Investor deck, Pre-seed
Relethe
Networking without the performance.
connect first
finite feed
intros that matter
Abiola Makinde, Co-founder & CEO | Nabil Chaib-Draa, Co-founder & CTO
01: Problem
The internet made it easy to discover people.
It made it hard to connect with the right ones.
It made it hard to connect with the right ones.
The gap
Signal without connection
No one needs a strong community more than builders, operators, and thinkers. Yet the tools built for them optimize for discovery, not depth. You can find anyone. You cannot reach the right person at the right time.
01
Noisy browsing
Feeds built to keep you scrolling reward performance over presence. You are expected to maintain a timeline, post consistently, and play the algorithm. None of that is connecting.
02
Low-signal intros
Most tools match on similarity: same industry, same title. You end up meeting people who look like you, not the people you actually need right now, at this stage, for this thing you are building.
Busy people still want to meet people worth meeting. No tool has been built around their time, their intent, or the quality of the connection itself.
02: Why this
Connection is not a metric.
It is the reason any of this matters.
It is the reason any of this matters.
Our thesis
Every meaningful thing people build, discover, or become is shaped by who they meet. Not followers. Not connections. People. The most underserved people on the internet are the ones who actually want depth: builders, operators, thinkers who need the right room, not a bigger audience.
Why now
AI is flooding every feed with generated content and emptying it of signal. The appetite for something intentional has never been higher. Relethe is built for what comes after the feed. Finite. Purposeful. Worth showing up for.
We think the internet has made it too easy to discover people and too hard to connect with the right ones at the right time.
Relethe is our attempt to fix that. A high-fidelity super-connector that works around your time, surfaces introductions matched to your intent, and asks nothing of your feed.
03: Solution
Built for where you are
right now.
right now.
Intros
AI superconnector
Set your match preferences before you have posted anything. Up to five introductions a week, behaviorally matched. Not just by title or industry.
Connect
Meet on your terms
Set your availability, frequency, and boundaries. Every introduction is a deliberate choice. You scan your weekly matches and decide who makes the cut.
Feed
The feed has a shape
A daily selection of posts. Short-form, intentional, finite. It ends. That is the point. The feed exists so you show up knowing who you are meeting and why.
04: Product
Connect first.
Feed with purpose.
Feed with purpose.
The matching is disciplined, not magic. Six mechanisms working together. Each one adds signal. Each one makes the next introduction sharper. The feed exists to serve that engine, not the other way around.
Explicit Intent
Match settings is the baseline. You say what you want instead of being passively fed. CEP goes deeper on paid: the AI conversation that surfaces what is actually missing. One idea, two depths.
Scored Complementarity
Behavioral inference layered beneath deterministic scoring, weighted against offer-ask gaps. What you need, not just what you like.
Event Logging
The system sharpens from actual behavior: who you accepted, who you passed, whether you followed through. It gets smarter from outcomes, not assumptions.
Post-Meeting Feedback
After each introduction, users report how the call went. Structured signal feeds into match accuracy monitoring. The system learns from what actually happened.
Human Vetting
Every suggestion reviewed before it reaches you. Catches what the model misses while the feedback loop is still young.
Public Intelligence
Twitter and LinkedIn scraped without OAuth. Surfaces connections your bio would never list. Cold start solved, and it compounds over time.
05: Market
The right moment for this product.
Addressable market
~50M
founders, operators & independent professionals globally
~10M
who would pay for better introductions
$1.2B
serviceable market at $10/mo
Primary audience
Independents
Solo operators, freelancers, and founders building on their own.
Epistemics
Thinkers, researchers, and writers who trade in ideas.
Social Impact
Mission-driven operators who need a network that matches their values.
Precedent
Lunchclub proved the appetite for intentional introductions. That demand never went away. No one has built the right successor.
Social impact
U.S. loneliness carries an estimated $406B annual healthcare burden. Better social technology is now both a market opportunity and a public-good imperative.
06: Business model
Depth is the product.
We charge for it.
We charge for it.
Free User
Joins via waitlist or invite. Completes onboarding. Receives base matches.
Signal Builds
Feed behavior, connection history, and CEP data accumulate passively.
Paid Conversion
Gets basic matching. Sees what precision looks like. Upgrades.
Roadmap
Beyond subscriptions
Enterprise and cohort licensing for accelerators, VC networks, and communities. In pipeline. Not priced yet.
Free tier
Core experience
Base AI matching on declared profile data. You get into the right room, the feed is live, and communities are open. But the free tier is doing more than activating users. Every connection, every pass, every behavior is building the signal layer that makes paid introductions accurate. Free users are not a charity case. They are the architecture.
Paid tier — $10/mo
Hyperpersonalized matching
Unlocks CEP (the deep context session), behavioral signal layering, and public data enrichment. This is not "more AI." It is authorship. You direct your own introductions with specificity: intent, context, and personality signals you define. The model compounds over time. The longer you are on Relethe, the more precise that direction becomes. That is what retains people.
07: Traction
Real engineered growth.
Year 1
2027
50k
users
$2k
MRR
Beta cohort, hand-curated. Matching quality proven. First paid conversions on the $10/mo tier.
Year 2
2028
200k
users
$20k
MRR
Waitlist opens. Word-of-mouth compounds. Community features deepen retention. Series A groundwork begins.
Year 3
2029
750k
users
$50k
MRR
Network effects compounding. International expansion. Strategic partnerships and B2B licensing unlock new revenue.
Early validation
16 founders and operators validated the concept. All 16 requested early access.
Full high-fidelity prototype completed by the founder alone. No technical hire yet.
First 50 users will be hand-picked for interconnectedness, not just interest.
Technical co-founder with ML background onboarded. Nabil joins as Co-founder and CTO.
08: Team
Built by people who had to.
Abiola Makinde
Co-founder, CEO & Designer
Eight years designing digital products at early-stage companies. Sole designer at Persana AI (YC W23) across product, marketing, and investor materials, contributing directly to a $2.3M raise.
Built Relethe's full prototype independently. Designers who build are rare. That is the gap this product requires.
Built Relethe's full prototype independently. Designers who build are rare. That is the gap this product requires.
Nabil Chaib-Draa
Co-founder & CTO
Data engineer at Lightcast with a strong foundation in Python, R, JavaScript, Pandas, and NumPy. Experienced in prediction models, Bayesian statistics, and data visualization across complex datasets.
Brings the ML infrastructure Relethe's matching engine requires. The technical depth to build it right, not just fast.
Brings the ML infrastructure Relethe's matching engine requires. The technical depth to build it right, not just fast.
09: Ask
Raising $25k to $50k.
$25–28k
AI infrastructure
Hosting, APIs, compute, and tooling. The largest and most critical spend.
$10–12k
Brand marketing
India-based contractor. Content, posts, and brand visibility across 3 to 4 months.
$10–12k
Connections manager
India-based, part-time. Manually reviews and approves AI-suggested matches for quality control.
$5k
Ops and legal
Incorporation, contracts, domain, and essential tools.
Total: up to $50k.
What success looks like
50 hand-picked users. 70% accept their first introduction. 30% report a meaningful outcome within 30 days. That is the only proof point this round is buying.
10: Timeline
From now to first
500 users.
500 users.
Month 1, Now to June
Lock in and align
Close the technical co-founder seat
Align on MVP scope: finite feed and profile-based matching, nothing else
Set up company registration, domain, and basic infrastructure
Month 2, July
Build fast, break things
Build core matching engine, profile-based, no feed dependency yet
Build finite feed layer in parallel
Internal testing, iterate fast
Month 3, August
Soft launch
Invite 50 to 100 hand-picked early users
Watch matching quality closely: does it surface people worth meeting?
Collect feedback, fix the model
Month 4, October
Open the waitlist
If matching works, push toward 500 users
User acquisition spend kicks in here
This is the milestone that proves the model
1 / 11