Weekend Project: Build a Personal CRM Micro‑App with Scraped Leads and an LLM Assistant

Build a Personal CRM Micro‑App in a Weekend: Scrape Leads, Store Them, and Add an LLM Assistant

Hook: If you’re a solo founder, indie seller, or small sales team who wastes hours chasing leads, this weekend project will give you a lightweight, reliable personal CRM that scrapes target pages for leads, stores structured data, and adds an LLM assistant to prioritize and draft follow‑ups—without buying enterprise software.

Why this micro‑app matters in 2026

Micro apps—small, single‑purpose apps built quickly for personal or team use—exploded in popularity through 2024–2026. With improved LLMs (including local models and Claude‑style assistants) and better developer tooling, building a functional CRM micro‑app in 48–72 hours is realistic. In late 2025 and early 2026 we’ve seen two trends that enable this approach:

• LLM tool use and agent capabilities—models now integrate with APIs and handle retrieval-augmented generation (RAG) reliably, enabling assistants that can summarize context, draft emails, and suggest next steps.
• Jam‑stack and micro services—lightweight frontends (Next.js, SvelteKit), serverless functions, and affordable vector DBs make deployment and iteration fast.

Project scope and what you’ll ship

In this guide you’ll build a micro‑CRM that:

• Scrapes lead pages (listings, directories, LinkedIn company pages, conferences) and extracts name, title, company, email (when present), and snippet of context.
• Stores structured leads in a relational DB plus embeddings in a vector DB for retrieval.
• Provides an LLM assistant that recommends follow‑up stages, drafts personalized emails, and schedules reminders.
• Includes basic integrations: SMTP/Gmail for sending, plus a minimal UI for review and tagging.

Architecture overview (simple and production‑aware)

Use a modular architecture so you can replace parts as needs change:

1. Scraper: Playwright or Puppeteer (JS) or Playwright for Python. Runs on schedule or on demand.
2. Extractor: DOM selectors + fallback heuristics and regex for emails.
3. Store: Postgres/SQLite for leads and metadata; vector DB (Pinecone / Weaviate / Milvus / pgvector) for embeddings.
4. LLM Assistant: OpenAI/Anthropic API or a local LLM (Llama 3 variants) for RAG + prompt templates.
5. Automation: Serverless function / cron job for periodic scraping, email sending, and reminders.
6. UI: Lightweight Next.js or SvelteKit app to view leads, run assistant prompts, and send messages.

Step‑by‑step weekend plan (48–72 hour schedule)

Follow this schedule to stay focused. Each step contains practical commands and code snippets to get you running.

Day 1 — Core scraping + storage

1. Pick a target: local business directory, conference attendee page, or a public company directory. Confirm scraping is allowed per site terms and robots.txt.
2. Spin up a minimal Postgres (or SQLite) and create a leads table. Example SQL:

-- leads table (Postgres)
  CREATE TABLE leads (
    id SERIAL PRIMARY KEY,
    name TEXT,
    title TEXT,
    company TEXT,
    email TEXT,
    source_url TEXT,
    snippet TEXT,
    scraped_at TIMESTAMP DEFAULT now(),
    stage TEXT DEFAULT 'new'
  );
  

3. Implement a simple scraper using Playwright (Python example). This approach handles dynamic pages and is resilient to JS‑rendered sites.

# requirements: playwright, asyncpg
  import asyncio
  from playwright.async_api import async_playwright
  import asyncpg
  import re

  async def extract_emails(text):
      return re.findall(r"[\w.-]+@[\w.-]+\.\w+", text)

  async def scrape(url, pg_dsn):
      conn = await async_playwright().start()
      browser = await conn.chromium.launch()
      page = await browser.new_page()
      await page.goto(url)
      html = await page.content()
      # TODO: replace selectors below
      items = await page.query_selector_all('.list-item')
      leads = []
      for it in items:
          name = await it.query_selector_eval('.name', 'el => el.innerText')
          title = await it.query_selector_eval('.title', 'el => el.innerText')
          company = await it.query_selector_eval('.company', 'el => el.innerText')
          snippet = await it.query_selector_eval('.bio', 'el => el.innerText')
          emails = await extract_emails(snippet + ' ' + name)
          email = emails[0] if emails else None
          leads.append((name, title, company, email, url, snippet))
      await browser.close()
      # Save to Postgres
      pg = await asyncpg.connect(pg_dsn)
      await pg.executemany('INSERT INTO leads (name,title,company,email,source_url,snippet) VALUES ($1,$2,$3,$4,$5,$6)', leads)
      await pg.close()

  asyncio.run(scrape('https://example.com/directory', 'postgres://user:pass@localhost/db'))
  

Tip: Start with a small sample (10–50 rows) to validate extraction rules. Use robust selectors and fallback heuristics—if a selector fails, fall back to regex or adjacent fields.

Day 2 — Embeddings, LLM assistant, and basic UI

1. Add a vector DB. If you want zero‑ops for a weekend, use Pinecone or a managed Weaviate. For self‑hosted, pgvector in Postgres is compact and fast for small datasets.
2. Generate embeddings for lead context to enable semantic search. Example using OpenAI embeddings (2026-compatible API):

import openai
  openai.api_key = 'YOUR_KEY'

  def get_embedding(text):
      r = openai.Embeddings.create(model='text-embedding-3-large', input=text)
      return r['data'][0]['embedding']
  
  # store embedding vector in pgvector or send to Pinecone
  

3. Build the assistant flow: Retrieval → Prompt Template → Generate. The assistant should:

• Fetch the lead record and nearest semantic neighbors (previous interactions or similar leads).
• Call the LLM with a concise system prompt and a few-shot template to generate a prioritized next action and a draft email.

# Pseudocode for assistant
  lead = db.get_lead(lead_id)
  neighbors = vector_db.query(lead.snippet, top_k=5)
  prompt = f"You are a sales assistant. Context: {lead.snippet}\nSimilar leads: {neighbors}\nTask: Suggest next step and draft a 3-sentence email to {lead.name} about {lead.company}."
  response = openai.ChatCompletion.create(model='gpt-4o', messages=[{'role':'system','content':'You are a helpful assistant.'},{'role':'user','content':prompt}])
  
  suggested_action = parse_action(response)
  draft_email = response
  

Practical prompt patterns:

• Instruction-first: "Given this context, suggest a single next action and a concise email draft."
• Constraints: 2–3 sentences, mention previous context, include a one‑line subject.
• Safety: instruct the LLM to avoid hallucinating contact info or promises.

Day 3 — Automations, sending, and polish

1. Set up a send pipeline: either SMTP/SendGrid or Gmail API with OAuth for personal use. Implement a scheduled job for follow‑up sequences and reminders.
2. Implement a simple UI to review LLM drafts and send or edit them.
3. Add logging, rate limits, and retry logic for both scraping and email sending.

Code snippets: Integrations you’ll need

1) pgvector insert example (Python + psycopg)

import psycopg2
  conn = psycopg2.connect('postgresql://user:pass@localhost/db')
  cur = conn.cursor()
  cur.execute("INSERT INTO leads (name, snippet, embedding) VALUES (%s,%s,%s)", (name, snippet, embedding))
  conn.commit()
  

2) Simple Next.js API route to call your assistant (Node/JS example)

// api/assistant.js (Next.js)
  import fetch from 'node-fetch'
  export default async function handler(req, res) {
    const { leadId } = req.body
    // fetch lead & neighbors from your API
    const response = await fetch(`${process.env.BACKEND}/lead/${leadId}`)
    const lead = await response.json()
    const llm = await fetch(process.env.LLM_API, { method: 'POST', body: JSON.stringify({ prompt: `Draft... ${lead.snippet}` }) })
    const draft = await llm.json()
    res.status(200).json({ draft })
  }
  

Key operational considerations (what trips teams up)

Scraping and personal CRMs are deceptively simple until scale or legal risk arrives. Here’s what to tackle early.

Respect site rules and privacy

• Check robots.txt and the site’s ToS. Some directories explicitly forbid scraping; treat email harvesting as high risk.
• Follow local privacy laws (GDPR, CCPA). Don’t store or contact personal data without legitimate purpose and lawful basis.

Handle anti‑scraping defenses

• Use human‑like delays, randomized UA strings, and headful browsers if necessary.
• For any production‑scale scraping, use rotating residential or datacenter proxies and backoff on HTTP 429/503 responses.
• Solve CAPTCHAs with a service if you have permission to scrape—otherwise, avoid pages behind aggressive protections.

Costs and throughput (benchmarks for planning)

These are 2026 rough estimates for a small setup:

• Scraping throughput: Playwright + 2–4 concurrent browsers can handle ~200–600 lightweight pages/hour. JS‑heavy pages are slower.
• Embedding costs: OpenAI/Anthropic embeddings vary—expect $0.0004–$0.002 per embedding depending on model and vendor; 1,000 leads ≈ $0.4–$2.
• LLM generation costs: A single follow‑up draft using a mid‑range model costs a few cents; sophisticated multi‑turn coaching costs more.
• Vector DB: Managed services cost a few cents per 1k vectors; self‑hosted costs are server costs (low for small datasets).

LLM assistant design patterns

Design your assistant with predictable behavior:

• One job per call: either summarize, draft, or prioritize—avoid multi‑purpose prompts that produce unreliable output.
• RAG with explainability: include the top 3 evidence snippets passed to the model so you can show why it suggested an action. For more on edge-driven personalization and evidence-forward design, see edge signals & personalization.
• Stateless prompts + stored context: store the prompt and result in audit logs to retrain your prompts later; consult the developer guide to offering content as compliant training data for guidance.

Example assistant prompt (concise)

"You are an expert outbound assistant. Given the lead context and prior notes, recommend one next action and draft a 2‑sentence email subject + body. Return JSON with action, subject, body, and rationale (1 sentence). Do not invent contact info."

Follow‑up sequencing and automation

Implement a small state machine for lead stages: new → contacted → interested → follow‑up → closed. Automate sequences but always require manual approval before the first outreach from a personal account.

1. Draft stage: assistant generates a draft and suggested send time.
2. Review stage: you edit/approve the draft in the UI.
3. Send stage: the system queues and sends with retries and bounce handling.

Templates and cadence

• Initial outreach: 2 short sentences + clear CTA (call or 15‑min demo).
• Follow‑up 1: Remind, add value (link or data point) after 3–5 days.
• Final nudge: Friendly breakup after 7–10 days.

Security, privacy, and trust (non‑negotiables)

As you gather personal data, lock it down:

• Encrypt data at rest and in transit. Use TLS for all endpoints and disk encryption for servers.
• Restrict access to API keys and use short‑lived credentials where possible.
• Log actions for auditability: who sent what and when, and store LLM prompts/responses for debugging and compliance.

Advanced moves for next iterations

Once the MVP is stable, add these features to increase productivity and reliability:

• Two‑way email logging and reply parsing to update lead state automatically.
• Integrations with Calendly or scheduling APIs to propose available slots in the assistant drafts.
• Automated enrichment: enrich leads with Clearbit/FullContact or public company data to improve personalization.
• Local LLM fallback: run a small Llama 3‑family model on a cheap GPU or single-board option for on‑premise prompts or cost reduction.
• Autonomous agents (carefully): use agent frameworks to triage leads, but always require human‑in‑the‑loop for outbound contact to avoid compliance issues; for governance and partnership implications see AI partnerships and governance guidance.

Real‑world example: Solo founder use case

Case: Sarah, a solo SaaS founder, scraped 1,200 startup directories over two weekends, enriched 600 reachable contacts, and used an LLM assistant to draft personalized messages. Her process:

1. Scraped directories with Playwright, saved 1,200 rows.
2. Filtered by title keywords and enriched 600 contacts with company size.
3. Generated drafts via LLM and reviewed 100/day, sending 20/day with manual approval.
4. Closed 4 meetings in month one; time saved: approx. 15–20 hours vs manual research.

This mirrors trends in 2025–26 where micro‑apps amplify single users’ productivity—combining scraping, semantic search, and LLMs to replace expensive CRM seats for early outreach.

Common pitfalls and how to avoid them

• Over‑automation: Sending aggressive auto‑emails without manual review increases spam risk and legal exposure. Always require human approval for the first contact.
• Poor data hygiene: Deduplicate leads (email, name+company) and normalize fields to avoid repeated outreach.
• Relying on a single LLM vendor: Have a fallback/resilience plan—cost spikes or API outages happen; track cloud vendor risk such as mergers and service changes in the market (see cloud vendor playbook).

Checklist for launch

• Data model: leads table + embedding store in place.
• Scraper: works reliably for your target site and respects rate limits.
• LLM assistant: produces consistent, editable drafts and stores prompts/results.
• Send pipeline: authenticated mail or API with retry and bounce handling.
• Security: keys safe, TLS enabled, basic logging enabled.
• Legal: reviewed robots.txt, ToS, and privacy considerations for stored contacts.

Future trends to watch (late 2025 → 2026)

Keep an eye on these developments that will shape micro‑CRM capabilities:

• Local, high‑quality LLMs: As Llama 3 family models and similar continue maturing through 2026, on‑device or single‑GPU options will lower costs and latency for assistants; see guides on building a local lab for options and tradeoffs.
• Agentization and secure tool use: Agents that can act on your behalf with file system and API access will make more autonomous flows possible—but they increase governance needs.
• Better embedding interoperability: Standardized embeddings and vector formats will simplify switching vector DBs and vendors.

Actionable takeaways

• Start small: Validate your scraping and extraction on a 50–100 lead sample before a full crawl.
• RAG is a force multiplier: Storing context as embeddings lets the assistant produce much better personalized drafts.
• Keep humans in the loop: Require approval for first outreach and log everything for compliance.
• Monitor costs: Track embedding and LLM usage—implement a cheap local model fallback if cost spikes.

Ready to build?

This weekend project gives you a functional, maintainable personal CRM micro‑app that combines web scraping, structured storage, a vectorized knowledge layer, and an LLM assistant tailored for outreach workflows. You’ll ship an MVP in 48–72 hours and iterate from there.

Next steps: Pick one target site, spin up a Postgres or SQLite instance, and run the sample Playwright scraper above to collect your first 20 leads. Then add embeddings and an assistant—test with one contact per day until the flow is smooth. If you want implementation examples for micro apps on simpler platforms, check out resources on micro‑apps on WordPress.

Call to action

If you want a head start, download a starter repo that includes a Playwright scraper, Postgres schema, embedding pipeline, and a sample Next.js UI—tailored for lead scraping and LLM‑driven follow‑ups. Build the micro‑CRM, tweak the assistant prompts for your use case, and share your results with the community.

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