Automating aggressive worth intelligence with Amazon Nova Act

Monitoring competitor costs is important for ecommerce groups to keep up a market edge. Nevertheless, many groups stay trapped in guide monitoring, losing hours every day checking particular person web sites. This inefficient strategy delays decision-making, raises operational prices, and dangers human errors that end in missed income and misplaced alternatives.

Amazon Nova Act is an open-source browser automation SDK used to construct clever brokers that may navigate web sites and extract information utilizing pure language directions. This publish demonstrates easy methods to construct an automatic aggressive worth intelligence system that streamlines guide workflows, supporting groups to make data-driven pricing choices with real-time market insights.

The hidden price of guide aggressive worth intelligence

Ecommerce groups want well timed and correct market information to remain aggressive. Conventional workflows are guide and error-prone, involving looking a number of competitor web sites for sure merchandise, recording pricing and promotional information, and consolidating this information into spreadsheets for evaluation. This course of presents a number of essential challenges:

• Time and useful resource consumption: Handbook worth monitoring consumes hours of workers time each day, representing a big operational price that scales poorly as product catalogs develop.
• Information high quality points: Handbook information entry introduces inconsistency and human error, doubtlessly resulting in incorrect pricing choices based mostly on flawed data.
• Scalability limitations: As product catalogs increase, guide processes turn out to be more and more unsustainable, creating bottlenecks in aggressive evaluation.
• Delayed insights: Essentially the most essential subject is timing. Competitor pricing can change quickly all through the day, which means choices made on stale information can lead to misplaced income or missed alternatives.

These challenges lengthen far past ecommerce. Insurance coverage suppliers routinely overview competitor insurance policies, inclusions, exclusions, and premium constructions to keep up market competitiveness. Monetary providers establishments analyze mortgage charges, bank card provides, and price constructions by time-consuming guide checks. Journey and hospitality companies monitor fluctuating costs for flights, lodging, and packages to regulate their choices dynamically. Whatever the trade, the identical struggles exist. Handbook analysis is gradual, labor-intensive, and susceptible to human error. In markets the place costs change by the hour, these delays make it nearly unattainable to remain aggressive.

Automating with Amazon Nova Act

Amazon Nova Act is an AWS service, with an accompanying SDK, designed to assist builders construct brokers that may act inside internet browsers. Builders construction their automations by composing smaller, focused instructions in Python, combining pure language directions for browser interactions with programmatic logic resembling checks, breakpoints, assertions, or thread-pooling for parallelization. By way of its software calling functionality, builders may allow API calls alongside browser actions. This provides groups full management over how their automations run and scale. Nova Act helps agentic commerce situations the place automated brokers deal with duties resembling aggressive monitoring, content material validation, catalogue updates, and multi-step searching workflows. Aggressive worth intelligence is a robust match as a result of the SDK is designed to deal with real-world web site conduct, together with format modifications and dynamic content material.

Ecommerce websites ceaselessly change layouts, run short-lived promotions, or rotate banners and elements. These shifts typically break conventional rules-based scripts that depend on mounted ingredient selectors or inflexible navigation paths. Nova Act’s versatile, pure language command-driven strategy helps brokers proceed working whilst pages evolve, offering the resilience wanted for manufacturing aggressive intelligence techniques.

Widespread constructing blocks

Nova Act features a set of constructing blocks that simplify browser automation. This can be utilized by ecommerce firms to gather and file product costs from web sites with out human intervention. The constructing blocks that allow this embody:

Extracting data from a webpage

With the extraction capabilities in Nova Act, brokers can collect structured information immediately from a rendered webpage. You’ll be able to outline a Pydantic mannequin that represents the schema that they need returned, then ask an act_get() name to reply a query in regards to the present browser web page utilizing that schema. This retains the extracted information strongly typed, validated, and prepared for downstream use.

Nova.act_get("Seek for 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi'.", schema=ProductData.model_json_schema())

Navigate to a webpage

This step redirects the agent to a particular webpage as a place to begin. A brand new browser session opens at a desired place to begin, enabling the agent to take actions or extract information.

nova.go_to_url(website_url)

Working a number of periods in parallel

Value intelligence workloads typically require checking dozens of competitor pages in a brief interval. A single Nova Act occasion can invoke just one browser at a time, however a number of cases can run concurrently. Every occasion is light-weight, making it sensible to spin up a number of in parallel and distribute work throughout them. This allows a map‑scale back fashion strategy to browser automation the place totally different Nova Act cases deal with separate duties on the similar time. By parallelizing searches or extraction work throughout many cases, organizations can scale back whole execution time and monitor massive product catalogs with minimal latency.

from concurrent.futures import ThreadPoolExecutor, as_completed

from nova_act import ActError, NovaAct

# Accumulate the whole record right here.
all_prices = []

# Set max staff to the max variety of lively browser periods.
with ThreadPoolExecutor(max_workers=10) as executor:
    # Get all costs in parallel.
    future_to_source = {
        executor.submit(
            check_source_price, product_name, source_name, source_url, headless
        ): source_name
        for source_name, source_url in sources
    }
    # Gather the leads to all_books.
    for future in as_completed(future_to_source.keys()):
        strive:
            supply = future_to_source[future]
            source_price = future.end result()
            if source_price is just not None:
                all_prices.lengthen(source_price.supply)
        besides ActError as exc:
            print(f"Skipping supply worth attributable to error: {exc}")

print(f"Discovered {len(all_prices)} supply costs:n{all_books}")

Captchas

Some web sites current captchas throughout automated searching. For moral causes, we suggest involving a human to resolve captchas relatively than making an attempt automated options. Nova Act doesn’t resolve captchas on the person’s behalf.

When working Nova Act regionally, your workflow can use an act_get() name to detect whether or not a captcha is current. If one is detected, the workflow can pause and immediate the person to finish it manually, for instance, by calling enter() in a terminal-launched course of. To allow this, run your workflow in headed mode (set headless=False, which is the default) so the person can work together with the browser window immediately.

When deploying Nova Act workflows with AgentCore Browser Device (ACBT), you should utilize its built-in human-in-the-loop (HITL) capabilities. ACBT offers serverless browser infrastructure with stay streaming from the AgentCore AWS Console. When a captcha is encountered, a human operator can take over the browser session in real-time by the UI takeover function, resolve the problem, and return management to the Nova Act workflow.

end result = nova.act("Is there a captcha on the display?", schema=BOOL_SCHEMA) if end result.matches_schema and end result.parsed_response:
    enter("Please resolve the captcha and hit return when executed")
...

Dealing with errors

As soon as the Nova Act shopper is began, it might encounter errors throughout an act() name. These points can come up from dynamic layouts, lacking parts, or surprising web page modifications. Nova Act surfaces these conditions as ActErrors in order that builders can catch them, retry operations, apply fallback logic, or log particulars for additional evaluation. This helps worth intelligence brokers keep away from silent failures and proceed working even when web sites behave unpredictably.

Constructing and Monitoring Nova Act workflows

Constructing with AI-powered IDEs

Builders constructing Nova Act automation workflows can speed up experimentation and prototyping through the use of AI-powered improvement environments with Nova Act IDE extensions. The extension is accessible for widespread IDEs together with Kiro, Visible Studio Code, and Cursor, bringing clever code era and context-aware help immediately into your most well-liked improvement surroundings. The IDE extension for Amazon Nova Act hurries up improvement by turning pure language prompts into production-ready code. As a substitute of digging by documentation or writing repetitive boilerplate, you may merely describe your automation targets. That is useful for advanced duties like aggressive worth intelligence, the place the extension will help you shortly construction ThreadPoolExecutor logic, design Pydantic schemas, and construct sturdy error dealing with.

Observing workflows within the Nova Act console

The Nova Act AWS console offers visibility into your workflow execution with detailed traces and artifacts out of your AWS surroundings through the AWS Administration Console. It offers a central place to handle and monitor automation workflows in real-time. You’ll be able to navigate from a high-level view of the workflow runs into the particular particulars of particular person periods, acts, and steps. This visibility lets you debug and analyze efficiency by exhibiting you precisely how the agent makes choices and executes loops. With direct entry to screenshots, logs, and information saved in Amazon S3, you may troubleshoot points shortly with out switching between totally different instruments. This streamlines the troubleshooting course of and accelerates the iteration cycle from experimentation to manufacturing deployment.

Working the answer

That can assist you get began with automated market analysis, we’ve launched a Python-based pattern challenge that handles the heavy lifting of worth monitoring. This resolution makes use of Amazon Nova Act to launch a number of browser periods directly, looking for merchandise throughout numerous competitor websites concurrently. As a substitute of going by tabs your self, the script navigates the online to seek out costs and promotions. It then gathers all the things right into a clear, structured format so you should utilize it in your individual pricing fashions. The next sections will describe how one can get began constructing the aggressive worth intelligence agent. After exploring, you may deploy to AWS and monitor your workflows within the AWS Administration Console.

The aggressive worth intelligence agent is accessible as an AWS Samples resolution within the Amazon Nova Samples GitHub repository as a part of the Value Comparability use case.

1. Stipulations

Your improvement surroundings should embody: Python: 3.10 or later and the Nova Act SDK.

2. Get Nova Act API key:

Navigate to https://nova.amazon.com/act and generate an API key. When utilizing the Nova Act Playground or selecting Nova Act developer instruments with API key authentication, entry and use are topic to the nova.amazon.com Phrases of Use.

3. Clone the repo, set the API key, and set up the dependencies:

To get began, clone the repository, set your API key so the applying can authenticate, and set up the required Python dependencies. This prepares your surroundings so you may run the challenge regionally with out points. An API Key may be generated on Nova Act.

# Clone the repo 
https://github.com/aws-samples/amazon-nova-samples.git 
cd nova-act/usecases/price_comparison 

# Create and activate a digital surroundings (non-compulsory however really helpful) 
python3 -m venv .venv 
supply .venv/bin/activate 

# Home windows:
.venvScriptsactivate 

# Set up Python dependencies 
pip set up -r necessities.txt 

# Set the Nova Act API Key export NOVA_ACT_API_KEY="your_api_key"

4. Working the script

As soon as your surroundings is about up, you may run the agent to carry out aggressive worth intelligence. The script takes a product identify (non-compulsory) and an inventory of competitor web sites (non-compulsory), launches concurrent Nova Act browser periods, searches every website, extracts worth and promotional particulars, and returns a structured, aggregated end result.

The earlier instance makes use of the script’s default competitor record, which incorporates main retailers resembling Amazon, Goal, Greatest Purchase, and Costco. You’ll be able to override these defaults by supplying your individual record of competitor URLs when working the script.

python -m fundamental.py 
    --product_name "iPad Professional 13-inch, 256GB Wi-Fi" 
    --product_sku "MVX23LL/A" 
    --headless

The agent launches a number of Nova Act browser periods in parallel, one per competitor website. Every session masses the retailer’s web site, checks whether or not a captcha is current, and pauses for person enter if one must be solved. As soon as clear, the agent searches for the product, opinions the returned outcomes, clicks probably the most related itemizing, and extracts the worth and promotional data. Working these flows concurrently permits the agent to finish a multi-site comparability effectively.

For instance, when concentrating on Amazon, the agent opens a recent browser session, navigates to amazon.com, and performs a site-specific seek for the product. It inspects the returned outcomes, identifies the product itemizing that the majority carefully matches the question, and extracts key particulars resembling worth, promotions, availability, and related metadata. This course of is mirrored within the following terminal output that displays every reasoning step (costs on this instance are illustrative and never consultant of actual market costs):

583c> act("Is there a captcha on the display?, format output with jsonschema: {"kind": "boolean"}")
583c> ...
583c> suppose("I'm on the Amazon homepage. My activity is to return whether or not there's a captcha on the display. I can look across the web page to try to discover a captcha. I do not see something that appears like a captcha. I additionally do not see something that looks as if it will require a captcha to be displayed. I ought to return false to point that there isn't any captcha on the web page.");
...
583c> act("Seek for 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi'.")
583c> ...
583c> suppose("I'm on the amazon homepage. My activity is to seek for 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi'. I see an empty search discipline. I have to kind 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi' into the search discipline.");
583c> ...
583c> suppose("I see that the search discipline is populated with 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi', however the search has not been initiated. Due to this fact, my final motion was not appropriate. I see a search button to the precise of the search discipline which can be utilized to finish the search. I'll click on on the search button to finish the search.");
...
583c> suppose("I'm now on the search outcomes web page for 'iPad Professional 13-inch (M4 chip), 256GB Wi-Fi'. My activity is full, and I have to return.");
...
583c> suppose("I'm on the Amazon search outcomes web page for "iPad Professional 13-inch (M4 chip), 256GB Wi-Fi". My activity is to overview all the outcomes and discover probably the most related one, then extract the worth and any promotion particulars. I have to return the data within the specified JSON format. I see an inventory of merchandise, however the first few are sponsored. I ought to scroll down the web page to seek out probably the most related, non-sponsored product.");
583c> ...
583c> suppose("The web page has scrolled down and I see extra merchandise. My prior motion of scrolling down was profitable. I see the primary non-sponsored product, which is the "Apple iPad Professional 13-Inch (M4): Constructed for Apple Intelligence, Extremely Retina XDR Show, 256GB, 12MP Entrance/Again Digital camera, LiDAR Scanner, Wi-Fi 6E, Face ID, All-Day Battery Life — Area Black". I ought to click on on the product to navigate to its web page and discover the worth and promotion particulars.");
583c> ...
583c> suppose("I'm now on the product web page for the "Apple iPad Professional (M4) 13-inch (M4 chip), 256GB Wi-Fi". My prior motion of clicking on the product was profitable. I see the worth of the product is $1,039.99 and there's a promotion for 19% off. I ought to return the worth and promotion particulars within the specified JSON format.");

4. Reviewing the output

After the agent finishes looking all competitor websites, it returns a consolidated desk that lists every retailer, the matched product, the extracted worth, the promotion particulars, and extra metadata. From this desk, you may evaluate outcomes throughout a number of sources in a single view. For instance, the output may look as follows (costs on this instance are illustrative and never consultant of actual market costs):

| Supply | Product Identify | Product SKU | Value | Promotion Particulars |
|--------|--------------|-------|-------|-------------------|
| Amazon | Apple iPad Professional (M4) 13-inch (M4 chip), 256GB Wi-Fi | MVX23LL/A | $1,039.99 | 19% off |
| Greatest Purchase | Apple - 13-inch iPad Professional M4 chip Constructed for Apple Intelligence Wi-Fi 256GB with OLED - Silver |  MVX23LL/A | $1239.00 | Save $50 |
| Costco | iPad Professional 13-inch (M4 chip), 256GB Wi-Fi | MVX23LL/A | $1039.99 | $200 OFF; financial savings is legitimate 11/12/25 by 11/22/25. Whereas provides final. Restrict 2 per member. |
| Goal | Apple iPad Professional (M4) WiFi with Customary glass | MVX23LL/A | $999.00 | Sale ends Wednesday |

The agent writes the extracted outcomes to a CSV file to later combine with pricing instruments, dashboards, or inside APIs.

Conclusion

Amazon Nova Act transforms browser automation from a posh technical activity right into a easy pure language interface, so retailers can automate guide workflows, scale back operational prices, and acquire real-time market insights. By considerably decreasing the time spent on guide information assortment, groups can shift their focus to strategic pricing choices. The answer scales effectively as monitoring wants develop, with out requiring proportional will increase in sources. Nova Act allows builders to construct versatile, sturdy brokers that ship well timed insights, decrease operational effort, and assist data-driven pricing choices throughout industries.

We welcome suggestions and would love to listen to how you employ Nova Act in your individual automation workflows. Share your ideas within the feedback part or open a dialogue within the GitHub repository. Go to the Nova Act to study extra or discover extra examples on the Amazon Nova Samples GitHub Repository.

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