Who's pain point and what is the pain?
Academics,Technical workers,Small teams ,MSEs are frustrated have hard time building and integrating very good intelligent GenAI solutions,work flows,etc. in their business processes and integrating data and business knowledge into best in class AI knowledge bases is even more difficult and no reliable solution.They can't keep buying and managing 100 different GenAI products and tools and have very limited resources at disposal and generic chatbots are of no much use they feel very over whelmed due to uncertain future of their jobs,businesses.In short accelerating actionables is a distant dream and no match to enterprise.This is disastrous in many ways!

Problem...how wide spread and Solution...how good?
It is a very good and scalable solution(see thesis) to problem of user case adaptability ,implementation quality, intent,context disconnect with AI systems and fragmented,siloed user data.Copilots can help solve only very specific problems,and cant help users build incremental solutions.Our product has huge potential to accelerate actionables through function calling and tool use,data modelling, decision making, engineering design,planning , code, research automation ,business use case integration by 100x _1000x and people dont need to buy 100 different products for various tasks.Also it's easier to ship many "good to have features" too!

Why an AI Agentic workspace?
Because we want to build a "go to" genAI ecosystem! Intelligence is already a commodity and We see a huge amount number of synergy building opportunity here in terms of secure user level data integration and advaced reasoning with agents.Our agents work in sync for reasoning at various levels of abstraction.And we build each of them to do the job very reliably.Even Microsoft is working on "copilot studio" very similar produt.

Our initail target segments...we have spoken a lot to and they will buy
Mainly Academics,Technical,Commerce Professionals,Researchers,Teams to adapt way of work in a post AGI world where most value creation will be with AI Agents, but everyone can still be a stake holder building on top of what they have and "AGI" level tech is coming really fast our product contributes to making the transition smooth!

What are things we understand better most peers and incumbents don't?
There is no breakthrough solution(only fragmented solutions) for above mentioned segments in Agentic AI workspace category.Enterprise players have huge many unfair advantages,for AI agents to understand human intents,stakes,objectives,aspirations and jobs better and better to help them build incremental AI solution...there is a lot of content of people connecting many scipts, tools softwares for their use cases but it is painful,time consuming and not good enough tech in most aspects...our product solves it bridging the gap...by accelerating their parallel action automations,data modelling, design, planning, organization,research.Every one deserves to be empowered!

How do we see the GAP and what are data points?
With better training data sets ,models & optimization techniques coming in its more viable approach to go about business to simplify data sharing, integration, user intent based actions with GenAI models,even if we dont use user data we can learn and build upon great deal about work flows in various industries.Building moats in form of public AI Agents ecosystem is easy...we have similar example of dev tools like Langchain,Llama Index these have their own problems and challanges to adoption though.

Whats impact value and moats will we be creating in next 5 years?
A lot in terms of market penetration especially in India and we will are building a product what people can't imagine living a day without! On top of this we aspire to create a huge open public and private business grade AI knowledge bases and data sets platform for people just like Github is for code.IIt's not about what we have ...it's about how we plan to use it.

Can we scale,out run the market and survive the bubble?
Yes, we gave it a shot and we think we can viably out run markets ;if we work persistently in this direction, we understand the math,innovation, architectures and bottle necks very well , we understand what is hype and what isn't, we know there are lot of other problems to get easier money but this is crucial tipping point ,there lot to learn and our calling for us to create a greater impact in long run.

Can we make the mark?
We are persistent, smart,patient ,down to earth, frugal, aggressive and have habit of building and testing hypotheses properly.There is steep competition,we believe most of peer ventures won't survive but we will.SAAS is rapidly being replaced with better Agentic solutions,mixed multi modal models and ASIC chips are just picking up and per unit inference costs are expected to go down drasticaly.

What really Inspires us?
Some projects like alpha geometry,alpha chip,etc. We are deeply inspired by problem solving SOPs of Google Deep Mind and we want to build, democratize and accelerate such tech with this approach and we think this is a tipping point and is going to give huge boost to ML Applications research in India.

What's the market scenario?
Most of people are scared and don't want to be replaced by AIs and if this at all happens the economy may suffer it's already happening in some places ,Material impact is overwhelming and competition is steep...with products like Claude computer use coming in,GenAI features being built into OSes,Advanced Robots with sensor fusion technologies arent far. Design is becoming automatic,code is becoming automatic,curation is becoming automatic...just everything but innovation and ingenuity.

How much are we looking to raise and whats our customer aquisition strategy, what will be initial CAC?
We are looking to raise a pre seed round of $700k to acclerate our sales and product development loop.We will acquire customers through influencer marketing,social media content strategy and partnerships with Academic institutions , we take inspiration from x11, CursorAI and Langchain.We will build short form content on use cases that appeal to some user segments the most.Our estimated initial CAC will be 0.1$ per user for early adopters in India...we don't yet know about global markets in terms of CAC and have to work out pricing for them too.

Reinforcement for our product hypothesis and the solution...a Tweet by SPC

Interestingly ... some problems we have studied and worked on converge with a tweet by SPC(South Park Commons - an elite technical community and fund backing founders at the earliest stages, known for supporting deep tech innovations)