Ashish Malani ✓
@malani4u · Pune, India · joined 20 Mar 2014
Accidental Investor. I like to read, trade ,invest , speculate. Not Sebi Registered. On X to share my thesis and connect with similar minds. Ex Product Manager.
19 155Followers
848Following
1 593Posts total
419.8KViews on collected posts
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RentoMojo IPO: A cash generating startup about to list soon. Worth buying? Lets dig.
1. RentoMojo is not just a rental company but a Play on Consumer Pattern Change!
The biggest question is not whether RentoMojo can grow.
The bigger question is:
Can it continue growing 40%+
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· 6 reposts · 2 replies
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ESDS bought at pre open. Huge Institutional Demand. Hold tight. Stay Invested. Some more huge deals in pipeline will come to reality in coming days.
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3 years ago E2E was 300 cr mcap. Yesterday it got a 1000 cr order for 3 years from a single customer.
The whole AI factory/Gpu As a Service businesses are seeing huge tailwind.
Expect them to announce more such deals in the coming days. But the business model is capex heavy so
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@malani4u 40–50% growth sounds incredible until you ask what it costs to produce. The best businesses scale revenue without needing capital to scale just as fast.
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14. WHY ESDS COULD ACTUALLY BE BETTER THAN E2E IN ONE RESPECT?
E2E is a fantastic pure AI infrastructure story.
But it needs to spend enormous amounts of capital to acquire GPUs. And a huge depreciation that follows.
ESDS potentially has:
Existing customers
Existing data
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MY FRAMEWORK on Valuation
Not company guidance — my scenario based on existing business growth + SharonAI ramp. ( Although major inputs taken from management meet )
FY27
Existing business grows ~50%.
SharonAI starts contributing from Q3.
Projected Revenue ~₹1,800–2,000 Cr
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4. Why would an enterprise choose ESDS for AI infrastructure instead of going directly to Sharon AI?
This is the part of the ESDS–Sharon AI deal I find most interesting.
The answer is ESDS isn't trying to compete with Sharon at the GPU-factory layer.
Sharon provides the
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7. ESDS's real differentiator may be the enterprise software layer
The AI infrastructure stack increasingly looks like:
GPU → Kubernetes → MLOps → Model → Security → Governance → Application
Sharon is extremely strong at the first layer — the AI factory.
ESDS is trying
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ESDS Software Solutions IPO— The NEXT Big listed Data center and AI Play after E2E.
PS: Long and detailed notes, be patient.
Very very few businesses can grow at 40-50% CAGR even for few years.
And even fewer companies can grow exponentially without significant equity
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@malani4u Actually lots of big investor names are associated with this company (can be a scenario of Pump and dump) also the company does not have strong moats, can't rely on takeover or acquisition.
will buy around Rs. 150
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Looking 3 years forward
You are getting E2E networks at a forward PE multiple of 6 times PAT and 3 times cash profits.
With a cycle of PE re-rating the company can be an easy 10x from here!
Disc: Invested
I am just sharing some research and my views.
Views are biased!
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Expected Projections : Guesstimates if all goes well.
FY 24 revenue 80 cr
FY 24 Cash profits 40 cr
FY 24 PAT 25 Cr
—
FY 25 revenue 140 Cr
FY 25 Cash profits 60Cr
FY 25 PAT 35 Cr
—
FY 26 revenue 250 Cr
FY 26 Cash profits 100 CR
FY 26 PAT 50 Cr
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answers it gives you and at an unmatched speed.
3.GPUs are made by NVDIA , AMD and Intel.
4.NVIDIA is the market leader in this space.
Do check NVIDIA stock price action of last 1 year.
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6. Ex- Drones flying and sending data in real time for image processing cannot be processed on a normal CPU it needs a superpowered Cloud GPU.
1. For all kinds of ML algo training and usage GPUs are required.
2. Chat GPT requires 1000s of super powered GPUS to give you the smart
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3. Machine Learning is a continuous process where the machine is constantly learning new patterns and and storing them and then learning again and the complexity keeps adding.
4. So with high complexity comes the need for GPU.
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Now lets understand what is a GPU and why it is so important for success of AI/ML products?
1.GPU is a m advanced version of the CPUs that we know as the powerhouse of computers.
2.Why are GPUs required for AI/ML applications?
GPU have the ability to process lot of complex data
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4. The company also provides Linux Cloud, Microsoft Cloud etc.
5. The company offers a lot of such IaaS and DbaaS solutions
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About Company :
1. Company is provider of Advanced Cloud GPU Infrastructure to any company/institution to build and launch AI/ML applications.
2. Company is first Indian AI Hyperscaler Cloud provider.
3. E2E has partnered with NVIDIA for GPUs.
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Business is very niche and MOAT.
Company is very competitive in terms of pricing .
Now let’s understand the company and Business in Detail
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Company expects to grow at 40% CAGR,
Company plans expand its gross block to 2x its capacity by next year. And then 2x again an year later.
The execution depends on availability of funds the available market demand is huge for company to grow even faster.
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At the start of a long term bull market to find a company at 10 PE
which will grow at 40-50% CAGR for next few years in a microcap space is crazyy!
I could not believe how I missed even reading about the company before yesterday.
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The need for data processing is rising at unprecedented levels.
The company has a extremely huge addressable market size which will keep growing at approx 30 % CAGR for next 5-10 years.
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A long Thread on new addition to portfolio.
Stock Name : E2E Networks
Sector - AI/ML Cloud
Potential 10x candidate
Current Valuation is 300 cr approx.
Fy 23 Revenue is 65 Cr.
OPM is 50 percent
Operating Profit 33 Cr
FY 23 PAT is approx 10 cr.
BUT
FY 23 Cash profits are 30+ cr.
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Face aux comptes de taille comparable
10 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.
Vues médianes3 870ce compte924médiane pour 10K–100K
Portée, %20.20%ce compte3.62%médiane pour 10K–100K
Engagement, %0.68%ce compte1.52%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 3 870 | 924 | 4.19× |
| Portée (vues ÷ abonnés) | 20.20% | 3.62% | 5.58× |
| Taux d'engagement | 0.68% | 1.52% | 0.45× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
4.9K19 Jul
6.7K
6.6K
2.2K20 Jul
59.3K25 Aug
3.1K
4.6K
2.6K
2.8K
2.1K
80526 Aug
13.6K1 Sep
14.6K4 Sep
11.6K14 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.18%19 Jul
0.19%
0.49%
0.65%20 Jul
0.67%25 Aug
0.29%
0.43%
0.69%
0.82%
0.38%
0.37%26 Aug
0.88%1 Sep
0.91%4 Sep
0.71%14 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes58.3%1 383 in total
Reposts8.3%196 in total
Replies3.8%91 in total
Quotes0.4%10 in total
Bookmarks29.2%693 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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