Apply for AI Grants India

Start by verifying what CLAW means

Before writing code, confirm the hackathon’s official definition of CLAW, its permitted tools, submission format, judging rubric, and intellectual-property rules. “CLAW” may refer to a named event, platform, agent framework, or challenge theme; it should not be assumed to be a standalone programming language or a package that can simply be installed with pip.

Read the organiser’s documentation, starter repository, API limits, model policy, and examples. If the event provides an SDK, use its pinned version and record setup steps in your README. If CLAW is a framework for AI agents, your project should demonstrate a useful workflow around it—not merely add a chatbot interface.

Pick a narrow problem with a visible outcome

Strong hackathon projects solve one specific problem for one identifiable user. Avoid broad claims such as “improve healthcare with AI.” Instead, define a task that can be demonstrated in two minutes:

• A district hospital worker summarises a Hindi referral note and flags missing information.
• A small manufacturer receives an explanation for an abnormal sensor reading.
• A student gets feedback on a scholarship application against a published checklist.
• A local-language support team routes incoming requests and drafts a response for review.

For India-focused projects, account for multilingual input, intermittent connectivity, low-cost devices, privacy, and uneven data quality. If your idea involves Indic languages, the guide to low-resource Indic natural language processing is useful for thinking through datasets, evaluation, transliteration, and language-specific failure modes.

Write a one-sentence problem statement:

> For [specific user], who struggles with [specific task], we will build [workflow] that produces [measurable outcome] using CLAW.

Then define what is explicitly out of scope. A smaller, reliable project usually scores better than an ambitious system with an unfinished demo.

Design the smallest credible architecture

Map the user journey before selecting models. A practical CLAW project can usually be divided into five components:

1. Input — text, audio, image, form data, or an API request.
2. Orchestration — CLAW routes the request, selects tools, and manages state.
3. Tools and data — retrieval, calculators, databases, search, or deterministic code.
4. Verification — schema checks, source citations, confidence thresholds, and human approval.
5. Output — an answer, recommendation, structured record, alert, or next action.

Do not use an autonomous agent where a fixed pipeline is safer. Use an agent when the task genuinely requires tool selection, multi-step reasoning, or adapting to different inputs. For more complex workflows, study patterns from building distributed systems with AI agents, but keep the hackathon implementation small enough to observe and debug.

A sensible first version might use one model, two tools, a lightweight database, and a web interface. Add a second agent only when it solves a demonstrated bottleneck. Every additional model or tool increases latency, cost, and the number of failure cases you must explain.

Build a vertical slice first

Your first milestone should be a complete path from input to useful output, even if the interface is rough. Create a small test set of 20–50 realistic examples before polishing the UI. Include normal cases, incomplete inputs, ambiguous requests, adversarial prompts, and examples in the languages your project claims to support.

Implement the happy path, then add guardrails:

• Validate inputs and constrain tool arguments with schemas.
• Keep secrets in environment variables, never in the repository.
• Log tool calls, latency, errors, and model versions without storing unnecessary personal data.
• Set timeouts, retry limits, token budgets, and fallback responses.
• Require confirmation before actions that send messages, modify records, or make consequential recommendations.
• Show citations or retrieved evidence when the output depends on external information.

If your project is voice-first, separate speech recognition, reasoning, and speech synthesis so each layer can be tested independently. The voice agent architecture and deployment guide covers the trade-offs between latency, turn-taking, and deployment choices.

Evaluate the system, not just the model

A high accuracy number is not enough. Define evaluation around the user’s outcome. Depending on the project, measure:

• Task success: Did the user complete the intended action?
• Grounding: Are claims supported by the supplied documents or sources?
• Extraction quality: Precision, recall, and F1 for structured fields.
• Reliability: Failure rate, timeout rate, and consistency across repeated runs.
• Latency and cost: Median and worst-case response time plus cost per task.
• Safety: Incorrect advice, privacy leakage, prompt injection, and unsafe tool use.
• Accessibility: Performance across languages, accents, devices, and connectivity conditions.

Create a simple evaluation table with the input, expected behaviour, actual output, severity, and fix. Separate blocking errors from cosmetic issues. For a legal, financial, medical, or public-service use case, position the system as decision support unless you have strong validation and qualified oversight.

Make the demo reproducible

Judges should see the value quickly. Structure the presentation around one user and one before-and-after workflow:

1. State the problem and who experiences it.
2. Show a realistic input, including relevant Indian context where appropriate.
3. Explain what CLAW orchestrates and which tools the system calls.
4. Display the output, evidence, and any human approval step.
5. Demonstrate one failure case and how the system handles it.
6. Report evaluation results, latency, approximate cost, and limitations.

Prepare a recorded backup in case an API, network connection, or live service fails. Seed the demo environment with safe, synthetic data. Never expose real Aadhaar numbers, medical records, financial details, or private conversations.

Your repository should include a concise README, architecture diagram, .env.example, installation commands, sample inputs, evaluation instructions, licence details, and a short limitations section. If you are building a portfolio-quality submission, compare your work with open-source AI projects for student developers and adopt their discipline around documentation and reproducibility.

Plan the final 24 hours

Freeze the feature set early. Use the remaining time to improve reliability, not to add speculative capabilities. A practical checklist is:

• Pin dependencies and test a clean installation.
• Run the full evaluation set after every major change.
• Add visible loading, error, and empty states.
• Verify that API keys and personal data are excluded from commits and logs.
• Record a fallback demo and export screenshots.
• Ask someone unfamiliar with the project to follow the README.
• Rehearse a three-minute explanation of the problem, architecture, evidence, and limitations.

A CLAW hackathon project succeeds when it is useful, testable, explainable, and easy to run. Choose a narrow Indian problem, build a complete vertical slice, use agents only where they add value, and prove performance with realistic evaluation rather than impressive claims.