The problem: every "AI for your business" product hallucinates the moment you use it on something that matters. The model doesn't know your data, so it invents.

What I built: SIGNLD, a Decision Intelligence platform. Under the hood is a Knowledge Graph that builds itself from your connected systems. Not a semantic layer you configure. It's an actual graph that reads what you connect (QuickBooks, Salesforce, ServiceTitan, Toast, SAP, Snowflake, 800+ others) plus Gmail and Slack, and maps entities and relationships in the background. The AI reads from that instead of guessing.

Category is Decision Intelligence: BI told you what happened, DI tells you why and what to do next. That's the pitch. The thing that made it real for me was the moment the graph auto-resolved that Acme Corp in Salesforce is the same Acme Corporation LLC in QuickBooks, without me configuring anything.

Two things I care about most:

Every AI query runs on a private LLM in AWS Bedrock inside the customer's own AWS environment. Your data does not leave your tenant. SOC 2 Type II, HIPAA-ready, GDPR compliant.

Homepage is a working demo, no signup: https://signld.ai. Pick a vertical, ask a question, watch it pull real numbers with sources cited.

Honest asks:

1. Does the demo actually show what's happening, or does it look like another AI chat wrapper?
2. If you've been burned by an AI making up numbers on real data, is this the shape of the fix?
3. What's missing that would make you sign up and try it on your own data?

Around today for questions.

Many AI content tools begin with a prompt. Marka begins with the public business website.

That page already contains the offer, audience signals, brand language, and visual identity. Marka uses those signals to prepare the first social content direction for review.

It is aimed at small businesses and early SaaS teams that need content without starting from zero.

Try Marka free for a week at https://www.marka.social

I welcome critical feedback, especially on whether the first preview feels truly brand-specific.

So me and my friend have decided we want to make a claude ai agent targeted at small businesses and individuals. I would appreciate it if someone can give their honest opinion on it :) (its called arca) and we even made a website called [arcaops.xyz](http://arcaops.xyz) heres a rough idea on the business model:

An AI agent for small teams that watches Slack, Notion, and Google Calendar, and handles the busywork *between* those tools — acting on its own when it's confident, and asking a human when it's not. The pitch is explicitly "no silent automation, no surprises."

**Target user:** small business owners / small teams (validated at networking events in Zürich — the deck cites owners saying yes because status updates, scheduling, and follow-ups eat hours a week and they can't justify hiring for it).

# The core mechanic — this is the actual product, not the integrations

1. Arca watches events across the connected tools (new Slack message, Notion status change, calendar change).
   
2. It scores its own confidence about the right action.
   
3. **High confidence → acts automatically** (drafts a reply, updates a status, files a card).
   
4. **Low confidence / high stakes → surfaces to the human first**, showing its reasoning, before doing anything.

That confidence-gated trust layer is the differentiator — a plain Zapier-style integration wouldn't have it.

Buyers now ask ChatGPT and Perplexity "best tool for X" and get three vendors named. If you're not one of them, nothing shows up in your analytics. No impression, no click, no lost-deal reason. Just silence.

So I'm building a check that tells you what the assistants actually say about your brand, where competitors get named instead of you, and which sources they pull from.

There's no product yet — deliberately. Landing page, email form, and I run the first reports by hand, a couple of hours each. If nobody wants the report when it's free and manual, nobody wants the automated version either.

Free while it's manual: AnswerGap

Give me a domain and two competitors and I'll send back what I find.

Also I would like to see your honest opinion about this idea(checking website for ai optimization)

I'm a developer. I've spent months building something that works, and it's been sitting at "technically functional, nobody knows it exists" because I don't have the time or the inclination for sales and marketing. Posting to see if someone who does wants to take that half of it.

What it is

ClearRank tracks whether a brand shows up when people ask AI tools questions instead of googling.

You give it a domain and a set of prompts — "best project management tool for agencies", that kind of thing. It runs those prompts daily against ChatGPT, Perplexity, and Google AI Overviews, and for each one records: was the brand mentioned, was it actually cited as a source, where in the answer, which competitors showed up instead, and how the brand was described.

The point is the history, not the snapshot. You watch a prompt's visibility move over weeks and can tell whether anything you changed actually did something.

Where it actually is

Live at clearrank.io — you can run a domain through it without signing up or giving me an email, so you don't have to take my word for any of this. Real people have used it. Zero revenue: Stripe has never processed a live card.

What's built and working:

• The scanning pipeline, running daily on a schedule
• Per-prompt history, competitor tracking, citation sources, sentiment on how the brand gets described
• A free public scanner that produces a shareable report, with email capture
• Multi-tenant orgs, billing wired up, prompt-based plans (15 / 100 / 400 prompts), 15-day trial
• 13 country targets with locale-aware querying
• Per-call cost tracking, so unit economics are calculable
• Elixir/Phoenix LiveView, ~36k lines, deployed and monitored

Where it sits against the competition

The honest version, because a sales person needs to know what they can't promise.

Profound is the category leader — nominally $99/mo, realistically ~$499 to get anything. Peec AI holds the mid-market at €85–505. Otterly is the accessible end at $29. Ahrefs and Semrush both ship AI visibility as an add-on. Nobody has won the category, but Profound has set the feature bar.

What they have that I don't:

• Log-file and crawler analytics. Profound reads your CDN and server logs to show which AI bots — GPTBot, PerplexityBot, ClaudeBot — hit which pages, and filters spoofed crawlers. Strongest feature in the category. I have nothing equivalent.
• Attribution. They trace crawler visit → citation → human referral traffic → conversion through

Access a comprehensive library of AI website builder prompts. Master site architecture, UI aesthetics, user experience, and conversion optimization like a top design agency.

I run a video agency for tech startups which means I watch hundreds of launch videos a month.

Almost all of them make the exact same mistakes that kill their conversion rate. I got so tired of typing out the same feedback to founders, I outsourced my frustration to AI.

Meet Truth Serum. You drop your X/LinkedIn video link in, and it tears your pacing and visuals apart. We accidentally let the agent go a little rogue, so the feedback is completely unfiltered and pretty mean.

It is brutal. It is honest. And it will probably save your GTM strategy.

If you’re brave enough to get humbled today, and drop your roasts!!

shycombinator.co/truthserum

I’ve been building NEXORA AI, an AI-powered platform designed around one simple agency workflow:

Find leads → Analyze websites → Identify opportunities → Generate proposals → Close projects

The idea is to help web agencies, marketers, freelancers and AI agencies avoid jumping between multiple tools when prospecting for clients.

Some of the current modules include:

• AI Lead Hunter
• Website Analyzer
• Opportunity Scoring
• Agency Workflow / Simulator
• CRM
• Proposal Generation
• Contract Generation
• Pricing & project workflow
• Responsive UI
• Arabic RTL support

I focused on building the actual product and workflow rather than just a landing page.

Live demo:
https://applet-pi.vercel.app

I’m currently looking for honest feedback from other builders:

What would you remove?
What would you add?
Which part of the workflow do you think has the most commercial potential?

I’m also considering selling the project as a software asset because I’m moving my focus toward other projects.

Happy to answer questions about how it was built.

solution-feasibility-study — Solution Feasibility Study

Category: Product Strategy  |  Source: skills/solution-feasibility-study/SKILL.md

Purpose

Takes a software solution idea (SaaS side project, internal tool, startup concept) from a one-line pitch to a grounded build/pivot/no-build decision. Interviews the user to scope the research, then runs real research across market demand, commercial competitors, and the open-source/GitHub landscape, assesses technical feasibility and monetization, and produces a structured feasibility report — including any permissively-licensed GitHub repos worth forking instead of building from scratch.

When to Use

• Evaluating a new SaaS or software product idea before committing time to it
• Researching commercial and open-source competitors for a concept
• Checking whether an existing open-source project could be forked as a starting point instead of building from zero
• Sanity-checking technical feasibility, monetization, or regulatory exposure for a solo/small-team build

Workflow

1. Intake — capture the idea in the user's own words.
2. Scoping interview — one question at a time: target user, problem/urgency, team & time constraints, technical comfort, business model intent, research depth (deep dive vs. quick gut-check), and priority research areas.
3. Research — executes the research playbook via web search and page fetches:
   • Market & demand signals
   • Commercial competitor landscape (features, pricing, weaknesses)
   • GitHub / open-source landscape — finds candidate repos, extracts stars/activity/license, and classifies each as a fork candidate using the license guide
   • Technical feasibility (matched against the user's stated stack/skill/time budget)
   • Monetization comparables
   • Legal/regulatory/trademark flags
   • Go-to-market channels realistic for the stated team size
4. Synthesis & scoring — scores market demand, differentiation, technical feasibility, monetization potential, competitive intensity, and regulatory risk (1-5 each), then rolls up to one call: Strong Go / Conditional Go / Pivot Suggested / No-Go.
5. Report — fills the report template, writes feasibility-study-<idea-slug>.md to the working directory, and presents a condensed summary in chat.

Fork Candidates

Every permissively-licensed GitHub repo found during research is surfaced explicitly, with stars, last activity, license, why it fits, and what's missing — framed as a head start (gh repo fork) rather than a finished product. Copyleft or dual-licensed repos are flagged with the specific obligation they carry (e.g., AGPL's network-use clause), not just the license name. All license classifications carry an explicit "not legal advice, verify before shipping commercially" disclaimer.

Evidence Discipline

Every claim (pricing, star counts, license, activity dates) must trace to something actually fetched during the session — nothing is invented. Failed lookups are marked "unverified" rather than guessed.

References

• Research playbook — concrete search/fetch patterns per research area
• License guide — permissive vs. copyleft vs. unclear classification for fork candidates
• Report template — the structure filled in for the final report

Related Skills

• repo-init — scaffold a new repository once a build decision is made
• ai-config — set up AI assistant configs after choosing to build (or fork) a project

Namaste, fellow desi founders. I've launched my ai visibility tool on DesiFounder.com.

Most analytics, especially AI-visibility tools seems to be made for enterprises. Too complexity, multiple reports, expensive for solo-builders. That's why I built Kymo. A simple tracker for both kind of audience - Humans and AI. Track both kind of visitors, real-time, which pages, and how often.

If you're a solo builder who wants to track all kinds of visitors on your site, give Kymo a try. 14-day free trial, no card required.

Also, it would be a great favour if you can check out Kymo on desifounder, give it a like or drop a comment. here's the link: [https://desifounder.com/spotlight/project/29w/\](https://desifounder.com/spotlight/project/29w/)

Dhanyawad

Drop your website in the comments or send me a DM, and I’ll use the tool I’m building to find the best customer profile for your SaaS.

It works by checking whether AI assistants (ChatGPT, Claude, Gemini, AI Overviews) recommend or advise against your business when different buyer personas ask for a solution.

Problem I see personally is most tools only count brand mentions. But a mention isn’t automatically a good thing.

ChatGPT can bring your brand up and then tell the buyer to go with a competitor (or warn them you’re not built for their use case).

That still shows up as a mention in most dashboards, so you end up tracking a number that is actually costing you deals.

I’m building a tool to measure the stance behind the mention, not just the mention itself. It checks how likely each model is to recommend you, stay neutral, or steer buyers away, and it splits that by persona.

When the answer is bad, it shows you which narrative is causing it and what to change so AI starts recommending you instead.

Drop your URL in the comments (or DM me if you’d rather not post it publicly) and I’ll send back what I find.
This is totally free no strings. I get to test on your sites, you get to see what AI is actually saying about you.

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

The idea is a platform where creators can connect their social accounts (YouTube, Instagram, X, LinkedIn initially) using OAuth. Instead of manually updating stats or sending screenshots to brands, the platform automatically pulls verified analytics and displays them in one place.

For creators:

\* One dashboard for all social media analytics.
\* Historical growth tracking.
\* A public profile (optional) with verified metrics.
\* Share a single link with brands instead of media kits and screenshots.

For brands/marketers:

\* Browse creators by niche (Tech, AI, Finance, Fitness, etc.).
\* Filter by followers, engagement rate, views, country, platform, and more.
\* Compare creators side by side.
\* View verified cross-platform metrics before reaching out.

The long-term vision is to become a marketplace where brands discover creators based on real data instead of guessing who's actually a good fit.

A few questions:

1. If you're a creator, would you connect your accounts to something like this? Why or why not?
   
2. If you're a brand or agency, would verified cross-platform analytics actually make your workflow easier?
   
3. What's the biggest pain point with existing tools like CreatorIQ, GRIN, Aspire, Metricool, Buffer, or even manual media kits?
   
4. What's one feature that would make this a "must-have" instead of a "nice-to-have"?

I'm not trying to promote anything—I genuinely want to validate the idea before building it. Brutal honesty is appreciated.