Agentic Commerce Development Company in USA
We are a US-based agentic commerce software development company headquartered in Miami, Florida. We help US merchants whose customers have started asking ChatGPT, Gemini, Claude or Perplexity to do the shopping for them, and procurement teams whose buyers now negotiate through enterprise bots rather than email threads. Some weeks those agents drive an unexpected lift; other weeks they ignore your catalog because the data is not clean enough or the checkout looks unsafe. Agentic commerce development is the engineering work that fixes both ends of that gap.
Since 2014 we have shipped commerce platforms, fintech integrations, healthtech systems and AI products for US retailers, marketplaces and B2B platforms. For agentic commerce we ship product feed audits, Schema.org structured data, agent-discoverable endpoints, signed checkout APIs, an evaluation suite and the governance layer that keeps finance, legal and compliance comfortable. No marketing-driven AI features bolted onto a storefront — real protocol-level engineering that survives a Friday afternoon in Q4.
What agentic commerce really is, in plain English
A definition that matches what US merchants actually pay us to fix.
A few definitions are floating around the industry. The one we use, because it matches what buyers actually pay us to fix, is this: agentic commerce is the practice of letting a software agent — an LLM-powered system with tools, memory and policy — do the research, comparison and purchase that a human used to do on a website. The agent might be ChatGPT shopping mode, a custom assistant inside a US bank app, an enterprise procurement bot at a Fortune 500 buyer, or a wallet-native shopper that lives in a browser extension on the buyer's laptop.
That is a real shift, not a vendor pitch. Shopify and Walmart have publicly reported that AI-originated traffic and orders are growing several-fold per year. McKinsey has put the total addressable value of agentic commerce in the trillions of dollars by 2030. You can argue with the numbers. You cannot honestly argue that the buyer is staying the same.
The implication for engineering is straightforward. The buyer no longer sees your hero banner, your CSS animations or your testimonial carousel. The buyer reads your product feed, your structured data, your /robots.txt, the JSON your APIs return and the price guarantee window encoded in your checkout response. Whatever quality your storefront has at the protocol level is the quality the buyer experiences. Marketing copy stops mattering once the buyer is a piece of software.
If you want a working introduction to what shopping agents currently do well and where they fail, the Google product structured data guide, the Schema.org Product reference and the W3C Agent Protocol Community Group are the most honest starting points we have found in 2026.
Already running AI on the human-facing side? Our AI e-commerce development service covers recommendation engines, personalization platforms and conversational shopping assistants for human shoppers; the agentic commerce service on this page is the protocol-layer work that prepares the same store for the next buyer — the autonomous one.
Who we build agentic commerce capabilities for
Three buyer profiles we see on every discovery call this year.
Brand-direct US retailers
You sell your own products and you have started seeing AI agent referrers in your analytics. The signal is small but consistent, and your team is being asked by leadership whether the company should embrace it or quietly hope it goes away. We bring the technical architecture and the operating model so the board conversation has numbers and a roadmap behind it — not a deck.
Marketplaces and aggregators
You have hundreds of thousands of SKUs from third-party sellers, and the data quality is uneven. Agents skip products that look ambiguous, which means a structural revenue ceiling. We design the seller scorecards, the schema enforcement and the catalog hygiene work that lifts that ceiling without breaking the seller experience or the marketplace's commission economics.
Vertical commerce platforms
You operate a niche US commerce platform — pharmacy, B2B parts, used vehicles, hospitality, regulated specialty — and you want a defensible position before generic agents flatten your category. We help you ship agent-friendly APIs that competitors will struggle to replicate without your domain catalog and your supply chain.
If your situation does not fit any of the three, that is also useful information. We have walked away from engagements where the simpler answer was a clean e-commerce development rebuild or a focused AI e-commerce project on the human-facing side. We tell you that on the first call — before you sign anything.
How we make a US store ready for AI shopping agents
A pragmatic, protocol-first methodology. Each phase ships a working artifact your team keeps using.
We run a five-phase program. We deliberately do not start with a flagship "agent" feature, because a flagship that sits on top of broken catalog data fails on the demo day no matter how clever the prompt is. Sequence matters more than novelty.
1. Catalog audit and gap report
Two engineers and a senior PM spend roughly two weeks running diagnostics on your catalog: missing attributes, conflicting prices between PIM and storefront, image alt-text quality, duplicate SKUs, GTIN coverage, category taxonomy drift, and how Google Shopping or Bing Shopping currently render your products. The deliverable is a prioritized backlog with the dollar impact of each fix attached, so finance and merchandising can pick where to start without arguing about which engineer "feels" most strongly about a missing barcode.
2. Structured data and feed freshness
We implement Schema.org Product markup, an offer model with availability and price, and a clean merchant feed with a signed freshness SLA usually under 60 seconds. Where shopping agents look first — the JSON-LD on the product page, the merchant feed, the sitemap — we make sure they find consistent answers. We tune the same surface for Google's Product Studio, Bing Shopping and the OpenAI shopping pipeline so a single investment lifts every agent simultaneously.
3. Agent-discoverable endpoints
We add a discoverable capability map at /.well-known/agent.json, an MCP server that exposes safe read tools (search, recommend, availability, sizing, compatibility), and rate-limited public APIs with explicit headers documenting authentication, refresh windows and consent. Agents that obey conventions can transact with you. The rest get a documented refusal rather than an opaque 403.
4. Signed checkout and price-guarantee API
This is where most projects die if they were rushed. We implement a checkout endpoint that issues time-bound order intents, requires a verified agent identity and consent token, and returns a price guarantee window the merchant can defend. We work with your payment provider (Stripe, Adyen, Braintree, Worldpay) so chargebacks and refunds reuse the same signed channel and reconcile cleanly with PCI-DSS audit trails. State-by-state shipping rules, sales tax, age verification (alcohol, firearms, vape, controlled substances) and import duties are encoded in the response, not bolted on at fulfillment time.
5. Evaluations and governance
We measure task completion rate, hallucination rate, price drift between feed and checkout, consent trace coverage and audit log completeness. We design a human-on-the-loop dashboard for high-risk actions, and we wire alerting that does not flood your on-call team. This phase usually overlaps with our AI agent observability practice and our LLM evaluation engineering work, so eval gates ship into your CI pipeline alongside unit tests rather than as a separate quarterly review.
Typical timeline: 8 to 14 weeks for a full rollout, 3 to 4 weeks for the audit-plus-feed milestone if leadership wants a fast first signal before committing to the rest. Multi-brand retailers, regulated payments and headless front ends usually push the upper bound.
Engineering for the buyer that does not see your hero image.
Engagement models and honest pricing ranges
We work in three commercial shapes. They differ in who owns scope and how risk is shared. We will tell you which one fits before you ask.
Project build
We deliver an agentic commerce rollout end-to-end with a fixed budget and a written change-control process. Range: USD 45,000 to 180,000. Best when leadership wants a defined deliverable with a clean handoff to your team. See our project-based outsourcing page for terms.
Dedicated agentic commerce squad
A multi-quarter retainer with a US tech lead embedded in your roadmap. Three to six engineers plus a PM, sized to your catalog and platform. Range: USD 22,000 to 60,000 per month. More on dedicated teams.
Staff augmentation
Senior engineers plug into your existing squads with your tools and your ceremonies. Rate: USD 65 to 110 per hour depending on seniority and stack. Miami HQ with same-day collaboration across US time zones. Details on staff augmentation.
Note on pricing. We share ranges because we have watched too many merchants stall for a quarter while a competitor shipped. A precise figure needs a discovery call and, on larger programs, a short paid discovery. The ranges above are the bands we actually quote against in 2026 for US-headquartered senior delivery.
Realistic agentic commerce use cases
Where this work lands inside a real US organization, not in a slide.
Search visibility for shopping agents
A mid-size US apparel retailer noticed AI agents recommending competitors despite having better price and stock. The fix was structured data, a clean merchant feed and a public availability endpoint. Recommendation share inside the agent funnel went from low single digits to roughly a third within ten weeks — without changing a single product page render.
Procurement and B2B replenishment
A B2B parts marketplace exposed an MCP server and a signed checkout endpoint to enterprise procurement agents. Cycle time on repeat orders dropped from forty minutes per buyer to under two minutes, with the same approval workflow on the customer side and the same SOX-compliant audit trail.
High-AOV configured products
A bicycle brand offering custom builds added a configurator that an agent can drive. The agent answers customer questions, validates compatibility against the live PIM and produces a configured order intent the human approves before checkout. Configuration errors at fulfillment dropped to near zero, and agent-attributed AOV ran 1.7x higher than the human funnel.
Subscription and reorder flows
A US pet food retailer integrated a shopper agent that handles reorder windows, dosage updates and substitutions when a flavor goes out of stock. Cancellation rate on subscriptions went down because the agent caught problems the human never bothered to email about — and CSAT on assisted reorders moved up.
Refund and customer-care agents
A national footwear retailer let an agent triage returns up to a fixed dollar threshold, escalating to a human only on edge cases. Average handling time fell from eleven minutes to under two, and CSAT on the agent-handled returns landed slightly higher than the human baseline. The dollar threshold is governed by a written policy reviewed by legal every quarter.
Cross-border and currency-aware agents
A US homeware brand selling into Canada, Mexico and the EU used a single signed checkout API that returns currency, tax and duty estimates the agent can show before placing the order. Fewer surprises at the doorstep, fewer chargebacks at the bank and a noticeable drop in international support tickets.
Case Study: Industrial MRO Marketplace, Chicago, IL — Twelve Weeks to First Signed Procurement Agent Orders
One of the most instructive agentic commerce projects we shipped in 2026 involved a US-based industrial maintenance, repair and operations (MRO) parts marketplace headquartered in Chicago, Illinois. The company connects roughly 1,400 distributors of industrial fasteners, bearings, motors, valves and electrical components to approximately 18,000 enterprise procurement teams across manufacturing, food processing, energy and logistics. Annual GMV was $310 million, and the platform listed about 1.4 million SKUs across 12 categories sourced from PIMs that had been merged through three different acquisitions.
The numbers told a familiar but frustrating story. Their largest enterprise customers had quietly started routing repeat purchasing through procurement agents built on top of OpenAI Operator and a couple of Anthropic-based internal tools. The agents fetched the marketplace's product feed, but completed almost none of the orders they tried to place. Three failure modes dominated:
- Price drift between feed and checkout. Roughly 9.6% of agent orders failed because the price the agent had read from the feed had already changed by the time the order intent was submitted, often by less than a dollar but enough to break the procurement contract.
- Identity rejection. The marketplace's checkout was hardened against bots, but had no way to distinguish a Fortune 500 procurement agent acting on a verified buyer's behalf from a scraper. Both got the same opaque 403.
- Catalog ambiguity. Three legacy PIMs meant the same fastener could appear with three different GTINs, two different product images and inconsistent thread specs. Agents skipped ambiguous SKUs because picking the wrong one risked a real production line going down.
They had explored a couple of off-the-shelf "agent commerce" plugins from their e-commerce vendor and a "we'll build a chatbot" pitch from a Big Four firm. Neither addressed the protocol surface. They needed a system that understood industrial procurement as a domain, the SOX-aligned approval workflows their customers ran internally, and the realities of multi-PIM data hygiene.
Over twelve weeks, our five-person team (one tech lead, two senior back-end engineers, one platform engineer focused on payments and observability, and a part-time PM) designed and deployed an agentic commerce stack directly on top of their Shopify Plus storefront and custom Node.js order management service.
Catalog and feed. We collapsed the three legacy PIMs into a single source-of-truth schema with strict GTIN, manufacturer part number and category enforcement, then built a continuous diff job between PIM and storefront with a published 45-second freshness SLA. Where category metadata was ambiguous, we shipped a seller scorecard that surfaced the gaps to the distributor without breaking their existing publishing workflow. Within four weeks, 92% of the catalog passed the new schema gate.
Agent endpoints. We added /.well-known/agent.json, an MCP server exposing safe read tools (search by spec, compatibility check, availability across distribution centers, contract-price lookup) and a rate-limited GET /catalog public API. Authenticated procurement agents got a richer view that included their contract price, freight estimate and lead time; unauthenticated agents got the public price and an honest "sign in for contract pricing" header.
Signed checkout and price guarantee. We implemented a checkout endpoint that issued time-bound order intents with a five-minute price guarantee window, a verified agent identity (signed token from the buyer's IdP), and a consent receipt that mirrored the buyer's internal SOX approval. We worked with their existing Stripe + ACH integration so chargebacks reused the same channel and reconciled cleanly with their finance team's General Ledger.
Evaluations and governance. We wired a TCR dashboard, a price-drift monitor, a hallucination probe (sample agent responses against a curated golden dataset) and a consent-trace exporter that satisfied their largest customer's internal audit team. A human-on-the-loop dashboard catches anomalies (orders 5x the buyer's rolling average, attempts to redirect ship-to addresses, agents that try to bypass contract pricing) and surfaces them to a small ops team in Chicago.
Results after twelve weeks in production:
−82%
Cycle time on repeat procurement orders, from roughly forty minutes per buyer (search, compare, raise PO, send to vendor) to under seven minutes end to end with the same SOX approvals on the customer side.
4.2x
Agent-attributed GMV by week twelve compared to the pre-engagement baseline, driven mainly by enterprise procurement agents now completing the orders they had been abandoning before.
+34%
Task completion rate on the agent funnel over the audit baseline, measured weekly against a curated set of agent-driven scenarios validated by the marketplace's largest five customers.
0.4%
Price drift between feed and checkout by week twelve, down from 9.6% in week one. The merchant's finance team finally signed off on a contractual price guarantee window for procurement agents.
The platform was built with Shopify Plus, Node.js, PostgreSQL, an MCP server in TypeScript, Claude and GPT for evaluation prompts, Stripe for payments and a Datadog-based observability stack. The marketplace has since extended the agent surface to international distributors in Canada and Mexico, and is piloting a vendor-side agent that helps distributors keep their feed metadata clean. Average customer support tickets per order dropped 31% because procurement agents pre-empt most "did this ship?" questions through the order-status endpoint. Want results in your domain? Let's talk — or read related work in our case studies library.
Why US merchants pick Siblings Software for agentic commerce work
We engineer for the buyer that does not see your hero image.
Siblings Software is a US-headquartered software outsourcing and staff augmentation company that has been delivering production work for U.S. and LATAM clients since 2014. Across the last decade we have shipped commerce platforms, fintech integrations, healthtech systems and AI-driven products. The bench that runs agentic commerce engagements is the same one that runs our AI agents development, MCP development and AI agent observability practices.
Senior-only delivery
Every squad is led by an engineer who can design protocols, not just implement tickets. Junior engineers exist on our teams but do not set direction in regulated commerce work. The person designing your checkout endpoint has shipped one before.
US-aligned time zones
Miami HQ with nearshore engineering aligned to US Eastern. We do code review the same business day, not with a 12-hour lag. That difference shows up the first time a checkout fails on a Friday afternoon during peak holiday traffic.
References we can talk about
Engagements across pharmacy, B2B parts, apparel, pet supplies, footwear and homeware. We will introduce you to a previous US client when scoping is far enough along to make a reference call worthwhile.
Security & compliance built in
SOC 2-aligned processes, OWASP-aligned reviews, PCI-aware checkouts, CCPA/CPRA-friendly consent surfaces and HIPAA-aware patterns when the merchant operates in pharmacy or wellness. We do not bolt compliance on at the end; we build it into the protocol surface from sprint one.
A written engagement plan
Before sprint one you receive a squad charter, a Definition of Done, the KPI sheet, the risk register and a roll-back plan. No "we will figure it out as we go" onboarding, no Slack-channel-as-spec.
Vendor-independent
We work with OpenAI, Anthropic, Google, Mistral and open-source stacks like Llama and Qwen. The protocol surface is the same. Vendor choice is a tactical decision, not a strategic one — and we'll fight for it to stay that way.
When a software agent is your customer, your protocol surface is your storefront.
Agentic commerce vs the alternatives buyers compare us with
When this conversation reaches procurement, the comparison is almost always between four options.
We are honest about where we are not the right answer.
Freelancers and marketplaces
Cheapest sticker price, highest variance. Fine for a one-off feed cleanup. Falls over when the work touches payments, identity or governance, because nobody owns the on-call rotation when something goes wrong at 2 a.m. on Black Friday.
In-house engineering teams
The right long-term answer when commerce is core to your business. The wrong answer when you need to ship in a quarter, you do not have a senior commerce platform engineer free, or hiring senior agentic AI talent locally is a multi-month exercise. Hybrid setups work well: we lead while you scale your team.
Big consulting agencies
Strong on strategy and frameworks, often weaker on production engineering. Estimates tend to be optimistic because they are written by people who will not be on the call when a checkout is rejected. Useful when your board needs an external playbook. Less useful when you need a working API by the end of the quarter.
Managed senior squad (us)
You get a tech lead, senior engineers, written commitments, a shared definition of success and an honest report every two weeks. The trade-off is a higher day rate than a freelancer, and you trust a partner with meaningful delivery responsibility. That trust is earned sprint by sprint, not page by page.
Risks we have seen and how we mitigate them
Agentic commerce projects fail in a small number of recognizable ways. The mitigations below are how we keep them out.
Catalog drift between feed and storefront
Agents punish inconsistency more than humans do, because they read both surfaces in parallel. We add a freshness SLA, a single source of truth in the PIM, and a continuous diff job that pages someone if the feed lags by more than the agreed window. Drift gets caught in 60 seconds, not a quarterly review.
Price guarantee abuse
Bad actors will mint thousands of order intents to lock in stale pricing. We treat the order intent as a signed, time-bound and rate-limited artifact, with risk scoring on the issuing agent identity. Anomalous patterns escalate to a human-on-the-loop with a written audit trail that finance can defend in a chargeback dispute.
Hallucinated product attributes
If your data is good, the agent has nothing to invent. We push aggressively on data quality before adding any clever model. When we can, we constrain the agent to retrieved attributes only, and we log every attribute it surfaces so quality regressions are catchable. This is where our LLM evaluation engineering practice usually plugs in.
Compliance and consent confusion
US states (California's CCPA/CPRA, Virginia's CDPA, Colorado's CPA) and federal guidance (FTC autonomous transactions) expect explicit consent for autonomous purchases. We build a consent surface the agent must read and a signed receipt the merchant keeps. This is where we usually borrow from our AI code security practice.
Lock-in to a single agent vendor
Some clients arrive with a contract pushed by one vendor that forces a custom integration. We push for protocol-level integrations — structured data, signed checkout APIs, MCP — that work for any well-behaved agent and avoid future migration pain.
Cannibalizing organic SEO
Teams sometimes split agent-friendly endpoints onto a parallel domain or block them with a permissive robots policy that hurts conventional ranking. We align canonicals, sitemaps and consent so the same investment lifts both surfaces — agents and Google.
Benefits of agentic commerce for US merchants in 2026
The protocol-level investment that lifts every shopping agent at once.
According to the National Retail Federation's research on AI adoption, US retailers that invested in machine-readable catalogs and structured data in 2025 saw materially better Google Shopping placement and earlier inclusion in OpenAI and Anthropic shopping pipelines than peers who waited. Shopify's developer documentation on AI commerce echoes the same pattern: clean data ships first, clever models second. Here is what agentic commerce adds to your stack:
Agent-readable catalog
Schema.org Product markup, GTIN coverage, fresh availability and consistent attribute taxonomy. The same investment that lifts agent visibility also feeds Google Shopping, Bing Shopping and the OpenAI shopping pipeline at the same time.
Signed checkout API
Time-bound order intents, verified agent identity, defensible price-guarantee window and a single signed channel reused for refunds and chargebacks. Finance, legal and compliance can defend the protocol in the same meeting.
Procurement-ready B2B
Contract pricing, freight estimates, lead time and approval-aware order intents that a procurement agent can hand to a buyer's internal SOX workflow without hallucination or copy-paste errors.
Evaluation suite + dashboards
Task completion rate, hallucination probes, price drift, consent trace coverage and audit-log completeness shipped as code into your CI — not a quarterly slide deck.
Human-on-the-loop governance
High-risk actions (price changes outside the guarantee window, refunds above policy, ship-to changes, anomalous order volume) escalate to a small ops team with a written runbook, not a wall of Slack messages.
Vendor independence
Protocol-level work survives any single LLM vendor's pricing change, deprecation or strategy pivot. Your store keeps shipping orders even when the agent ecosystem reorganizes itself.
For deeper background on the standards and policies driving this work, the Model Context Protocol documentation, OpenAI's research publications and the Anthropic research blog publish ongoing technical detail on agent behavior and safety. The FTC business guidance blog is the most useful US regulatory source we track.
Choose us as your
Agentic Commerce Development Company
in USA
Industries
Agentic commerce delivers measurable results across regulated and unregulated US verticals.
We build agentic commerce capabilities for US companies across a wide range of industries. Here are the verticals where protocol-level work consistently delivers the highest ROI in 2026:
Industrial & MRO B2B
Procurement-agent-aware checkout, contract-price lookup, multi-distributor catalog hygiene and SOX-aligned order intents that plug into a buyer's existing approval workflow without hallucination.
Pharmacy & health
HIPAA-aware consent surfaces, prescription-aware availability checks, dosage and refill agents, and signed checkout that respects state-by-state shipping rules and DEA-controlled categories.
Apparel & lifestyle
Sizing-aware agent endpoints, return-aware order intents, visual search compatibility for "shop the look" agent flows, and bundling logic the agent can actually defend at checkout.
Food, grocery & subscription
Reorder windows, dietary and allergy-aware catalogs, substitution policies the agent must respect, and subscription mutations (skip, swap, cancel) that go through a single signed channel.
Regulated specialty
Alcohol, firearms, vape, supplements: age verification, state-by-state shipping rules, federal restricted categories and an audit trail your compliance team can defend before a regulator.
Marketplaces & aggregators
Seller scorecards, schema enforcement, agent-aware ranking, anti-fraud across storefronts, and commission-aware order intents that respect existing marketplace economics.
USA Agentic Commerce Development Company
We're a US-headquartered agentic commerce development company focused on shipping production-grade protocol surfaces for US merchants, marketplaces and B2B platforms. Based in Miami, Florida, our team combines deep AI engineering with hands-on commerce platform experience to build systems that measurably increase agent-attributed revenue, drop cycle time on procurement orders and survive both a regulator and a Black Friday spike.
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Agentic Commerce Development
Frequently Asked Questions
It is the engineering work that makes a US store reachable, understandable and transactable by AI shopping agents acting on behalf of a human buyer. We focus on the protocol layer: clean catalogs, Schema.org structured data, agent-friendly read APIs, a signed checkout endpoint with price guarantee, evaluations and a governance layer that finance, legal and compliance can defend.
Regular e-commerce optimizes for a human browsing a website. AI e-commerce typically adds recommendations or chat assistants on top of that human-facing site. Agentic commerce optimizes for software agents that do the browsing, comparison and checkout themselves, often without a UI. The metrics, integrations and trust requirements are different, and the work touches catalog data, identity, payments and observability rather than CSS and copy.
A focused project build typically lands between USD 45,000 and USD 180,000. A dedicated squad runs USD 22,000 to 60,000 per month. Senior engineers under staff augmentation are between USD 65 and 110 per hour. Final pricing depends on platform, regulated checkouts (alcohol, firearms, pharmacy) and number of integrations.
A first measurable milestone — usually a clean product feed and a discoverable agent capability map — ships in three to four weeks. A complete rollout that includes a signed checkout API, evaluation suite and human-in-the-loop dashboard is typically 8 to 14 weeks. Multi-brand merchants and PCI-heavy payment flows take longer.
Yes. We have shipped on Shopify and Shopify Plus, BigCommerce, Adobe Commerce (Magento 2), Salesforce Commerce Cloud and headless stacks built with Next.js, Remix, Astro and Hydrogen. The protocol layer (structured data, signed checkout, MCP, evaluations) is platform-agnostic.
The signed checkout endpoint is the trust boundary. Order intents are time-bound and tied to a price-guarantee window, agent identity and consent are verified, and high-risk actions go to a human-on-the-loop with a written audit trail. Refunds and chargebacks reuse the same signed channel so finance and compliance both get a clean reconciliation story aligned with PCI-DSS.
Done well, no. Cleaner Schema.org structured data, faster product feeds and crisper attribute coverage also help conventional Google ranking and Google Shopping placement. The risk shows up only when teams add agent endpoints in isolation, so we plan canonicals, sitemaps and consent for both audiences in the same release train.
We design every checkout, identity and consent surface against PCI-DSS for payments, FTC guidance on autonomous transactions, CCPA/CPRA for California shoppers, and HIPAA when the merchant operates in pharmacy or wellness. Audit logs, signed receipts, age verification and state-by-state shipping rules are first-class concerns, not afterthoughts. SOC 2-aligned processes are baked into delivery from sprint one.
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