# Estimate Your AI Data Preparation Investment AI Agent Connect > AI Data Preparation Estimator calculates the total investment required to get your raw data ready for machine learning. This MCP estimates costs, project timelines, and projected data quality based on data volume, cleaning intensity, labeling needs, and preprocessing complexity. You can get a full picture of the investment using the `get_prep_summary` tool, or drill down with specific estimates for budget, timeline, and quality score. ## Overview - **Category:** analytics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_kwWZZlX9cEbWk5rrXHbqISdKwxwjnWrlaO34C5Bd/ai-agent-connect - **Tags:** data-prep, ai-investment, machine-learning, data-quality, cost-estimation ## Description Starting an AI project is rarely about the model; it's about the data. The biggest risk is underestimating the effort and cost of data preparation. This MCP helps you figure out exactly what that investment looks like. You feed it your variables—how big the data is, how dirty it is, and how much labeling you need—and it gives you hard numbers. Need a full picture? Use the `get_prep_summary` tool for a complete overview of the investment, time, and quality. If you only care about the money, `estimate_preparation_budget` tells you the total cost. If you need to talk to a project manager, `estimate_preparation_timeline` gives you the weeks required. And if you're worried about the final product, `predict_data_quality_score` tells you what the data will look like after prep. It’s the planning tool you need before you commit to a massive data pipeline. ## Tools ### estimate_preparation_timeline Answers "How many weeks will the data preparation phase take?" ### get_prep_summary Answers "Give me a complete overview of the investment, time, and quality for my plan." ### predict_data_quality_score Answers "What will the quality of the prepared data be based on the chosen strategy?" ### estimate_preparation_budget Answers "How much money will it cost to prepare this dataset?" ## Prompt Examples **Prompt:** ``` How much will it cost to prepare 500GB of data with high cleaning intensity and 1000 labeling samples at moderate complexity? ``` **Response:** ``` The total estimated cost for this preparation is $12,500, consisting of $8,000 for automated processing and $4,500 for manual labeling. ``` **Prompt:** ``` How many weeks will it take to prepare 100GB of simple data with 50 labeling samples using automated heavy processing? ``` **Response:** ``` The estimated timeline for this task is 2 weeks. ``` **Prompt:** ``` What is the expected quality score for 1000GB of data with low cleaning and 5000 labeling samples at complex difficulty? ``` **Response:** ``` The projected quality score is 72% with a confidence interval of +/- 5%. ``` ## Capabilities ### Calculate total budget Use this when you need to know the dollar cost of data cleaning, labeling, and preprocessing. ### Estimate project duration This is useful for setting realistic deadlines and communicating timelines to stakeholders. ### Predict data quality score Run this when you need to know if your chosen preparation strategy will result in high-quality training data. ### Get full investment overview Call this when you need a single, comprehensive report covering cost, time, and quality simultaneously. ## Use Cases ### Starting a new image recognition project You need to know if your current dataset size and required labeling effort will fit within the quarter's budget and timeline. ### Evaluating legacy data sources Before committing to migrating old data, use the MCP to estimate the cost and time needed to clean and standardize the records. ### Pitching an AI product to investors Generate a detailed, data-backed investment summary that shows exactly what the data preparation phase requires. ### Scaling a successful pilot project Determine if the current data preparation process can handle a 10x increase in data volume without ballooning costs or delays. ## Benefits - Pinpoint resource needs, preventing unexpected budget overruns from data cleaning or labeling. - Set accurate project deadlines by calculating the precise timeline required for data readiness. - Quantify data risk by predicting the quality score before the data is processed. - Consolidate complex variables—volume, intensity, complexity—into actionable financial and time metrics. ## How It Works Connect your AI client to this MCP. You provide the variables—data size, cleaning level, and labeling requirements—and the MCP returns concrete financial, temporal, and quality estimates. 1. Connect your preferred AI client (Claude, Cursor, Windsurf, etc.) to the Vinkius Catalog. 2. Ask your agent to run the `get_prep_summary` tool, providing the dataset variables. 3. The MCP processes the variables and returns a structured estimate. 4. You receive the total cost, the projected timeline, and the expected data quality score. ## Frequently Asked Questions **Does this estimate cover labeling costs?** Yes. The tools factor in labeling needs and complexity. The budget estimate accounts for both automated processing and the cost associated with manual labeling efforts. **What variables do I need to provide?** You must provide details on the data volume, the required cleaning intensity, the number of labeling samples, and the overall preprocessing complexity. **Is this just a rough guess?** No. The MCP uses established models to calculate the investment. It provides measurable estimates for budget, timeline, and quality score, giving you concrete planning data. **Can I get a summary or separate estimates?** You can use the `get_prep_summary` tool for a full overview, or you can use specific tools like `estimate_preparation_budget` or `estimate_preparation_timeline` if you only need one metric.