Building AI Automations with the Right Model and Tool

Automations work best when you stop treating "the AI" as one thing.

Most useful workflows have two separate decisions:

1. Which model should think about this step?
2. Which tool should provide the data or action?

NanoGPT is set up for that style of work. You can use a fast model for cheap classification, a reasoning model for the hard synthesis step, web search for current facts, memory for long-running context, and specialized tools for structured data from websites, social platforms, maps, email lookup, image generation, and more.

The basic pattern

A good automation is usually a pipeline, not a single prompt.

For example:

1. Gather data with a tool.
2. Clean or extract the useful fields with a cheap model.
3. Use a stronger model only when the task needs judgment.
4. Save the result, send it somewhere, or run the next tool.
5. Keep state with memory if the workflow runs repeatedly.

This is more reliable and usually much cheaper than sending every step to the most expensive model.

Pick the model for the step

Different parts of an automation need different models.

Fast and cheap models are best for high-volume work:

• tagging support tickets
• extracting names, URLs, prices, dates, or categories
• rewriting short snippets
• deciding whether a scraped item is relevant
• producing a quick summary before a deeper pass

Use models like GPT-4o Mini, Gemini Flash, GLM Turbo-style models, or the lower-cost open-source models for this layer. If a task has hundreds or thousands of rows, start here.

Reasoning models are best for the parts where mistakes are expensive:

• comparing conflicting sources
• writing a final recommendation
• planning a multi-step agent run
• debugging code
• deciding what to do after a tool returns messy data
• synthesizing a weekly or monthly report

For these steps, use stronger models like Claude Sonnet Thinking, Claude Opus, Gemini Pro, GPT-5-class models, Kimi, GLM, or auto-model if you want NanoGPT to pick a suitable model automatically.

Multimodal and media models are best when the output is not just text:

• image generation for product concepts, ads, thumbnails, or social assets
• video generation for short creative experiments
• audio and TTS for voiceovers, summaries, and spoken updates
• vision models for screenshots, receipts, product photos, or UI checks

Do not force a text model to describe an image prompt forever if the next useful step is generating the image.

Add tools for fresh or structured data

Models are useful, but tools make automations concrete.

NanoGPT now has several ways to connect models to data and actions:

Tool type	Use it for
Web search	Current events, product releases, pricing pages, source-backed answers
Context Memory	Long-running automations that need to remember previous runs
MCP	Connecting NanoGPT to agent clients like Claude Code and Cursor
Website Crawler	Scraping pages, mapping site links, extracting markdown, screenshots, summaries, and PDFs
Reddit Scraper	Monitoring posts, comments, communities, users, and search results
X Research	Searching and inspecting public X posts, users, timelines, lists, and Spaces
TikTok Scraper	Tracking hashtags, profiles, searches, video URLs, comments, subtitles, and metadata
Instagram Scrapers	Pulling profile data, posts, captions, metrics, comments, and media metadata
Facebook Ads Library Scraper	Competitor ad monitoring, creative analysis, and regional campaign checks
Google Maps Scraper	Local business lists, contact details, websites, social profiles, images, and lead data
Google Maps Reviews	Review monitoring, rating analysis, owner replies, and location-level feedback
Hunter Email	Finding and verifying professional emails, enriching people and companies, and building lead lists

You can use many of these directly from the website under Applications, or through API endpoints such as:

• /api/v1/firecrawl
• /api/v1/reddit
• /api/v1/x
• /api/v1/tiktok
• /api/v1/instagram/profile
• /api/v1/instagram/posts
• /api/v1/facebook/ads
• /api/v1/googlemaps
• /api/v1/googlemaps/reviews
• /api/v1/hunter

The important part is that the model does not need to guess. The tool fetches the raw material, then the model turns it into something useful.

Useful combinations

Here are a few practical patterns.

Competitor monitoring

Use the Website Crawler for competitor pages, Facebook Ads Library Scraper for current ads, Reddit or X Research for public reactions, and a fast model to classify each item.

Then run a stronger reasoning model once a day or once a week:

• What changed?
• Which offers are competitors pushing?
• Which objections are customers repeating?
• Which ads or posts look worth responding to?

This is a good place to use Context Memory, because the automation gets more useful when it remembers the previous baseline.

Local lead generation

Use Google Maps Scraper to find businesses in a location and category. Enrich the results with websites, social links, and contact details. Use Hunter Email when you need professional email lookup or verification.

Then use a cheap model to score fit:

• industry match
• company size hints
• whether the website looks active
• whether reviews mention a problem you solve

Only use a stronger model for the final account summary or personalized outreach draft.

Research briefings

Use web search for fresh sources and the Website Crawler for pages that need deeper extraction. Add :memory if this briefing repeats over time.

For API calls, a simple combined model suffix can look like this:

curl -X POST https://nano-gpt.com/api/v1/chat/completions \
  -H "Authorization: Bearer $NANOGPT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini:online:memory",
    "messages": [
      {
        "role": "user",
        "content": "Find the latest changes in this market and summarize what changed since our last briefing."
      }
    ]
  }'

For the final synthesis, switch to a stronger reasoning model and pass in the cleaned notes.

Social listening

Use Reddit, X, TikTok, Instagram, and Google Maps Reviews depending on where your audience talks.

Then split the model work:

• cheap model: classify sentiment, topic, product area, urgency
• stronger model: summarize themes, identify risks, propose responses
• memory: keep track of repeated issues and whether they are getting better or worse

This is especially useful for product feedback, support triage, brand monitoring, and launch tracking.

Content and media production

Use search or crawling for research, a writing model for the script, an image model for visual concepts, a video model for motion, and TTS for voice.

The same automation can move through multiple model types:

1. Research the topic.
2. Draft a short script.
3. Generate a thumbnail or image concept.
4. Generate a short video.
5. Generate a voiceover.
6. Ask a critic model what should be improved.

This is where using one account and one API matters. You do not need a separate vendor setup for every stage.

When to use MCP

MCP is useful when you want an agent client to call tools itself.

The NanoGPT MCP server lets compatible clients like Claude Code and Cursor connect to NanoGPT tools such as chat, web search, image generation, URL scraping, and YouTube transcripts. That makes it a good fit for coding agents, research agents, and local workflows where the agent should decide which tool to call next.

If you are building your own backend automation, the API endpoints are usually simpler. If you are using an agent product that already supports MCP, MCP is often the cleaner setup.

Keep costs under control

The easiest way to waste money on automations is to use a premium reasoning model for every row.

Better defaults:

• Use cheap models for extraction, tagging, routing, and formatting.
• Use stronger models only for synthesis, planning, and judgment.
• Keep tool calls bounded with result limits and max charge settings where available.
• Use web search only when freshness matters.
• Use memory for repeated workflows, not for one-off throwaway jobs.
• Log intermediate results so you can inspect failures without rerunning the whole pipeline.

Also consider auto-model for general chat or mixed tasks where you do not want to manually pick the model. For production automations, it is still worth explicitly choosing models once you understand the cost and quality tradeoff.

A simple rule

Use the smallest model that can do the current step reliably, and give it the right tool instead of asking it to guess.

That is the practical shift. The model is the reasoning layer. The tools are the eyes, hands, memory, and data sources. Once you combine them deliberately, automations become cheaper, more accurate, and much easier to scale.