the phone or reply to a DM at 9 pm. So I leaned hard into AI to cover that:
a chatbot + assistant that answers clients and books them in;
a voice agent that picks up actual phone calls and schedules;
sentiment analysis that flags people who left an unhappy review before they quietly leave for good;
scheduling that fills gaps and chases down no-shows.
It's multi-tenant and white-labeled for each business, so it looks like their own tool, not ours.
Still early and figuring things out. Link's here if you want to poke at it: https://agendo.ro
One thing I keep going back and forth on — where's the line where people stop trusting an AI to talk to their customers? Genuinely curious how the folks here think about it.
@grrigore The 9pm missed booking problem is so real, and it's the kind of thing that sounds small but kills retention for small service businesses over time. The sentiment flagging before someone quietly churns is the feature I'd actually pay for tbh.
On your trust question, I think the line moves depending on stakes. Booking a haircut? People don't care if it's an AI. Rescheduling a medical appointment with a personal note? That's where it starts to feel off. White-labeling helps a lot because at least it doesn't feel like a bot from a third-party.
@nolan_vu I feel like people are not ready for AI calls. At least the feedback I got is that they are not really willing to talk to an AI. This is mostly based on their previous experiences with older bots. I also agree with you; I wouldn't be eager to have a complex call with an AI for now, but some kinds of bookings can now be easily handled.
@grrigore I agreed with you on that one, but AI calls will dominate the CS field as it can optimize the amount and quality of calls your business can receive. Hope that the tech can develop well so clients can accept the AI call later on
@grrigore The trust line may be different for every business, not just every use case.
Are you thinking of letting each salon/clinic/studio define what AI can handle on its own and what should always be handed off to a human?
@daria_brown Yes, that's exactly the direction — per business, not per use case.
Two salons with the same service list will draw the line in different places depending on how much they trust a phone booking.
We've got a first version of this: each business picks which of their services the AI is allowed to book over the phone; everything else is off by default. It's a hard boundary, not something the AI can be talked out of.
The voice agent captures leads, too, which is the other half. If it can't complete something, it takes the caller's details and drops them in a Leads inbox for staff to follow up.
@grrigore That makes a lot of sense. I like that you’re treating the boundary as a product rule, not something the AI decides in the moment. For customer-facing voice agents, that probably matters a lot.
The lead capture part is also interesting, even when the AI can’t complete the booking. My team worked on a similar logic back when we were involved in product development, so this part feels especially practical to me.
I’d be happy to keep following what you’re building. What’s the best place to stay in touch with you?
@daria_brown I'm on LinkedIn at grrigore. Right now, I don't really post about what I'm building (this was my first post), but I can let you know as soon as I do.
What you're building: Building NexQL, a PostgreSQL extension for VS Code — database explorer, SQL notebooks, real-time dashboard, and an AI assistant, all without leaving the editor.
Who it's for: developers and small teams running Postgres who want AI help writing/optimizing queries but don't want an agent with unsupervised write access to their database. It provides an MCP server that can be used in other agents/models to access your data without any risk.
Hardest part right now: the trust problem, not the AI problem. Every AI tool wants to prove it can write better SQL — the harder question is what happens once that AI has real access to a production database. I built the safety layer first (environment tagging, read-only enforcement on prod, query risk scoring) and only added agentic AI on top of that, including a built-in MCP server so external agents can query safely too. Getting that balance right — useful and autonomous enough to save real time, restrained enough that nobody's afraid to point it at prod — is the actual hard part.
Product hunt link: https://www.producthunt.com/products/nexql
Check it out here: https://nexql.astrx.dev/
@ric_v36 For database tools, the hard part is not just helping AI write better SQL, but deciding what it should be allowed to do once real production data is involved.
Curious how you’re thinking about adoption for small teams, do they mainly want AI help for query writing and exploration, or is the bigger value in giving external agents safe, controlled access to their Postgres data?
Hi @daria_brown, you hit the nail on the head. For database tools, the real battleground isn't SQL generation, it's governance, trust, and execution safety.
To be honest, traditional DB clients have a shelf-life. The real future is moving toward the Model Context Protocol (MCP) and agentic ecosystems. In the near future, developers and analysts won't write raw SQL unless it's absolutely critical. That might a very subjective take on the future.
That is exactly how we are positioning NexQL. Chatting with an AI is just step one. The long-term game is building a highly secure, context-aware, and bulletproof protocol for external LLMs and agents to interact with production data safely.
For small teams and enterprise environments alike, adoption hinges entirely on trust. We are building that trust by focusing on three pillars:
Guarded Execution: We don’t just let an agent loose. We enforce a strict "observe-and-continue" loop. Read-only actions (like investigating schemas or pulling query plans) are seamless, but any mutating action triggers mandatory, multi-factor confirmation gates. Prompt-based rules aren't enough; safety must be backend-enforced.
Context Intelligence: Agents fail when they are blind or overwhelmed. NexQL leverages schema-aware retrieval (filtering and truncating wide table DDLs based on lexical relevance) so the model gets highly specific context without blowing token budgets or hallucinating identifiers.
Deep Transparency: We maintain a rigorous audit trail of every single step an agent takes—from the raw tool call to user approvals and execution.
Ultimately, we want NexQL to be the absolute safest environment for teams to transition from simple "AI assistance" to full "AI autonomy." If we can build that trust within the extension ecosystem today, it paves the path for NexQL to expand far beyond the IDE tomorrow 🤞 😅
@ric_v36 Thanks for such detailed answer! This makes your direction much clearer.
I really like the shift from "AI writes SQL" to "agents can safely interact with production data". The guarded execution and context intelligence parts are especially interesting, because they solve two very real problems: trust and giving the model enough context without overwhelming it.
Would be great to stay in touch and follow how NexQL evolves. What’s the best place to connect with you? X, LinkedIn, email, or somewhere else?
@daria_brown I don't have an X account. You can connect with me over LinkedIn https://linkedin.com/in/ric-v/ or connect over email on ric-v@astrx.dev
@ric_v36 sorry first for my late response mate
This hits close to home ngl. Worked with a small team last year that almost gave an AI agent direct write access to a production DB because "it seemed fine in testing." Still get a lil anxious thinking about that one.
The fact you built safety first and layered agentic AI on top instead of the usual way around says a lot about priorities. Small teams running Postgres need exactly this, something useful enough to save time but not scary enough to keep you up at night.
Rooting for NexQL, upvoted already and following for updates. Also, if you're open to checking out other builder projects, I'd genuinely appreciate your feedback on AI Hive, an automation platform we're growing, an upvote or follow would help a ton too: https://www.producthunt.com/products/ai-hive
@nolan_vu Thanks for your kind words, really made me happy that you went through the post and not just responded with a generic comment.
I really liked your product ai-hive and started following you after seeing your vision.
Hope to connect with you on linkedin.
Building Glint, a neutral trust and control layer for AI coding agents.
Glint plugs into the agents teams already use (Claude Code, Cursor, Codex-style CLIs, via regular subscriptions or APIs) and gives them one workflow, one safety net, one memory across all of those tools.
It is for software teams that want developers to keep their preferred coding agent, while the team defines a shared brain for the repo:
global rules (scope limits, “do not touch” areas, required checks)
context files (project briefs, architecture notes)
skills and agents (which tools can do what)
standards (review gates, diff quality, test expectations)
Just like teams already do with claude .md, .cursorrules, or other .md files, Glint reads those definitions once and then every connected coding agent follows them seamlessly, regardless of vendor.
In short: bring your own agent, keep your standards.
Right now the focus is making cross-agent A/B runs (same task, different agents, same guardrails) feel natural in day-to-day coding, and validating that this neutral control-plane really solves the pain of keeping standards and context consistent when teams use more than one AI coding tool.
@waqas_baloch4 "Bring your own agent, keep your standards" is such a clean way to put it, the real pain with multi-agent teams isn't the tools themselves, it's every dev's agent following different rules. Cross-agent A/B runs with the same guardrails sounds like exactly what's missing right now.
Hope Glint nails that neutral control-plane vision, upvoted and following.
@nolan_vu Thank you so much Nolan, You are spot on!
The pain was never the tools, it's that everyone's agent follows a different rulebook. Your Cursor, your teammate's Claude Code, someone's Codex, all sharp, all pulling in slightly different directions on the same repo.
So here's the thing I'm obsessed with getting right: Glint reads your rules once from the CLAUDE.md / .cursorrules / AGENTS.md files you already keep, then doesn't just hand them to each agent and hope. It checks the result afterward too, whatever the vendor. Telling an agent the rules is hope; checking is control.
And the A/B you flagged is already live, same task, every connected agent, same guardrails, keep the diff you like. Watching Claude and Codex solve the same thing side by side is way more addictive than it has any right to be.
What's your stack? If you've got two agents pulling at one repo, that's the exact mess I built this for. I'd genuinely love to put it in your hands and hear what breaks.
@waqas_baloch4 Have you considered adding a unified view of usage, cost, and performance across the connected agents? It could help teams understand not only whether each agent follows the same standards, but which one is most effective for different types of work.
@daria_brown Love this suggestion, thank you! 🙌
The performance part is exactly where I'm taking compare next: it already runs one task through every connected agent, so tracking which agent wins at what (refactors, tests, UI) over time is the natural step.
On cost, I'm being deliberately careful. For folks on flat subscriptions, which is most users today, the "cost" of a run isn't what they actually pay. The agents don't even report usage in a common unit: one gives dollars, another raw tokens. I'd rather show honest effectiveness than a precise-looking number that isn't true.
But for teams connected via API, cost is real and metered, and that's exactly where a unified cost view makes sense. So that's how I'm thinking about it: effectiveness for everyone, real cost tracking for API teams.
What kind of work would you most want the effectiveness breakdown for? That'd genuinely help me shape it.
@waqas_baloch4 I’d be most interested in the breakdown for refactoring, test generation, debugging, and code review - areas where both output quality and the number of iterations can vary significantly between agents.
I'm building Norl,
a modern business toolkit that starts with a free invoice generator with no sign-up.
The long-term vision is to create beautifully designed tools that help freelancers and small businesses manage everything from invoices and quotes to payments, documents, and workflows—all in one simple, accessible platform.
Check it out here: https://www.producthunt.com/products/norl
Would love to hear about your suggestions and feedback...Also tell us about which tool you want to use regarding invoices and quotes.
We are also building Invoice tracker in our app..so that when user creates an invoice and sends it to customer, the user will have access to view invoice analytics like the customer has seen invoice and paid the invoice. Also tracks late payments and due dates! Would love to hear your suggestions!
@ahmad_toor Free invoice generator with zero sign-up is such a smart hook, most tools make you create an account before you even see if it fits your workflow. The invoice tracker with view/paid status sounds genuinely useful too, chasing late payments manually is a pain every freelancer knows too well.
Hope Norl keeps growing into that full toolkit vision, upvoted and following. Would appreciate your thoughts on AI Hive if you get a sec, we're building automation flows for teams: https://www.producthunt.com/products/ai-hive
@nolan_vu Thanks, Nolan! Really inspiring to see how ChatX evolved into AI Hive by solving bigger problems instead of chasing trends. I'm at the beginning of a similar journey with Norl, starting with a free invoice generator and hoping to grow it into a complete business toolkit. Wishing you and the AI Hive team continued success!
@ahmad_toor Thanks Ahmad, that genuinely means a lot. The invoice tracker with paid/viewed status sounds like the missing piece for anyone doing freelance work, chasing payments with zero visibility on whether the client even opened the invoice is one of those low-key painful things.
The free-first hook is smart, getting people to feel the value before asking for anything is still the best distribution move for tools like this. Wishing Norl the same. Let's keep in touch as both products grow.
What I'm building: Agentcroft Insight. It connects to your ad accounts, analytics and CRM, then learns the human context around them: what you're trying to do this quarter, why you killed that campaign, what your CEO actually cares about. The result knows your business better than any one person in it. Your paid person doesn't see the CRM. Your founder doesn't see the ad accounts. Nobody has the whole picture. This does. And it talks to each of them in their own language: payback for the CFO, what changed and why for the CEO, campaign detail for the marketer.
Who it's for: Marketing teams with real budget and not enough people. Plus agencies, who brand it as their own and give it to clients. Anyone who's having communication problems with the 'internal sell' and is overwhelmed with their data.
The hardest part right now: The product works. We use it every day and it's genuinely better than what we replaced. The hard part is that "AI marketing dashboard" is the most crowded phrase on the internet right now, and nobody believes a landing page anymore. Everyone claims insight. Everyone claims AI. The thing that makes ours different only shows up after a few weeks of use, once it has enough context to say something nobody on the team could have said. That's a terrible thing to put on a homepage and a brilliant thing to experience. So the challenge is getting people far enough in to feel it, without asking for three weeks of faith up front. That's why we're launching here rather than buying ads at it.
Thanks Nolan!
@danielboardman The way Agentcroft builds context across different data sources sounds interesting. Is the insight layer based on external AI models, or have you built a different setup for it?
@daria_brown Thanks - It's powered with the Anthropic API as the model, but the Brain itself is a custom build. It categorizes context into different node types depending on the information, for example; data, hypothesis, constraint, goal etc. Then dependent on what it knows, it'll give analysis and advice - and if there's context gaps it'll ask 'why did you do x?' so it can learn. The idea is that it becomes a team member with perfect memory AND understanding of the business.
@danielboardman The problem you described, where the paid person doesn't see the CRM and the founder doesn't see the ad accounts, is one of those things that's so obvious once you say it out loud but nobody actually fixes it. Building context that talks to each person in their own language is smart positioning.
The cold start challenge is real too. Something that only shows its value after a few weeks of use is genuinely hard to demo on a landing page. Launching here instead of buying ads at it makes a lot of sense for that reason. Upvoted and following.
Would appreciate your thoughts on AI Hive if you get a chance. We're building on the workflow automation side and think there might be some interesting overlap in how we think about context: https://www.producthunt.com/products/ai-hive
@nolan_vu Thanks Nolan, appreciate the support - I'm excited to see feedback when we have our launch day next week. The cold-start thing is a real pain point, you've spotted the issue perfectly. I'll be sure to check out AI Hive - happy to support and definitely be good to explore if there's any overlap. Thanks!
@danielboardman Thanks a lot Daniel, really appreciate you saying that. Wishing you a smooth launch next week, cold-start products are always the trickiest to demo but usually the ones that stick longest. Take your time checking out AI Hive whenever it fits, no rush. Would genuinely love your take when you get a chance, especially from someone thinking about context and personalization the way you do. Definitely open to swapping notes down the line.
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We're continually improving Duedocs and would love your feedback!
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What's one feature you'd love to see in an AI tool for property buyers?
@nidhisachan Appreciate you posting this, property docs are genuinely one of the most stressful things to read as a buyer. I remember getting a 40 page vendor contract and just staring at it not knowing what mattered.
The risk insights + plain English summary combo is smart, that's exactly the gap between "I read it" and "I understood it." One feature I'd want: flagging clauses that differ from standard market terms, so buyers know what's actually unusual.
@nolan_vu
Thanks, we really appreciate that. That's exactly the experience we kept hearing from buyers, it's not that they don't want to read the contract, it's that it's difficult to know what matters and what deserves closer attention.
Flagging clauses that differ from standard market terms is a great suggestion. Helping buyers identify what's routine versus what may be unusual or warrant further discussion with their conveyancer is definitely an area we're continuing to explore. Feedback like this helps shape our roadmap, so thanks for taking the time to share it!
@nidhisachan Thanks Nidhi, glad that resonated. Yeah that's exactly the pain point I keep running into when talking to first-time buyers tbh. They don't skip the contract because they're lazy, they skip it because 40 pages of legal jargon feels impossible to parse without a lawyer sitting next to them.
The non-standard clause flagging thing came from a real situation where a friend almost missed a sunset clause buried on page 31. If DueDocs can nail that "here's what's weird about THIS specific contract" angle, that alone would save people a lot of stress (and potentially a lot of money). Looking forward to seeing how you guys build it out.
@nidhisachan Appreciate that Anuj, and honestly DueDocs solving for exactly that "staring at 40 pages wondering what's important" problem is spot on. That's the gap I see most proptech tools still missing. Most buyers don't need to understand every clause, they just need to know which ones could bite them later.
One thing I'd add to the non-standard clause idea: if you can show a quick comparison like "standard market term says X, your contract says Y" side by side, that would be a game changer for buyers who aren't legally trained. Way more useful than just highlighting something in red and hoping they know what to do with it. Keep shipping, this is a real problem worth solving properly.
@nidhisachan @nolan_vu Thanks so much, really appreciate you sharing that. Your experience is exactly why we built DueDocs: to cut through the noise and surface what actually matters in a property contract, so buyers aren’t left staring at 40 pages wondering what’s important.
Love your suggestion about flagging non-standard clauses. That’s a powerful way to help buyers spot what’s routine vs. what might need a closer look or a conversation with their conveyancer. We’re already exploring ways to highlight unusual or high-risk terms, and your feedback reinforces that this is a priority. Thanks again for the thoughtful input.
@nidhisachan The combination of document review and property research is what stood out to me. I’d be curious how the experience changes when someone uploads several long or complex documents at once, can Duedocs still return the analysis within minutes?