AGENTS

PM Smart AI Effort Estimator Agent

Agent for project quoting & effort estimation — vector KB replaces experience memory, batch-generates quotes in seconds.

Core Pain Points

1. Manual estimation depends on experience: Project quotation and effort estimation rely on historical memory and heuristic judgment — easily affected by individual experience differences.
2. Slow response: Humans need to read requirements item by item, find similar tasks, and consolidate — the quotation cycle can stretch to weeks.
3. Insufficient reuse identification and batch processing: Similar requirements, reusable code, and baseline items are hard to match accurately; when faced with large batches of requirements, manual one-by-one handling is costly.

Solution & Technical Implementation

1. Multi-format input parsing: Supports requirement and effort documents in Excel, Word, PDF, TXT, and more; also supports automatic image parsing.
2. Historical knowledge base + vector tags: Adds historical projects to AI vectors and intelligent tags, building a retrievable knowledge base.
3. Real-time semantic estimation: Through semantic matching and industry calibration, matches new requirements to appropriate baseline items, forming structured quotations.
4. Parallel output: Processes requirement batches with high concurrency, generating structured quotation results for 10M+ requirements within minutes.

Highlights

• Seconds-level knowledge retrieval: Shifts from memory-dependence to vector matching.
• Accurate reuse identification: Replaces heuristic guessing with vector retrieval.
• Balancing speed, accuracy, and reasonableness: Maps to faster startup, reduced errors, and more reasonable task matching, respectively.
• Large-scale parallel: Suitable for centralized estimation of massive requirement sets.

Business Value

• PPT quantified comparison: manual ~2 person-weeks, AI ~1 person-day; quotations are obtained within minutes after submission.
• Reduces matching errors and improves code-reuse rate, helping lower project costs.
• Historical knowledge base can be reused across multiple projects and divisions, supporting large-scale pre-sales.
• Quotation results, task tags, and actual delivery data can continuously feed back to gradually improve estimation accuracy.

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