Hello, I've made a product which is a SaaS Society Management Platform, similar to MyGate and NoBrokerHood. Currently I'm facing issues regarding the marketing and sale of this product. I need a direction to work in. The people I pitch to are usually society presidents or secretaries, and such people are either not interested or too non-techy to adopt such a service. I am unable to convert societies into customers. Please suggest a direction I should work in. I am also open if someone wants to work along with me, to grow the product.

Thanks.

85 AI TERMS EXPLAINED

Artificial Intelligence (AI)

Systems that perform tasks typically requiring human cognition: reasoning, learning, perception, decision-making.

Machine Learning (ML)

AI subset where systems learn patterns from data rather than following explicit programmed rules.

Deep Learning (DL)

Using multi-layered neural networks to learn hierarchical representations from large datasets.

Neural Network

Computational model using interconnected nodes (neurons) organized in layers to process information.

Learning Paradigms

• Supervised Learning: Training on labeled data where inputs are paired with correct outputs.
• Unsupervised Learning: Training on unlabeled data to discover hidden patterns or structures.
• Reinforcement Learning (RL): Learning through trial and error, optimizing actions based on reward signals.
• Self-Supervised Learning: Model generates its own labels from input data (e.g., predicting masked words).
• Transfer Learning: Applying knowledge from one task/domain to improve performance on another.
• Fine-Tuning: Adapting a pre-trained model to a specific task using targeted training.
• Zero-Shot Learning: Performing tasks without task-specific training examples.
• Few-Shot Learning: Learning from very limited examples (typically 1-10).

NLP & Language

• Natural Language Processing (NLP): AI focused on understanding, interpreting, and generating human language.
• Embedding: Dense vector representation capturing semantic meaning of text, images, or other data.
• Semantic Search: Search based on meaning/intent rather than keyword matching.
• Tokenization: Splitting text into tokens for model processing.
• Attention Mechanism: Allows models to weigh importance of different input parts when producing output.
• RLHF (Reinforcement Learning from Human Feedback): Training method using human preferences to align model behavior.

Model Architecture & Training

• Transformer: Architecture using self-attention mechanisms; foundation of modern LLMs.
• Large Language Model (LLM): Neural network trained on massive text data to understand and generate language.
• Foundation Model: Large model trained on broad data, adaptable to many downstream tasks.
• Parameters: Learnable weights in a model that adjust during training.
• Hyperparameters: Configuration settings fixed before training (learning rate, batch size, etc.).
• Training Data: Dataset used to teach a model patterns and relationships.
• Epoch: One complete pass through the entire training dataset.
• Batch Size: Number of samples processed before updating model parameters.
• Loss Function: Measures difference between predicted and actual outputs; guides optimization.
• Gradient Descent: Optimization algorithm that iteratively adjusts parameters to minimize loss.
• Backpropagation: Algorithm for computing gradients by propagating errors backward through the network.

Vision & Multimodal

• Computer Vision: AI that interprets and analyzes visual information from images/video.
• Multimodal AI: Models that process and relate multiple data types (text, image, audio).
• OCR (Optical Character Recognition): Converting images of text into machine-readable text.
• Image Segmentation: Partitioning an image into distinct regions or objects.
• Quantization: Reducing model precision (e.g., 32-bit to 8-bit) to decrease size and speed inference.
• Object Detection: Identifying and locating objects within images.

Generative AI

• Generative AI: AI that creates new content (text, images, audio, video, code).
• Prompt: Input text/instruction that directs a generative model's output.
• Token: Basic unit of text processing (word, subword, or character).
• Context Window: Maximum token length a model can process in one inference.
• Temperature: Parameter controlling output randomness; higher = more creative, lower = more deterministic.
• Inference: Using a trained model to generate predictions on new inputs.
• Diffusion Model: Generative model that learns to reverse a noise-adding process to create data.
• Autoregressive Model: Generates output sequentially, each token conditioned on previous ones.

Model Behavior & Problems

• Hallucination: Model generating plausible but factually incorrect or fabricated content.
• Overfitting: Model memorizes training data, failing to generalize to new inputs.
• Underfitting: Model too simple to capture underlying patterns in data.
• Bias: Systematic errors or unfair outcomes due to flawed data or design.
• Alignment: Ensuring AI systems behave according to human values and intentions.
• Jailbreaking: Attempts to bypass a model's safety constraints through adversarial prompts.
• Prompt Injection: Malicious inputs designed to override a model's instructions.

AI Agents & Systems

• AI Agent: System that perceives environment, reasons, and takes autonomous actions toward goals.
• Agentic AI: AI capable of multi-step planning, tool use, and independent task execution.
• Tool Use: AI's ability to invoke external functions, APIs, or services to complete tasks.
• Chain-of-Thought (CoT): Prompting technique that elicits step-by-step reasoning.
• RAG (Retrieval-Augmented Generation): Combining retrieval from external knowledge with generation for grounded outputs.
• MCP (Model Context Protocol): Open standard for connecting AI models to external tools and data sources.

Evaluation & Metrics

• Benchmark: Standardized test for comparing model performance.
• Accuracy: Proportion of correct predictions out of total predictions.
• Precision: Of positive predictions, proportion that are actually correct.
• Recall: Of actual positives, proportion correctly identified.
• F1 Score: Harmonic mean of precision and recall.
• Perplexity: Measures how well a language model predicts text; lower = better.
• BLEU Score: Metric for evaluating generated text against reference translations.
• Ground Truth: Verified correct answer used for training or evaluation.

Safety & Ethics

• Explainable AI (XAI): Methods making AI decision-making interpretable to humans.
• Red Teaming: Adversarial testing to identify vulnerabilities and failure modes.
• Constitutional AI: Training approach using principles to guide model behavior.
• Guardrails: Constraints preventing harmful or undesired model outputs.
• Data Privacy: Protecting personal information used in AI training and inference.
• Model Card: Documentation describing model's capabilities, limitations, and intended use.

Infrastructure & Deployment

• API (Application Programming Interface): Interface allowing software to interact with AI models programmatically.
• Latency: Time delay between input and model response.
• Throughput: Number of requests a system can handle per unit time.
• GPU (Graphics Processing Unit): Hardware accelerating parallel computations for AI training/inference.
• TPU (Tensor Processing Unit): Google's custom AI accelerator chips.
• Quantization: Reducing model precision (e.g., 32-bit to 8-bit) to decrease size and speed inference.
• Distillation: Training smaller models to mimic larger ones, preserving performance.
• Edge AI: Running AI models locally on devices rather than cloud servers.

Advanced Concepts

• Latent Space: Compressed representation space where similar concepts cluster together.
• Emergent Abilities: Capabilities appearing in large models not present in smaller versions.
• Scaling Laws: Predictable relationships between model size, data, compute, and performance.
• In-Context Learning: Model adapts to tasks from examples provided in the prompt, without weight updates.
• Synthetic Data: Artificially generated data used for training or augmentation.
• Mixture of Experts (MoE): Architecture where different subnetworks specialize in different inputs.
• AGI (Artificial General Intelligence): Hypothetical AI matching human-level reasoning across all cognitive domains.
• ASI (Artificial Superintelligence): Theoretical AI surpassing human intelligence in all areas.

I have had this conversation too many times.

Founder asks me how to raise money. I ask if they have applied for any government grants. They look at me like I said something weird.

Most founders know Startup India exists. Maybe they have heard of DPIIT recognition. That is about it.

What they do not know is that there is over Rs 12,000 crore in non-dilutive funding available for early stage Indian startups right now. No equity. No pitch deck. No investor meeting.

Just apply.

Here is everything I have found, verified as of April 2026.

BEFORE ANYTHING ELSE - GET THIS DONE FIRST

DPIIT Startup Recognition Almost every scheme below requires this. It is free, takes a few days, and without it you cannot access most of what follows. Do not skip this step. Apply here: startupindia.gov.in

CENTRAL GOVERNMENT SCHEMES

1. Startup India Seed Fund Scheme (SISFS) This is the one most early stage founders should start with. Up to Rs 20 lakh grant for proof of concept, prototype, or product trials. Up to Rs 50 lakh in convertible debt for commercialisation. If you have a working prototype and no revenue yet, this was made for you. Apply here: seedfund.startupindia.gov.in

2. Startup India Fund of Funds 2.0 Just approved in February 2026. Rs 10,000 crore corpus focused on deep tech, innovative manufacturing, and early growth stage startups. This does not go directly to you. It flows through VC funds. But it means more early stage capital is entering the ecosystem right now than at any point before. Read more: pmindia.gov.in

3. SAMRIDH Scheme - MeitY For software product startups. Matching funding up to Rs 40 lakh if you have been selected by a top tier accelerator. Also comes with mentorship and market access support which is honestly more valuable than the money for most founders at this stage. Apply here: samridh.meity.gov.in

4. TIDE 2.0 - Ministry of Electronics and IT If you are building in hardware, robotics, or IoT this is the one. Grant support specifically for those sectors which most general schemes ignore completely. Apply here: tide.meity.gov.in

5. Atal Innovation Mission Managed by NITI Aayog. Grants for early stage innovators in deep tech, AI, and emerging sectors. Particularly good for first-time founders and student entrepreneurs who do not have an existing track record. Apply here: aim.gov.in

6. BIRAC BIG Grant Up to Rs 1 crore for biotech and healthtech startups. Non-dilutive. If you are building anything in life sciences, diagnostics, or healthcare and you have not applied for this, stop reading and go apply right now. Apply here: birac.nic.in

7. iDEX DISC Up to Rs 1.5 crore. The largest government grant available to Indian startups. For defence and aerospace. Niche, but if you are in this space it is completely underused by founders. Apply here: idex.gov.in

8. MeitY GENESIS Up to Rs 1 crore for deep tech founders working in AI, quantum computing, and semiconductor design. Relatively new and very few founders know about it. Apply here: genesis.meity.gov.in

9. Credit Guarantee Scheme for Startups (CGSS) Not a grant. A loan guarantee. 85% cover on loans up to Rs 10 crore, 75% cover on loans between Rs 10 and Rs 20 crore. Zero collateral. If you need debt and do not want to pledge personal assets this is worth knowing. Apply here: ncgtc.in

10. PM Mudra Yojana - TarunPlus New category added in 2026. Loans between Rs 10 lakh and Rs 20 lakh. Zero collateral. 0.50% processing fee. For micro and small businesses at very early stage. Not glamorous but genuinely useful if you just need working capital. Apply here: mudra.org.in

11. Stand Up India Specifically for women founders and SC/ST founders. Collateral-free loans between Rs 10 lakh and Rs 1 crore. Severely underused. Apply here: standupmitra.in

12. Women Entrepreneurship Platform - WEP NITI Aayog initiative. Mentoring, networking, funding access, and training for women-led startups. Less about money, more about ecosystem access which is often harder to find than the money itself. Apply here: wep.gov.in

STATE SCHEMES - DO NOT IGNORE THESE

Karnataka - Elevate Program Up to Rs 50 lakh per startup. If you are building in Bangalore this is one of the best programs available to you and most founders have never heard of it. Apply here: startup.karnataka.gov.in

Maharashtra - MSInS Seed Fund Early stage grants plus access to a Rs 500 crore Maha-Fund for later stage startups. Apply here: msins.in

Tamil Nadu - TANSEED Up to Rs 15 lakh for early stage startups. Apply here: startuptn.in

Telangana - T-SEED Rs 250 crore corpus. One of the more active state programs. Apply here: tsic.telangana.gov.in

Uttar Pradesh Seed capital plus a Rs 1,000 crore fund of funds. Genuinely surprising how much UP has invested in its startup ecosystem recently. Apply here: startup.up.gov.in

Gujarat - SSIP 2.0 Specifically for student founders. If you are in college and building something in Gujarat this was made for you. Apply here: ssip.uiet.edu.in

SECTOR SPECIFIC ONES WORTH KNOWING

Agritech founders: drone-based agricultural subsidies and cold-chain logistics subsidies are available but almost nobody in the startup ecosystem talks about them.

Spacetech founders: IN-SPACE scheme and ANIC are your starting points.

Deep tech founders: GENESIS for AI, quantum computing, and semiconductor design.

HOW TO APPROACH THIS

Do not try to apply for everything at once. Pick the one that matches your current stage and sector. Get your DPIIT recognition done first. Then prepare your business plan properly before applying.

The founders who get rejected are usually the ones who apply carelessly. The ones who get funded treat it like an investor pitch. Same rigour, different audience.

Have you applied for any of these? Did it work? Did you get rejected? Drop your experience below. This thread will be more useful with real stories from real founders. 👇

— u/harshamv

Founding Moderator, r/AllIndiaStartups

Over the past couple of years, our team has helped 13 startups collectively raise ₹52 crores through government schemes, grants, and investor connections.

Sharing this because the number of founders who don't know how much is available to them is honestly surprising. Schemes like PMEGP, MUDRA, CGTMSE, Stand Up India, and Startup India have real money in them — the barrier isn't eligibility, it's navigating the process.

We've recently expanded our tie-ups across multiple new government sectors, which means more businesses now qualify than before.

Here's what we handle for you:

1.Pitch deck built around your specific business

2.Investor-ready business plan

3.Applications filed across all relevant schemes and grants on your behalf

4.Connections to equity investors where it makes sense

5.Continued consulting through your growth phase, not just until the paperwork is done

I personally stay accountable for every client we work with. If something stalls or isn't moving, you come back to me directly — not a support ticket, not a junior executive.(promotion)

If you're an SME owner, sole proprietor, or at the idea stage trying to figure out where to begin, drop a comment or DM me. I'll tell you honestly whether you qualify and which schemes fit your business. No commitment needed for that conversation.