Competitor Price Scraping: A Schema-First Pipeline

The Hard Part Is Not Fetching the Page

If you have run competitor price monitoring for more than a quarter, you know the failure rarely looks like an outage. It looks like a dashboard that keeps updating with numbers that happen to be wrong. A competitor redesigns a product template, your selector starts grabbing the struck-through list price instead of the live one, and nothing throws an error. The pipeline reports a hundred percent success while it quietly feeds bad data into your repricing logic.

Competitor price scraping is the practice of collecting product prices from competitor websites on a recurring schedule and turning them into normalized records you can compare against your own catalog. The hard part is rarely fetching a single page once. It is turning thousands of differently built, frequently changing competitor pages into clean, typed records, on a schedule, without silently corrupting the data downstream.

This guide lays out a schema-first pipeline for that job. You define the price record you want as a typed schema, keep fetching separate from extraction, and use a model that returns strict JSON matching your schema regardless of how the page is laid out. We cover the required schema fields, deduplication and validation, batch extraction with webhooks for recurring runs, an honest cost model at 10k, 100k, and 1M pages, and the accuracy checks that catch silent failures before they reach a pricing decision.

Read time: 14 minutes

Why Competitor Price Pages Break Rule-Based Selectors

A CSS or XPath selector encodes an assumption about where a value lives in a specific page's DOM. That assumption is fragile because you do not control the pages. Competitors change layouts on their schedule, not yours, and competitive price scraping at any real breadth means tracking dozens of sites, each with its own templates.

The breakage modes are predictable once you have seen them a few times:

• JavaScript-rendered prices. Many retailers inject the price client-side after the initial HTML loads, so a selector run against the raw response finds nothing or finds a placeholder. Extracting the real price often requires a headless browser, and rotating IPs or user agents alone is frequently not enough on large sites.
• List price versus sale price. A product card shows a struck-through original price and a live price. A selector keyed to the first .price node grabs whichever the new layout puts first, and that is not always the one you want.
• A/B-tested and localized layouts. The same URL serves different markup to different sessions, and localized pages change currency symbols, decimal separators, and stock wording.
• Variant and bundle markup. A price sits inside a variant picker or a bundle widget, so the "price on the page" is ambiguous without knowing which variant is selected.
• Anti-bot interstitials. A challenge page returns a completely different DOM, and a naive selector either fails or, worse, matches something on the interstitial.

Now multiply that by every competitor you track and every page template each one runs. The selector-maintenance bill never stops growing, and each break is one more chance to ship a silent wrong value instead of a clean error.

A schema takes the opposite approach. Instead of describing where a value sits in the DOM, you describe what you want: a record with an offer price, a currency, and a stock status. A model that extracts against that schema reads the page's meaning rather than its structure, so a layout change does not break the contract. Here is the kind of messy markup a competitor page actually serves:

<div class="product" data-sku="EXT-4421">
  <h1 class="pdp-title">Aurora 14" Ultrabook (16GB / 512GB)</h1>

  <div class="price-block">
    <span class="was" aria-label="original price"><s>$1,299.00</s></span>
    <span class="now" data-testid="price">$1,099.00</span>
    <span class="ccy">USD</span>
  </div>

  <p class="stock in-stock">In stock - ships in 1-2 days</p>

  <div class="variant-picker">
    <button class="variant selected" data-variant="16-512">16GB / 512GB</button>
    <button class="variant" data-variant="32-1tb">32GB / 1TB (+$240.00)</button>
  </div>

  <script type="application/ld+json">
    {"@type":"Product","offers":{"price":"1099.00","priceCurrency":"USD"}}
  </script>
  <style>.price-block .was{color:#999}</style>
</div>

There is a list price, a current price, a currency, stock text, variant markup, and a script tag, all jumbled together. A selector pipeline needs a rule for each of those quirks. A schema-first pipeline needs one schema.

The Pipeline: Fetch, Extract, Validate, Store

Before any code, fix the shape of the system. A durable competitor price scraping pipeline has five stages, and the discipline that makes it durable is keeping fetching and extraction in separate stages:

1. Fetch or render each competitor page with your existing crawler and proxy stack.
2. Pre-clean the HTML with lxml to strip scripts, styles, and boilerplate.
3. Extract by sending the cleaned HTML and your response schema to a schema-guided model, which returns strict JSON.
4. Validate and deduplicate the record, then route anything low-confidence to a review queue.
5. Store the validated record in a price-history table that downstream pricing and alerting read from.

The reason to draw the boundary between stage one and stage three so firmly is that they are different problems with different tools. Fetching is an availability and anti-bot problem solved by proxies and headless browsers. Extraction is a correctness problem solved by a schema. Schematron is the extraction layer here; it does not crawl, render JavaScript, solve CAPTCHAs, or rotate proxies. If you are choosing between a crawler and the extraction model, you are asking the wrong question, because you need both in different stages.

Figure 1: Schema-First Competitor Price-Monitoring Pipeline

The schema is the stable contract that runs through the middle of this diagram. Everything upstream can change vendors, and everything downstream can change consumers, as long as the record shape stays fixed.

The Required Price-Monitoring Schema

The schema is the most important design decision in the whole pipeline, because it is both the extraction contract and the validation contract. For price monitoring, a record needs seven fields at minimum:

• sku — your internal product identifier, so you can match a competitor's listing back to your own catalog.
• competitor — the competitor's domain or name, so a record is attributable.
• offer_price — the current purchasable price as a number, not a string.
• currency — an ISO 4217 code, so $10 and €10 never get compared as if they were equal.
• availability — a constrained enum (in stock, out of stock, preorder, unknown) rather than free text.
• captured_at — the timestamp of the fetch, which is what turns a pile of records into a price history.
• evidence — the exact text the price was read from. This is the field most pipelines skip and the one that makes silent failures auditable.

The evidence field is the one most teams skip, and it is worth a paragraph on its own. When a reviewer sees a price that moved 40 percent overnight, the fastest way to tell a real promotion from an extraction miss is to read the source text the model pulled the number from. Storing that text costs almost nothing, and it pays for itself the first time it stops you repricing against a number that was never really on the page.

Because the extraction model has no prompt or instruction channel, every bit of steering you want lives in the schema itself: field types, required flags, and field descriptions. A description like "the current purchasable price, not the struck-through list price" is how you resolve the list-versus-sale ambiguity, and there is nowhere else to put it. Keep temperature at zero for deterministic extraction.

Defining the Schema in Python (Pydantic)

In Python, a Pydantic model gives you the schema and the validator in one definition:

from enum import Enum

from pydantic import BaseModel, Field


class Availability(str, Enum):
    in_stock = "in_stock"
    out_of_stock = "out_of_stock"
    preorder = "preorder"
    unknown = "unknown"


class PriceRecord(BaseModel):
    sku: str = Field(
        ...,
        description="Your internal product identifier used to match this listing to your catalog.",
    )
    competitor: str = Field(
        ...,
        description="The competitor's domain or brand name.",
    )
    offer_price: float = Field(
        ...,
        description=(
            "The current purchasable price as a number, not the struck-through "
            "list price. If a variant is selected, use the selected variant's price."
        ),
    )
    currency: str = Field(
        ...,
        description="ISO 4217 currency code, e.g. USD, EUR, GBP.",
    )
    availability: Availability = Field(
        ...,
        description="Stock status read from the page.",
    )
    captured_at: str = Field(
        ...,
        description="ISO 8601 timestamp of when the page was fetched.",
    )
    evidence: str = Field(
        ...,
        description=(
            "The exact source text the offer price was read from, copied verbatim "
            "from the page so a reviewer can audit the extraction."
        ),
    )
    list_price: float | None = Field(
        default=None,
        description="The struck-through original price, if shown.",
    )
    seller: str | None = Field(
        default=None,
        description="The seller or merchant name, if distinct from the competitor.",
    )

The enum on availability and the description on offer_price are not decoration. They are the only mechanism you have to tell the model what "price" means on an ambiguous page.

The Same Schema in TypeScript (Zod)

If your pipeline runs on TypeScript, Zod expresses the identical contract, and .describe() carries the same field-level guidance:

import { z } from "zod";

export const PriceRecord = z.object({
  sku: z
    .string()
    .describe(
      "Your internal product identifier used to match this listing to your catalog.",
    ),
  competitor: z.string().describe("The competitor's domain or brand name."),
  offerPrice: z
    .number()
    .describe(
      "The current purchasable price as a number, not the struck-through list price. If a variant is selected, use the selected variant's price.",
    ),
  currency: z.string().describe("ISO 4217 currency code, e.g. USD, EUR, GBP."),
  availability: z
    .enum(["in_stock", "out_of_stock", "preorder", "unknown"])
    .describe("Stock status read from the page."),
  capturedAt: z
    .string()
    .describe("ISO 8601 timestamp of when the page was fetched."),
  evidence: z
    .string()
    .describe(
      "The exact source text the offer price was read from, copied verbatim from the page so a reviewer can audit the extraction.",
    ),
  listPrice: z
    .number()
    .nullable()
    .describe("The struck-through original price, if shown."),
  seller: z
    .string()
    .nullable()
    .describe("The seller or merchant name, if distinct from the competitor."),
});

Whichever language you use, the schema is portable: the same record shape drives extraction, validation, and storage.

Fetching Pages Separately From Extracting Fields

It is tempting to let one tool fetch and extract in a single call. Resist it. The reasons show up the first time something breaks.

When fetch and extract are coupled, a proxy outage and a schema bug look identical from the outside: both produce empty or wrong records. Pull them apart and a fetch failure stays a fetch failure, an extraction failure stays an extraction failure, and you can go fix the thing that actually broke.

Separation also unlocks three concrete wins:

• Cache the raw HTML. If you store the fetched HTML, you can re-extract with an improved schema without paying to re-fetch. Re-fetching is expensive and rate-limited; re-extraction is cheap.
• Swap the fetch vendor freely. Your proxy or headless provider can change without touching a line of extraction code, because the extraction stage only ever sees HTML.
• Monitor and bill the two independently. Fetch cost is dominated by proxy and rendering infrastructure; extraction cost is per-token. Keeping them separate keeps your cost model legible.

The one preprocessing step worth doing before extraction is cleaning the HTML. Schematron was trained on HTML that had been pre-cleaned with lxml to strip scripts, styles, and inline JavaScript, so aligning your preprocessing with that improves extraction quality and consistency.

import lxml.html as LH
from lxml.html.clean import Cleaner

HTML_CLEANER = Cleaner(
    scripts=True,
    javascript=True,
    style=True,
    inline_style=True,
    safe_attrs_only=False,
)


def strip_noise(html: str) -> str:
    """Remove scripts, styles, and JavaScript from HTML using lxml."""
    if not html or not html.strip():
        return ""
    try:
        doc = LH.fromstring(html)
        cleaned = HTML_CLEANER.clean_html(doc)
        return LH.tostring(cleaned, encoding="unicode")
    except Exception:
        return ""

Other tools such as Readability, Trafilatura, or BeautifulSoup are acceptable; lxml is simply the one that matches training, and you should err on removing less content rather than more. If you want a fuller walkthrough of the fetch-and-clean stage in Python, including how to clean the HTML first before extraction, that pattern is covered in depth separately. The payoff of cleaning before extraction is that the model receives signal instead of noise, and because the model enforces strict schema adherence, the JSON you get back is always shaped like your schema.

Extracting Price Records With Schematron

With a clean page and a schema, the extraction itself is a single call against an OpenAI-compatible endpoint. You pass the HTML as the user message and the schema as the response format; there is no prompt to write.

import os
from datetime import datetime, timezone

from openai import OpenAI

from lxml_clean import strip_noise
from price_record_schema import PriceRecord

client = OpenAI(
    base_url="https://api.inference.net/v1",
    api_key=os.environ.get("INFERENCE_API_KEY"),
)


def extract_price(raw_html: str, sku: str, competitor: str) -> PriceRecord:
    html = strip_noise(raw_html)  # pre-clean before sending to the model

    resp = client.beta.chat.completions.parse(
        model="inference-net/schematron-v2-turbo",
        messages=[{"role": "user", "content": html}],
        response_format=PriceRecord,
        temperature=0,  # deterministic extraction
    )

    record = resp.choices[0].message.parsed
    # The model has no prompt channel for runtime values like the fetch time,
    # so set captured_at and your own identifiers after extraction.
    record.captured_at = datetime.now(timezone.utc).isoformat()
    record.sku = sku
    record.competitor = competitor
    return record


if __name__ == "__main__":
    with open("messy-product-html.html", encoding="utf-8") as fh:
        page = fh.read()
    rec = extract_price(page, sku="EXT-4421", competitor="example-competitor.com")
    print(rec.model_dump_json(indent=2))

A few details matter here. The base URL is https://api.inference.net/v1, the model identifier is inference-net/schematron-v2-turbo, and temperature is zero. The schema goes in response_format, and because the model runs in strict JSON mode with full schema adherence, the response is always valid JSON that matches your record shape. That property changes what a validation failure means: it can no longer be malformed JSON, so any failure is a content problem like a missing price or an unexpected currency, which is exactly the class of problem you want your validation to focus on.

A Small Example: HTML to a Typed Record

Run the messy markup from earlier through the cleaner and then the extraction call, and you get back a record like this:

{
  "sku": "EXT-4421",
  "competitor": "example-competitor.com",
  "offer_price": 1099.0,
  "currency": "USD",
  "availability": "in_stock",
  "captured_at": "2026-06-13T14:02:11+00:00",
  "evidence": "$1,099.00 USD",
  "list_price": 1299.0,
  "seller": null
}

Notice the evidence field carries the exact source string the price came from, the offer_price is the live price rather than the struck-through one (because the schema description said so), and currency is a normalized code. This is a record your pricing logic can consume directly. If you want to try this path yourself before wiring up the full pipeline, you can run a single page through the model from the quickstart.

Deduplication and Validation

Extraction gives you records. Turning them into a trustworthy price history takes two more steps, and this is where most pipelines either earn or lose the trust of the team that depends on them.

Deduplicate so the history reflects changes, not fetches. A recurring sweep re-fetches the same products constantly, and the vast majority of fetches find no change. If you write a row on every fetch, your price-history table becomes mostly noise. Dedup on the tuple of (sku, competitor, offer_price, currency, availability) and persist a new row only when one of those changes, keyed by captured_at. The result is a compact table where every row is an actual price event.

Validate beyond schema validity. Strict mode already guarantees the JSON matches your schema, so validation here is about business rules, not syntax. The checks worth running on every record are:

• offer_price is greater than zero.
• currency is in your allowlist of expected currencies.
• availability is one of the enum values.
• offer_price falls within a sane band relative to the last known price for that product, so a 10x jump gets flagged rather than stored.
• The evidence text actually contains the extracted price string.

from price_record_schema import Availability, PriceRecord

ALLOWED_CURRENCIES = {"USD", "EUR", "GBP", "CAD", "AUD"}
MAX_PRICE_JUMP = 10.0  # flag a record if price moves more than 10x vs last known


def normalize_price_text(text: str) -> str:
    return text.replace(",", "").replace("$", "").replace("€", "").replace("£", "")


def is_valid(record: PriceRecord, last_price: float | None) -> tuple[bool, str]:
    """Business-rule validation. Schema validity is already guaranteed by strict mode."""
    if record.offer_price <= 0:
        return False, "non_positive_price"
    if record.currency not in ALLOWED_CURRENCIES:
        return False, "unexpected_currency"
    if record.availability == Availability.unknown:
        return False, "unknown_availability"
    if last_price is not None and last_price > 0:
        ratio = record.offer_price / last_price
        if ratio > MAX_PRICE_JUMP or ratio < (1 / MAX_PRICE_JUMP):
            return False, "price_band"
    # Evidence-consistency: the extracted price must appear in the source text.
    price_str = f"{record.offer_price:.2f}".rstrip("0").rstrip(".")
    if price_str not in normalize_price_text(record.evidence):
        return False, "evidence_mismatch"
    return True, "ok"


def dedupe_key(record: PriceRecord) -> tuple:
    return (
        record.sku,
        record.competitor,
        record.offer_price,
        record.currency,
        record.availability.value,
    )


def persist_if_changed(record: PriceRecord, last_key: tuple | None, store) -> bool:
    """Write a new history row only when the dedupe key changes."""
    key = dedupe_key(record)
    if key == last_key:
        return False  # no change; do not write a duplicate history row
    store.append(record)  # keyed by captured_at downstream
    return True

Catching Silent Failures

That last check, evidence consistency, is the single most valuable line in the pipeline. A silent failure is a record that is schema-valid and plausible but wrong, and the only thing that distinguishes it from a real price change is whether the number the model returned can be found in the text it claims to have read it from. If the extracted price is not present in the evidence, the record does not get trusted, full stop.

Validate on ingest even though the model should always return schema-valid JSON. The schema guarantee protects you from malformed output; the business-rule and evidence checks protect you from confidently wrong output, and those are different threats. This validation discipline is the same one that underpins a broader extraction architecture for any web data, not just prices.

Recurring Monitoring: Batch Extraction and Webhooks

A one-off extraction is a synchronous call. A recurring monitoring sweep across tens of thousands of products is an asynchronous job, and that is what the Batch API is for.

The pattern is to build a JSONL file with one line per competitor URL, where each line's custom_id is your record key (a SKU-and-competitor pair, say), and each body contains the cleaned HTML and the same response schema you use synchronously. You upload that file, create a batch against https://batch.inference.net/v1, and the job begins immediately. A single batch can include up to 50,000 requests and the input file can be up to 200 MB, with a 24-hour completion window.

import json
import os

from openai import OpenAI

from price_record_schema import PriceRecord

client = OpenAI(
    base_url="https://batch.inference.net/v1",  # NOTE: batch host, not api.inference.net
    api_key=os.environ["INFERENCE_API_KEY"],
)

SCHEMA = {
    "type": "json_schema",
    "json_schema": {
        "name": "price_record",
        "strict": True,
        "schema": PriceRecord.model_json_schema(),
    },
}


def build_batch_file(pages: dict[str, str], path: str = "batchinput.jsonl") -> str:
    """pages maps custom_id (your record key) -> cleaned HTML for that competitor page."""
    with open(path, "w", encoding="utf-8") as fh:
        for custom_id, html in pages.items():
            line = {
                "custom_id": custom_id,  # e.g. "EXT-4421@example-competitor.com"
                "method": "POST",
                "url": "/v1/chat/completions",
                "body": {
                    "model": "inference-net/schematron-v2-turbo",
                    "messages": [{"role": "user", "content": html}],
                    "response_format": SCHEMA,
                    "temperature": 0,
                },
            }
            fh.write(json.dumps(line) + "\n")
    return path


def submit_sweep(pages: dict[str, str]) -> str:
    path = build_batch_file(pages)  # up to 50,000 lines; file up to 200 MB
    batch_input_file = client.files.create(file=open(path, "rb"), purpose="batch")

    batch = client.batches.create(
        input_file_id=batch_input_file.id,
        endpoint="/v1/chat/completions",
        completion_window="24h",
        metadata={"description": "nightly competitor price sweep"},
        # webhook_url is not in the official SDK types; pass via extra_body in Python.
        extra_body={"webhook_url": "https://example.com/price-webhook"},
    )
    return batch.id


def collect_results(batch_id: str) -> dict[str, dict]:
    """Call this when your webhook reports status == 'completed'.

    The webhook POST body looks like:
        {"batch_id": "...", "status": "completed", "metadata": {...}}
    Output line order is NOT input order, so map by custom_id.
    """
    batch = client.batches.retrieve(batch_id)
    out = client.files.content(batch.output_file_id).text
    results: dict[str, dict] = {}
    for line in out.splitlines():
        row = json.loads(line)
        record = row["response"]["body"]["choices"][0]["message"]["content"]
        results[row["custom_id"]] = json.loads(record)
    return results

Two operational details make this production-grade. First, pass a webhook_url so you get an HTTPS POST with the batch id and status when the run finishes, instead of polling; note that this parameter sits outside the official SDK types, so in TypeScript you cast the params and in Python you pass it via extra_body. A webhook when the batch finishes is what lets you trigger validation and storage automatically. Second, the output line order does not match the input order, so you map results back to products by custom_id, never by position. You are only charged for completed requests; anything that expires lands in the error file for a retry. For smaller recurring runs, the Group API takes up to 50 requests in one payload with a single callback when the whole group is done.

A Cost Model for 10k, 100k, and 1M Pages

Extraction cost is one of the few parts of this pipeline you can compute exactly, so do it rather than guess. Start with a per-page token budget. A cleaned competitor product page runs around 6,000 input tokens, and a compact price record is around 300 output tokens. With those assumptions, per-page cost is just the token counts times the model's rates.

At current model-page pricing, Schematron V2 Turbo is $0.03 per million input tokens and $0.15 per million output tokens, which works out to roughly $0.000225 per page. Schematron V2 Small is $0.05 per million input and $0.25 per million output, or about $0.000375 per page. Scale those out:

Assumptions: ~6,000 input tokens per cleaned page, ~300 output tokens per record. Extraction only; fetch/proxy cost is separate.

The headline is that extraction is cheap. Monitoring a million pages costs a few hundred dollars on the extraction line, and your numbers will scale linearly with page size and schema size, which is why showing the formula matters more than the exact figure. The cost that dominates a price-monitoring budget is fetching, not extracting. Record-based fetch APIs that bundle proxy and rendering infrastructure run far higher per unit; Bright Data's Web Scraper API, for instance, is around $1.50 per 1,000 records on pay-as-you-go. That contrast is the whole argument for keeping fetch and extract on separate cost lines, and it is the same calculus behind any decision to build it in-house or call an API for the extraction layer.

Schematron V2 Small vs Turbo

The two models trade quality against throughput and cost, and a monitoring pipeline can use both.

Throughput and quality from the Schematron V2 launch evaluation; pricing from current model pages.

Use Turbo for the high-volume recurring sweep, where throughput and cost dominate. Reserve Small for the hard pages, or as a second-pass re-check on records that failed validation, where the marginal quality is worth the marginal cost. Both far exceed the previous-generation models on extraction quality.

Accuracy Checks and Human-Review Thresholds

No competitor price scraping pipeline should aim for zero human review; it should aim for a small, high-signal review queue. The way you get there is with thresholds rather than all-or-nothing trust.

Route a record to human review when any of these is true: a required field is missing, the price moved beyond a configured band relative to the last known value, the currency changed, or the evidence text does not contain the extracted price. Everything else flows straight through. Tuning the band is the lever that controls queue size: too tight and reviewers drown in routine promotions, too loose and a real error slips by. Start conservative and relax it as you build confidence in the validated fields.

A periodic sampling audit is a cheap insurance policy on top of the thresholds. Re-extract a random sample of pages with V2 Small and compare against what the Turbo sweep produced; systematic disagreement is your early signal of drift on a particular competitor's template.

The reason you can automate most records rather than reviewing all of them is that the extraction layer is measurably reliable. On the published Schematron V2 quality evaluation, where a GPT-5.4 judge graded extractions on a 1-to-5 scale, V2 Small scored 4.060 and V2 Turbo scored 4.039, with both models outperforming DeepSeek V3.2 and GPT-5.4 Nano. Turbo also processed 4.14 requests per second on a single H100 in the same evaluation, which is what makes the high-volume sweep practical. If you want to evaluate extraction quality on your own pages, the published extraction-quality metrics and how to reproduce them are worth reading before you commit to thresholds.

When Schematron Is Not the Right Layer

Schema-first extraction is the right tool for turning HTML into typed price records. It is not the right tool for every adjacent problem, and being clear about that is part of designing the pipeline well.

It is not the right layer when the entire problem is fetching. If you do not have the HTML yet because a site's anti-bot defenses are the blocker, you need a crawler, proxy, and headless-browser layer first; extraction has nothing to work on until that layer succeeds. It is not the right layer when the source is a pure PDF or image with no HTML, which is an OCR and document-intelligence problem. And it is not the right layer when you have no engineers to run a pipeline and what you actually want is a turnkey dashboard with product matching built in, which is what the packaged price scraping tools and price scraping software sell. If a single stable selector already works on a single site that never changes, you do not need a model at all.

The framing that keeps this honest is that the extraction model complements the fetch layer rather than replacing it. The win is on the correctness axis, turning messy retrieved HTML into records you can trust, and you should reach for it when that is where your bottleneck actually lives.

Conclusion

A competitor price scraping pipeline that holds up over time looks the same regardless of which sites you track: a stable typed schema with an evidence field, fetching kept separate from extraction, deduplication and validation that catch silent failures before they reach a pricing decision, batch extraction with webhooks for the recurring sweep, and an extraction cost that is a rounding error next to fetch. The schema is the contract that lets everything around it change.

For a small test, run a single page or a small batch through the model from the quickstart and see the records it produces against your own competitor pages. For ongoing monitoring at scale, where throughput, schema design, and dedicated capacity become the real questions, it is worth talking through the pipeline with someone who builds this.

Related Reading

• HTML-to-JSON Extraction API — the product-hub explainer on typed extraction versus DOM converters.
• Automated Data Extraction from Websites — architecture, schema design, and validation in depth.
• Firecrawl Alternatives for Structured Extraction — when the bottleneck is JSON quality, not crawling.
• Price Scraping with AI: Extract Product Prices into JSON — the single-page price extraction pattern this pipeline scales up.
• Product Data Extraction: E-Commerce HTML to a Typed Feed — extracting the full product record alongside the price.