Context, Not Calls: Mapping the Search & Data-API Landscape in AI-Native Apps

Introduction

We’ve spent the last few months talking to a few hundred builders, indie hackers, seed-stage startups, and internal-tool teams shipping AI-native products. Same story every time:

• Five external data classes always show up: Market and Financial (Public/Private/Filings), news, research, web search and custom db’s.
• Each API has its own dialect, auth flow, and rate limits, so glueing them together soaks up more time than shipping features.
• Sooner or later, everyone wishes for one clean endpoint that returns production-ready context.

This post maps that API landscape, surfaces the hidden engineering tax, and shows why we built Valyu’s Context API to make the plumbing boring.

If you’re wiring up an AI dashboard or agent in Retool, Bubble, Supabase, n8n, or straight code - read on. It should save you a few days of yak-shaving.

Let’s dive in.

Section 1: The 5 Data APIs Every Dev Eventually Reaches For

You can’t ship an AI feature without external context. After the first prototype, you’ll end up adding at least one of these five feeds whether you planned to or not:

Market & Financial – live quotes, earnings calls, private-company filings. Powers dashboards, risk models, and investor prompts.

News Wires – real-time headlines for sentiment, alerts, and “what-just-happened?” LLM grounding.

Research Corpora – peer-reviewed papers, white-papers, patents, anything deeper than a blog post, for citation-worthy answers.

Web-Search Indexes – fresh web pages and long-tail queries, so your bot isn’t stuck in last year’s dataset.

Custom DB's / Knowledge Bases – your own docs, user notes, scraped PDFs because public data never covers edge-case questions.

Each source fixes one blind spot and inserts another:

• Different auth flows and payload shapes
• Conflicting rate and quota rules
• Mixed latency and freshness guarantees

You get richer answers, but you also inherit five ways to fall over in prod. The next sections break down the upsides and trade-offs of each API type and why many teams end up rebuilding the same glue code.

1. Market & Financial Data APIs

Use in AI Applications:

• Injecting live prices and fundamentals into LLM prompts (“What moved AAPL today?”)
• Powering investor chatbots that answer follow-ups with source links
• Auto-summarising earnings calls and filings for daily-briefing agents
• Feeding features into ML models for risk or sentiment scoring
• Back-testing autonomous trading or rebalancing agents

Why you need them

An AI assistant can’t bluff market numbers users will sanity-check on Yahoo in seconds. Live quotes, historical bars, and filing metadata are baseline inputs for any finance-aware LLM workflow.

What’s good

Public-market ticks (real-time & historical) – Alpha Vantage, Twelve Data, Polygon stream prices, OHLC bars, splits, and dividends in clean JSON that drops straight into a prompt or chart.

Public-company filings & transcripts – EDGAR, Quartr, Intrinio expose 10-Ks, 10-Qs, and earnings-call audio in endpoint form, so you don’t have to scrape PDFs or decode XBRL by hand.

Private-company fundamentals – Crunchbase, PitchBook, Dealroom, Tracxn APIs surface funding rounds, revenue ranges, and cap-table snapshots. Handy when your agent gets “What’s Stripe’s latest valuation?” instead of “What’s AAPL trading at?”

Where it bites

Latency and coverage vary by ticker and region. Quotas spike once you outgrow the hobby tier. Filings arrive as semi-structured blobs that need heavy parsing. Merging time-series with document text into one embedding context is plumbing work that solo devs, indie hackers, and full teams end up repeating.

2. News & Event APIs

Use in AI Applications:

• Streaming headlines and social chatter into real-time sentiment or event-detection agents
• Grounding LLM answers with “what just happened?” context including memes, not just press releases
• Auto-summarising daily digests of market-moving news and viral threads
• Triggering alerts when a portfolio company, ticker, or keyword trends across outlets and social feeds

Why you need them

LLMs don’t ship with a live newswire. If your tool tracks markets, policy, or culture shifts, you’ll miss half the signal unless you watch both traditional outlets and the faster-moving Reddit/X fire hose.

What’s good

Breadth: APIs like NewsAPI, GDELT, and ContextualWeb cover thousands of publishers.

Social sentiment: Reddit (via Pushshift, Hacker News, etc.) and X (via the official v2 stream or third-party resellers) add crowd temperature and early rumours.

Lightweight filters: Most feeds offer keyword, date, language, and source params, enough to prototype event triggers or RAG injects in an afternoon.

JSON payloads: Easy to drop into a summariser or embedding pipeline.

Where it bites

• Noise: Duplicate headlines, paywalls, click-bait, bot tweets - you’ll write a scoring and deduplication layer before anything is production-ready.
• Sparse metadata: Many items lack tickers, geotags, or entity labels, so entity-linking is DIY.
• Rate & cost creep: Free tiers disappear once you poll minute-by-minute or subscribe to the X fire hose.

In short, the feeds are indispensable for context, but the plumbing, especially normalising newsroom XML and social JSON into one timeline, lands squarely on solo devs, indie hackers, and small teams.

3. Research APIs

Use in AI Applications:

• Pulling peer-reviewed papers into RAG pipelines so answers cite real science
• Auto-summarising new arXiv pre-prints for “what’s state-of-the-art?” briefings
• Linking claims in LLM output to DOI, PubMed, or patent IDs for credibility
• Building your own DeepResearch tool

Why you need them

Users spot hand-wavy answers fast. If your bot claims “Transformer-based compression outperforms BERT on PubMed,” it should link the paper—ideally the PDF, not a blog recap.

What’s good

Open pipes: Semantic Scholar, OpenAlex, arXiv, CrossRef - most let you hit a REST endpoint without begging for an enterprise key.

Rich metadata: authors, journal, DOI, citation graph, publish date all great hooks for reranking and provenance.

Structured abstracts: Short enough for embeddings, already chunked.

Where it bites

• Abstract-only ceiling: Semantic Scholar and friends rarely expose full text or figures; you end up with surface-level context.
• Patchy coverage: Some APIs lag on paywalled journals or conference proceedings; others miss arXiv updates for days.
• Schema drift: Fields, naming, and nesting vary, so normalising multiple sources is more plumbing work for solo devs and indie hackers.
• Commercial tie-in: Bridging academic IDs to ticker symbols, financial metrics, or market events is still hand-rolled CSVs and regex.
• Legal Barrier: Semantic has stopped offering it's API for commercial use.

Bottom line: research feeds give your LLM authority, but stitching them into a broader commercial or financial knowledge graph is still a DIY exercise.

4. AI-Native Search APIs

Use in AI Applications:

• Pulling fresh pages into RAG so an answer reflects today, not the last model checkpoint
• Letting agents chase long-tail questions (“compare the latest LangChain release to v0.1”)
• Filling coverage gaps when structured feeds don’t mention a niche library or brand-new repo

Why you need them

Your LLM froze the day its weights were cut. If the question is about something published this morning, a search endpoint is the only sane way to inject up-to-date context.

What’s good

LLM-aware design: Tavily, Exa, LinkUp, and Brave’s API return snippet-sized JSON with URLs, titles, and sometimes pre-baked summaries - easy to slot straight into a prompt.

Relevance knobs: Most expose simple params (domain allow-list, recency, semantic vs. keyword rank) so you can trade speed for quality.

No headless browsers: You avoid the captcha/SERP scraping dance and stay inside TOS.

Where it bites

• SEO noise: Marketing pages, listicles, and AI-generated blogs still dominate results; you’ll build a post-filter or reranker.
• Thin verification: Snippets can be wrong or out of date, so you need fallback cross-checks or a second source.
• Rate & cost cliffs: Per-query pricing stacks up fast when your agent chains multiple searches.
• Legal & content gaps: Some domains block crawler APIs entirely, leaving blind spots you must detect and patch manually.
• Quality & freshness gap: Some API's lag on indexing speed or fail to return relevant results consistently.

Useful, but not turnkey solo devs and indie hackers still end up writing ranking, dedup, and fact-checking glue before the data is safe for production prompts.

5. Custom Databases & Knowledge Bases

Use in AI Applications:

• Feeding LLMs with product docs, support tickets, or client notes the public web will never have
• Mixing proprietary tables with scraped PDFs so an agent can answer “How does our pricing work?”
• Building internal copilots that search both Slack threads and PostgreSQL rows in one shot

Why you need them

Public data hits a wall fast. The moment users want answers about your roadmap, churn reasons, or niche engineering choices, you have to index your own corpus.

What’s good

• Drop-in vector stores: Pinecone, Weaviate, Supabase, pgvector, or an S3 + FAISS combo spin up in minutes.
• Tooling glue: LangChain, LlamaIndex, and open-source ingestion scripts handle embeddings, chunking, and basic retrieval out of the box.
• Full control: You decide what goes in the index, how often it refreshes, and who can query it - crucial for GDPR or SOC2.

Where it bites

• Ingestion, not retrieval, is the grind: Cleaning PDFs, normalising CSVs, and chunking Markdown is where solo devs and indie hackers burn hours. Garbage in = garbage embeddings.
• Schema drift: Mixing structured rows with free-form docs means deciding on metadata, versioning, and dedup logic up front.
• Security overhead: Internal KBs demand ACLs, audit trails, and tokenised PII, none of which vector DBs solve out of the box.

Building a private knowledge layer unlocks bespoke answers, but the ETL plumbing is still yours to own or to automate away with a unifying Context API.

Section 2: Why This Stack Becomes a Nightmare

When you try to bolt these APIs together, you introduce three core problems:

1. Fragmentation

Each API has its own auth flow, schema, format, rate limits, and limitations. Good luck building one pipeline that works across all of them and stays working.

2. Hidden Costs

Most APIs hide the good stuff behind usage tiers, premium endpoints, or per-call pricing. It scales poorly. You’ll also end up paying for irrelevant or duplicate data if you don’t spend time building filters.

3. Complexity Kills Speed

Each integration requires time: to normalise, to query properly, to manage failures. That’s time not spent building features or shipping products.

The bottom line: You become a data plumber instead of a builder.

Section 3: One Context API Instead of Five

Valyu ships a single endpoint that abstracts the feeds above market ticks, news wires, research papers, filings, even niche domains like law or medicine already indexed and ready for prompts.

What you query through one pipe

• Live and historical market data + earnings-call transcripts
• Real-time and archival news articles
• Peer-reviewed research, white-papers, patents
• Business filings and private-company signals
• Additional licensed verticals (legal, medical, etc.) as add-ons

Ship Features - Not ETL

Most builders discover too late that the hardest part of an AI product isn’t the model - it’s the data plumbing behind it. Five feeds become five auth flows, five quota dashboards, and five ways to break in prod. We’ve felt that drag ourselves, which is why Valyu’s Context API collapses market ticks, filings, news, research, social sentiment, and your own docs into a single, versioned source of truth.

Eliminate the time-sink of schema transforms, rate-limit retries, and dedup scripts, and you get back the one resource you can’t buy: focused engineering hours. Whether you’re a solo indie hacker, a two-person SaaS, or an internal tools squad, the pattern holds - less plumbing, more product.

Ready to trade data chores for shipped features?

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