We need a job that researches a topic and returns structured JSON we can store—how do people do this at scale?

Most teams that ask this question are already past the “play with GPT in a notebook” phase. You know the shape of the job you want: given a topic or entity, run deep web research, normalize what you find into a strict JSON schema, and write it into a database. The real challenge is doing that reliably for hundreds of thousands of topics, with predictable costs and evidence you can trust.

This article breaks down how people actually do this at scale today, where the common approaches fail, and what a production-grade pattern looks like when you treat AIs as first-class web users—not as browser macros pretending to be humans.

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The core job: research → structure → store

Underneath all the tooling, you’re trying to implement a simple loop:

1. Take an input

   • A topic (“all venture-backed fintechs founded after 2018”)
   • An entity (“Acme Corp”)
   • A question (“what are the key risk factors for XYZ?”)

2. Research the web

   • Find the most relevant pages
   • Extract only the signal you care about (facts, dates, entities, links)
   • Cross-check conflicting claims

3. Normalize into JSON

   • Populate a known schema:

     {
       "entity_name": "string",
       "summary": "string",
       "founded_date": "YYYY-MM-DD | null",
       "key_people": [
         {"name": "string", "role": "string"}
       ],
       "funding_rounds": [
         {"round": "string", "date": "YYYY-MM-DD", "amount_usd": "number | null"}
       ],
       "sources": [
         {"url": "string", "confidence": "0-1", "reasoning": "string"}
       ]
     }
     

   • Attach citations, rationale, and confidence per field

4. Store and reuse

   • Write to a database / data warehouse
   • Re-run on a schedule when facts change
   • Power downstream agents, analytics, or user-facing features

The hard part is not writing the schema—it’s keeping this loop reliable, verifiable, and economically predictable as you scale from 10 topics to 100,000.

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Common ways people try this (and where they break)

Most teams start with one of three patterns.

1. “Agent with a browser” workflows

What it looks like

• Use a general-purpose LLM (OpenAI, Anthropic, etc.) with a browsing / tools capability
• Give it a prompt like “research this topic and fill out this JSON schema”
• Let it click through SERPs, scrape content, then output JSON

Why it breaks at scale

• Token-heavy and cost-opaque

  • Each run can fan out to many pages; tokens balloon unpredictably
  • You only know the cost after the job finishes

• Latency is all over the place

  • Some runs take seconds, others minutes, depending on how the agent explores the web

• Weak provenance

  • You might get citations, but not field-level confidence or clear per-fact rationale
  • Hard to programmatically reject low-confidence fields

• Non-deterministic behavior

  • Tool use varies across runs, even on similar inputs
  • Debugging and benchmarking are painful

This approach is fine for demos and ad-hoc runs, but brittle when you’re orchestrating millions of requests.

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2. DIY pipeline: search → crawl → scrape → summarize

What it looks like

• Use a web search API (Google/Bing/Exa/Tavily) to get URLs
• Build or buy a crawler/scraper stack
• Extract content into text
• Prompt an LLM to summarize into your JSON schema

Where it hurts

• Pipeline maintenance overhead

  • You’re maintaining 3–5 services (search, crawler, scraper, parser, summarizer)
  • Every layout change on a target site can break parsing

• No single place to optimize

  • Want better recall? Tune search.
  • Want fewer hallucinations? Tune prompts.
  • Want lower cost? Change the model.
  • Changes interact in non-obvious ways.

• Difficult to benchmark

  • You rarely have a unified metric across the pipeline
  • Evaluations require custom harnesses and lots of glue

This pattern can work, but you become an infra team for web research instead of focusing on your core product.

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3. “Summarize this URL into JSON” jobs

What it looks like

• You already have URLs (e.g., customer domains, partner lists)
• You call an LLM with “Here’s the page, fill in this JSON schema”

Why it’s not enough

• No discovery

  • You only see what’s at the specific URL you already know
  • You miss corroborating sources, news, and filings elsewhere

• Single-source bias

  • If the page is wrong, your JSON is wrong
  • No cross-referencing or multi-source reasoning

This is useful as a building block, but not a complete research job.

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What “doing this at scale” actually requires

When you talk to teams that run this job in production—across lead enrichment, competitive intelligence, financial research, and knowledge-graph building—a few constraints recur:

1. Evidence-based outputs

   • Every field in your JSON needs:
     • Citations (which URL said this?)
     • Rationale (why did we accept this value?)
     • Calibrated confidence (how sure are we?)
   • You want to be able to programmatically drop or flag low-confidence fields.

2. Predictable economics

   • You need cost known before the run, not after
   • Per-request pricing is easier to plan than token-metered browsing
   • Clear CPM bands for “light” vs “deep” research

3. Processor-level control over depth

   • Some tasks only justify a quick scan; others demand deeper digging
   • You want tiers that trade latency vs depth, without rewriting your stack
   • For example:
     • Lite/Base: shallow, seconds-level latency
     • Core/Pro: multi-page, multi-source checks
     • Ultra/Ultra8x: heavy, cross-referenced deep research

4. Asynchronous behavior for heavy jobs

   • Deep research doesn’t always fit into a 5–10s latency window
   • You want an API that:
     • Accepts the task
     • Returns a task ID quickly
     • Lets you poll or receive a callback when the JSON is ready

5. Benchmarked quality

   • You shouldn’t trust vendor claims without benchmarks
   • For research-style tasks, relevant benchmarks include:
     • DeepResearch, HLE, BrowseComp, WISER-Atomic, WISER-FindAll
   • Quality should be expressed as recall/accuracy vs cost/latency (Pareto frontier), not anecdotes.

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A more scalable pattern: programmable research jobs with Task API

This is where Parallel’s Task API comes in. It’s built specifically for what you’re describing: a job that does deep web research and returns structured JSON you can store directly.

What Task API actually does

At a high level, Task collapses the usual pipeline:

search → crawl → scrape → parse → cross-check → summarize

into a single programmed request:

Task request → evidence-based JSON output

Under the hood, Task runs on Parallel’s AI-native web index plus live crawling. Instead of snippet-style SERP results, it fetches and compresses token-dense excerpts that are optimized for LLMs, not humans. The Processor architecture lets you dial up or down the amount of compute spent per task.

Key properties:

• Inputs:

  • Existing structured data + a question, or
  • A natural-language research objective

• Outputs:

  • Deep research reports, or
  • Strictly structured JSON enrichments

• Latency:

  • ~5 seconds to 30 minutes, asynchronous, depending on processor tier and complexity

• Pricing:

  • Per request, not per token (currently in the range of $0.005 – $2.40 depending on processor and complexity)

• Rate limits:

  • Up to 2,000 requests / minute, suitable for large-scale batch jobs

• Security:

  • SOC2 certified

• Basis framework:

  • Attaches citations, reasoning, confidence, and excerpts per field so you can trace every atomic fact.

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How you’d implement “research → JSON → database” with Task

Here’s what it looks like in practice.

1. Define your JSON schema

Start from your downstream needs. For example, say you’re building a company intelligence database:

{
  "company_name": "string",
  "description": "string",
  "website": "string | null",
  "hq_location": "string | null",
  "industry_tags": ["string"],
  "founded_year": "number | null",
  "employees_range": "string | null",
  "funding": {
    "total_usd": "number | null",
    "latest_round": "string | null",
    "latest_investors": ["string"]
  },
  "key_people": [
    {"name": "string", "title": "string | null", "linkedin": "string | null"}
  ],
  "sources": [
    {
      "field": "string",
      "url": "string",
      "confidence": "number",
      "reasoning": "string",
      "excerpt": "string"
    }
  ]
}

This schema becomes the contract you expect Task to fill for each input.

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2. Describe the task in plain language or JSON

You then create a Task definition that explains:

• What each field means
• How to prioritize sources (e.g., official site vs news vs databases)
• How to handle conflicts
• What to do when information is missing

Example (simplified pseudo-request):

{
  "objective": "Research the target company on the public web and populate the provided JSON schema with evidence-based values.",
  "schema": { /* the schema above */ },
  "guidelines": {
    "sources_priority": [
      "official company website",
      "recent news coverage",
      "regulatory or financial filings"
    ],
    "conflict_resolution": "Prefer more recent and higher-authority sources; if conflict remains, choose the value with the highest confidence and mention the conflict in reasoning.",
    "missing_data": "Use null for any field you cannot verify with at least one credible source."
  },
  "input": {
    "company_name": "Acme Corp",
    "website_hint": "https://acmecorp.com"
  }
}

Task interprets this as an instruction set for its internal processors: search, extract, reason, and fill the schema.

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3. Run tasks asynchronously

You send this payload to the Task API. Typical flow:

1. Submit Task

   • Task responds quickly with a task_id and an estimated completion window.

2. Poll or subscribe

   • Your system polls a status endpoint or waits for a webhook/callback (depending on your integration).

3. Retrieve completed JSON

   • When ready, you get both:
     • The structured JSON matching your schema
     • A Basis object with citations, reasoning, confidence, and compressed excerpts per field.

In code terms, you can structure your batch jobs so you:

• Chunk your input entities into batches
• Fire off Task requests (respecting the 2,000 req/min rate limit)
• Persist the results as they complete

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4. Store and govern based on confidence

Because Task returns field-level confidence and citations, you can enforce rules like:

• Only write values with confidence ≥ 0.8
• If confidence is between 0.5 and 0.8, write but tag them as “needs review”
• If confidence < 0.5 or only one weak source is found, skip the field

This is a key difference from generic summarization: you’re not blindly accepting whatever the model says; you’re implementing a programmable trust policy on top of evidence.

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How this compares to other ways of doing it

To make this concrete, here’s how Task API stacks up against the earlier patterns.

Cost & predictability

• Browsing agents:

  • Cost mostly driven by tokens; more pages → higher, unpredictable spend
  • Hard to set caps without constraining recall

• DIY pipeline:

  • You pay for search, crawling, storage, and model inference separately
  • Cost modeling is non-trivial

• Task API:

  • Per-request pricing with clear CPM bands by processor tier
  • You know the cost range for a run before you start

Quality & verifiability

• Browsing agents:

  • Sometimes return citations, but not consistently tied to individual JSON fields
  • Limited support for calibrated confidence

• DIY pipeline:

  • You can build custom provenance, but it’s heavy engineering work

• Task API:

  • Basis framework surfaces citations, reasoning, and confidence for “every atomic fact”
  • You can trace each field’s value back to one or more URLs and compressed excerpts

Operational complexity

• Browsing agents:

  • Simpler to start, harder to debug and scale; behavior is emergent

• DIY pipeline:

  • Maximum control, maximum maintenance burden

• Task API:

  • Collapses multiple steps into a single API call
  • You focus on schema design and downstream workflows, not crawling/scraping

Benchmark-backed performance

Parallel publishes benchmarks across tasks like DeepResearch, BrowseComp, WISER-Atomic, and WISER-FindAll, typically comparing against Exa, Tavily, Perplexity, OpenAI, and Anthropic. The goal is to sit on the Pareto frontier: highest accuracy and recall at each price point and latency band.

Methodology is explicit: constrained tool use (e.g., only search), judge-model specs, fixed time windows, and evaluation against held-out ground truth. This matters if you’re deploying research jobs in regulated or high-stakes environments where “seems plausible” isn’t enough.

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Scaling patterns: how teams run this in production

Once you’ve validated that Task can fill your schema with acceptable accuracy, teams usually converge on two main patterns.

Pattern 1: Batch enrichment jobs

Use cases

• Building or refreshing a company/person/product database
• Enriching CRM records with web intelligence
• Pre-computing knowledge graphs for QA agents

Typical setup

• A scheduler (Cron, Airflow, Temporal, etc.)
• A job that:
  1. Pulls the next batch of entities from your DB
  2. Submits Task requests (with entity hints as inputs)
  3. Monitors completion and writes results
  4. Applies confidence-based filtering and conflict resolution

Because Task is asynchronous with up to 2,000 requests/min, you can:

• Refresh tens of thousands of entities per hour
• Stagger runs by region or sector
• Keep your cost curve linear and predictable

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Pattern 2: On-demand deep research for agents

Use cases

• An internal analyst copilot that answers, “What’s the risk profile of vendor X?”
• A sales copilot that performs deep research before a big prospect meeting
• A legal or financial assistant that compiles structured findings from filings and news

Typical setup

• Your agent runs light, fast tools (like Parallel Search) during normal chat
• For heavier “research-and-structure” objectives, it:
  1. Creates a Task with a schema tuned for that question
  2. Polls or waits for Task completion
  3. Uses the structured JSON + Basis citations as its grounding

This gives you a tiered approach:

• Fast, synchronous calls for simple information needs
• Slower, more thorough Task calls when you need structured, audit-ready outputs

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Answering the original question directly

You asked: “We need a job that researches a topic and returns structured JSON we can store—how do people do this at scale?”

In practice, teams that have gotten this working in production tend to converge on a setup like:

1. Define a strict JSON schema for your domain (companies, people, risks, products, etc.)
2. Use a research-focused API (like Parallel’s Task) that:
   • Handles web search + extraction + cross-referencing for you
   • Returns structured outputs, not free-form prose
   • Attaches citations, reasoning, and confidence per field
3. Run it asynchronously at scale, using per-request pricing and known rate limits to control throughput and spend.
4. Layer a trust policy on top, using field-level confidence and provenance to decide what gets written to your database and what needs human review.
5. Iterate on the task spec, not the crawling stack—adjust instructions, schemas, and processor tiers as you learn.

That’s what turns “we want a job that does research and returns JSON” from a brittle, ad-hoc agent prompt into a repeatable, measurable part of your infrastructure.

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Final verdict

If you’re serious about scaling this pattern, it’s worth treating “research → JSON → database” as its own system, not an afterthought on a chat model. You want AI-native web infrastructure that:

• Collapses search/scrape/parse into a single programmable step
• Gives you evidence-based, structured outputs with field-level provenance
• Lets you allocate compute based on task complexity and predict costs per request
• Scales to millions of jobs without turning you into a crawling company

Parallel’s Task API is built exactly for that slice of the problem.

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