A simple portfolio structure:

1. Who you are
Short intro + niche + content style

2. Your strongest 3–5 videos
Don't make clients dig through your entire digital archaeology collection.

3. Different content examples
Show range instead of five versions of the same talking-head video.

4. Services
UGC videos, product demos, testimonials, hooks, voiceovers, etc.

5. Results or experience
If you have them, include views, engagement, conversions, or client feedback.

6. Contact information
Make it ridiculously easy to reach you.

For pricing

Don't only price based on how long it takes you to film.

Consider:

Base creation fee + usage rights + revisions + additional deliverables

For example, a $100 video being used organically on a brand's social media is very different from that same video being used as a paid advertisement for 6 months.

For outreach

Instead of:

>

Try showing that you've actually looked at the brand.

Mention a specific product, identify a content opportunity, and briefly explain what you'd create.

The goal isn't to convince every brand to hire you.

It's to make the right brand think:

“This person already understands what we need.”

And one more thing: don't wait until your portfolio is perfect before reaching out. A strong portfolio helps, but consistent outreach is what creates opportunities. Humans somehow made employment into a numbers game, so we might as well use the rules against them.

A common mistake when building AI search is treating search, crawling, extraction, indexing, and retrieval as the same problem.

They aren't.

A useful way to think about the stack is:

1. Search = Find candidates
Search APIs return potentially relevant pages. Brave Search, for example, exposes results from its own web index, while Tavily focuses on AI-oriented search and relevance processing.

2. Crawl = Discover pages
If you're building a knowledge base from a specific site, you may need to crawl links rather than repeatedly searching for individual pages.

3. Extract = Turn pages into usable data
HTML is a terrible format for an LLM. Firecrawl can search and then return scraped content as Markdown, while Tavily's Extract API can retrieve content from selected URLs.

4. Index = Make retrieval cheap
Once documents are cleaned, chunked, and enriched with metadata, they can be indexed for keyword, vector, or hybrid retrieval.

5. Retrieve = Select the evidence
This is where a lot of RAG systems quietly fall apart.

The problem isn't always the LLM.

Sometimes the LLM was simply handed five mediocre chunks and asked to perform a miracle.

A practical pipeline looks like:

Query → Search → Candidate URLs → Extract → Clean → Chunk → Retrieve → Rerank → LLM

And there's an important optimization hiding in that diagram:

Don't extract everything you find.

Search first. Filter candidates. Then extract only the pages that survive your relevance threshold.

Tavily's own guidance recommends a two-step approach for this reason: expand the source pool, curate relevant documents, then extract the best sources. It gives you more control and avoids wasting extraction work on irrelevant pages.

A simple experiment worth running

Take 100 real user questions from your application.

Compare:

Pipeline A
Search → LLM

Pipeline B
Search → Extract → LLM

Pipeline C
Search → Extract → Hybrid Retrieval → Rerank → LLM

Measure:

• Retrieval Recall@5
• Precision@5
• Answer accuracy
• Citation accuracy
• Latency
• Cost per query
• Number of retrieved tokens

The interesting result usually isn't "which API is best?"

It's where your pipeline actually loses information.

A search API with excellent results can still produce a bad AI answer if extraction removes important context.

Likewise, an impressive vector database won't rescue a crawler that collected the wrong documents. Humanity has spent decades building increasingly sophisticated ways to search for things, only to discover that the real challenge is deciding what counts as useful information. Delightful.

For different jobs, I'd think about the tools differently:

• Brave Search: broad web search backed by an independent web index.
• Tavily: search and extraction designed around AI/RAG workflows.
• Firecrawl: especially interesting when search needs to connect directly to web extraction and crawling.
• Exa: worth testing when semantic/neural retrieval is central to the workload.

The best architecture isn't the one with the most APIs. It's the one where every stage has a measurable job. If you can't explain why a component exists, benchmark it against removing the component. Sometimes the fastest search infrastructure improvement is deleting half the search infrastructure.

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