AI AGENT DATA SOLUTIONS
Let your AI agent
see the market as it happens
Connect live Amazon, search, trend and compliance data to your agent. Give every research step, recommendation and action current evidence—not stale knowledge or copied pages.
Which new “portable fan” products are entering an opportunity window?
EVIDENCE READYCandidate directions found, with facts, changes and open questions separated
Trusted by 2,000+ ecommerce and development teams
THE DATA GAP
Same question. Same model.
Very different answer.
The difference is whether the agent can retrieve evidence from the market now. Choose a task to compare both workflows.
Which product opportunities in this category deserve validation first?
“Look for portable, energy-efficient and low-noise products. Review bestseller lists, demand and customer feedback.”
Reasonable—but it cannot answer:Which products are growing now, and what evidence supports the opportunity?
THE REAL DIFFERENCEIt is not whether AI can produce an answer. It is whether that answer maps to now, to specific objects and to verifiable evidence.
WHY REAL-TIME DATA
Agents can reason.
They do not inherently know what just changed.
Freshness is only the first requirement. Market work also needs objects, sources, structure, continuity, location and clear operating boundaries.
PANGOLINFO DATA LAYER
Build an agent-ready data layer
between the model and the live market
Pangolinfo handles access, rendering, parsing and structure, so your agent can focus on selecting tools, combining evidence and moving the task forward.
Pangolinfo
Retrieve live Amazon product, search, list, review, seller and category data.
Answer product, competitor, listing and monitoring questions with fields and capture context intact.
FROM DATA TO CAPABILITY
Your agent does not simply know more.
It can do more.
Move from signal discovery and verification to monitoring, product workflows and team decisions.
Find change
Spot meaningful movement across lists, new products, search, price and reviews before a person has to notice it first.
Test a hypothesis
Take the model’s ideas back to product, review, SERP and risk evidence.
Watch continuously
Turn one-time research into scheduled collection, change detection and triggers.
Enter your systems
Deliver structured results to an app, database, workspace or another agent.
Support decisions
Move evidence, conclusions and review items into the team’s existing workspace.
CHOOSE YOUR ROUTE
Start with how the agent needs to work.
Then choose the interface.
Interactive research, repeatable tasks and production pipelines need different levels of control. The three routes can also work together.
INTERACTIVE RESEARCH
Let the agent discover and call live tools for the question
Designed for MCP-compatible environments such as Codex, Claude Code and WorkBuddy. The agent can inspect available capabilities, then select product, review, list, search or compliance tools.
- Ask in natural language without moving data by hand
- Continue questioning, comparing and validating results
- Fit exploratory and multi-step research
REUSABLE WORKFLOW
Package repeatable business work as an agent-readable Skill
Use a Skill for product research, listing optimization, competitor radar and other tasks with an established method. It tells the agent which data to use, how to analyze it and what to deliver.
- Reduce repeated prompting and workflow design
- Standardize the team’s method and output
- Combine with MCP for both tools and guidance
PRODUCTION PIPELINE
Build a controlled, scalable data layer for your own AI product
Use REST APIs for batch jobs, schedules, databases, warehouses and production systems. Your team controls requests, fields, concurrency, caching and delivery.
- Fit predictable, scheduled and high-volume jobs
- Write results to databases, BI, tables or apps
- Keep complete control of execution and delivery
WORK WITH YOUR AGENT
Keep the AI you already use.
Give it a reliable data connection.
Select your current agent to see a practical starting route, the best-fit tasks and a first prompt to test.
RECOMMENDED · MCP + SKILL
Let Codex validate product decisions with live market facts
Use MCP for live tools, then add Skills when useful. A strong fit for data products, competitor analysis, field validation and project delivery.
Platform features and setup can change. Check the latest Pangolinfo and client documentation before connecting.
START WITH A REAL TASK
Start with one verifiable question.
Let the agent’s capability expand from there.
Choose the task closest to the business. Let the agent retrieve evidence first, reason second and return something a person can review.
Product and niche research
Categories, lists, trends, price bands and review density
Sponsored placement monitoring
Keyword results, ad positions and competitor movement
New-product monitoring
New Releases, new ASINs and early performance signals
Competitor monitoring
Price, rank, promotion, seller and review changes
Keyword research and optimization
Search positions, competitor language and real customer terms
Titles and bullet points
Product facts, review VOC and reviewable copy suggestions
CAPABILITY WITH CONTROL
Give the agent more capability.
Keep the team in control.
Live data reduces errors caused by stale context. Scope, usage, location, judgment and final actions remain visible to the team.
in controlYour team owns the action
Visible planDeclare tools, objects, fields and steps first.
Bounded scopeLimit marketplaces, objects and collection depth.
Controlled usageFilter first, then retrieve deeper evidence.
Managed accessUse separate keys and least-necessary permissions.
Explicit locationSet marketplace, language and postcode context.
Traceable evidenceKeep capture time, source and original context.
Reviewable reasoningSeparate facts, inference and open questions.
Approved actionsKeep human checks before writes and publishing.
FAQ
Questions teams ask before
connecting an AI agent
The fastest validation starts with one client, one real question and a small set of objects.
Why not let the AI browse the web on its own?
AI web search works for a small number of public facts. It is poorly suited to decisions involving hundreds or thousands of products or many pages of results. The agent would also have to handle anti-bot systems, CAPTCHA and location checks, JavaScript rendering, pagination, missing data and changing page structures. Passing raw HTML back to the model then consumes a large context window and many tokens. Pangolinfo handles access, rendering, parsing and structure before returning only the fields the agent needs.
Why not pass full pages or all raw data to the model?
Raw pages contain navigation, scripts, styling, recommendations and repeated content. Most of it is irrelevant to the decision. Structured product, price, rank, review, ad-position and timestamp fields reduce wasted context and reserve model tokens for comparison, reasoning and output.
Can an agent analyze a massive product set in one pass?
It should not read an unlimited set. First narrow the candidates through categories, lists, search or explicit rules, then retrieve deeper product and review evidence for the smaller set. Use REST APIs or a data pipeline for collection and preprocessing at scale; let the agent explain the filtered results.
Should I use MCP, an AI Skill or a REST API?
Choose MCP for interactive research, a Skill for a repeatable business method, and REST APIs for batches, schedules, warehouses or your own AI product. They also combine well: APIs handle scale, MCP handles interaction, and Skills make the method reusable.
How does Pangolinfo handle anti-bot systems and dynamic pages?
Pangolinfo packages access, proxy orchestration, rendering, verification handling and Amazon-specific parsing into the data layer. The agent calls relatively stable structured fields instead of writing a brittle scraper for every task.
Does live data make every agent answer correct?
No. Live data improves freshness, access and evidence. It does not replace a clear objective, representative sampling, sound analysis or human review. Ask the agent to distinguish source facts, model inference and items that still need validation.
How do I control data usage and cost?
Ask the agent to state its plan before calling tools. Set the marketplace, objects, fields, pages and limits. Search and filter first, then retrieve detail or reviews. For scheduled or batch work, add caching, concurrency, budget and stop conditions at the API layer.
Will every marketplace and location return the same result?
No. Price, stock, delivery, search results, ads and some content can vary with marketplace, language and postcode. Treat location as part of the agent’s data context whenever the task relates to a specific market.
Can the data enter my app or team workspace?
Yes. REST APIs fit apps, databases, BI and automation systems; MCP and Skills fit live research and workflow orchestration inside an agent. Your team controls credentials, permissions, delivery and final actions.
GIVE YOUR AGENT LIVE CONTEXT
Make the next AI answer
reflect the market now
Connect one agent and test one real question. Validate the data, fields and output before expanding into a team workflow or production pipeline.
