// ── HERO ────────────────────────────────────────────────────────── // Same dark-green motion-blur treatment as Home/Contact heroes. // Headline = approved Short description from SERVICE-DESCRIPTIONS.md. // Sub-paragraph = first paragraph of the approved Expanded description. const SolutionHero = ({ onCTA }) =>
; // ── OVERVIEW ─────────────────────────────────────────────────────── // Mirrors the Homepage "30+ years of experience" two-column rhythm: // eyebrow + hairline rule, then a 1.15fr / 1fr split with H2 on the // left and a body paragraph on the right. // Copy = approved 2nd paragraph of the Expanded description. const Overview = () =>
Overview

A human data operations partner for enterprise AI.

Argos Data designs collection programs around clear data specifications, contributor criteria, consent standards, secure workflows, and quality controls. Most programs run inside Argos Myriad — the Argos Data Platform — where SmartSuite, our customizable tooling environment, enables embedded QA controls, scalable workforce deployment, and secure integration with client systems. Where clients prefer to operate inside their own platforms or use offline file exchanges, Argos Data adapts to the deployment model the program requires.

; // ── USE CASES ────────────────────────────────────────────────────── // 6 bullets, verbatim from the approved "Use cases" list in // SERVICE-DESCRIPTIONS.md under AI Data Collection. Each renders as // a numbered row inside a single bordered column block — light, calm // rhythm consistent with the rest of the site's hairlined dividers. const UseCases = () => { const items = [ "Custom Data Collection for model-specific dataset requirements", "Multilingual Data Sourcing for language-specific and locale-aware AI systems", "Low-Resource Language Data for hard-to-source languages, dialects, and regional variants", "Speech & Audio Data Collection for ASR, voice, conversational AI, and audio intelligence systems", "Multimodal Data Collection for models using combinations of text, image, audio, and video", "Synthetic Data Generation for controlled dataset expansion, edge-case coverage, and scenario-based model training", ]; return (
Use cases

Where AI Data Collection is applied.

{items.map((it, i) =>
{String(i + 1).padStart(2, "0")}
{it}
)}
); }; // ── SUB-SERVICES GRID ────────────────────────────────────────────── // Reuses the Homepage Solutions card pattern verbatim: numbered card, // rounded image tile, title, approved short description, pill CTA, // on the same cream/yellow radial wash. 6 cards = the 6 sub-services // documented under AI Data Collection in SERVICE-DESCRIPTIONS.md. // // Image assignments: where a Homepage card uses an approved photo // for the same topic, we reuse it. Where the doc names no thumbnail, // we pick from the approved photography pool (no new imagery // invented). If no approved photo clearly matches, a literal // "Lorem ipsum…" placeholder tile is shown so missing imagery is // visible to the manager — per project rules. // SvcIcon is loaded globally from components/svc-icons.jsx // (28 icons from the supplied Argos iconset, addressable by name "01"–"28"). const SubServices = () => { const services = [ { id: "01", slug: "custom-data-collection", parent: "ai-data-collection", title: "Custom Data Collection", desc: "Purpose-built multimodal datasets aligned to specific AI model objectives", icon: "stack", bg: "#0E1F3E", fg: "#EEE36B" }, // navy + yellow { id: "02", slug: "multilingual-data-sourcing", parent: "ai-data-collection", title: "Multilingual Data Sourcing", desc: "Language-specific datasets aligned to model objectives, domains, and real-world use cases", icon: "arcs", bg: "#30AA74", fg: "#FFFFFF" }, // green + white { id: "03", slug: "low-resource-language-data", parent: "ai-data-collection", title: "Low-Resource Language Data", desc: "Targeted sourcing for languages, dialects, and regional variants underrepresented in mainstream AI training data", icon: "half-arcs", bg: "#EEE36B", fg: "#0E1F3E" }, // yellow + navy { id: "04", slug: "speech-and-audio-data-collection", parent: "ai-data-collection", title: "Speech & Audio Data Collection", desc: "Voice and audio datasets that reflect real-world speakers, environments, and use cases", icon: "speech", bg: "#002042", fg: "#30AA74" }, // navy-deep + green { id: "05", slug: "multimodal-data-collection", parent: "ai-data-collection", title: "Multimodal Data Collection", desc: "Secure, model-ready datasets across text, image, audio, and video for multimodal AI systems", icon: "layers", bg: "#000000", fg: "#EEE36B" }, // black + yellow { id: "06", slug: "synthetic-data-generation", parent: "ai-data-collection", title: "Synthetic Data Generation", desc: "Human-validated synthetic datasets for edge cases, rare scenarios, and controlled model coverage", icon: "orbit", bg: "#3CA276", fg: "#0E1F3E" }]; ; // green-soft + navy // Shared "Learn more" text link — sits at the bottom of every card. const LearnMoreLink = ({ href = "#" }) => Learn more ; // Shared icon plate — full-height navy column on the LEFT of every // card. Icon sits near the top, number anchored top-left in cream. // Width scales between full and half cards. const IconPlate = ({ s, width }) =>
{s.id}
; // ── FULL CARD — horizontal, wide navy column + content ────────── const FullCard = ({ s }) =>
{s.title}

{s.desc}

; // ── HALF CARD — horizontal, navy column left, content right ───── // Two halves stacked vertically = same overall height as one full card. const HalfCard = ({ s }) =>
{s.title.replace(/\n/g, " ")}

{s.desc}

; return (
); }; // ── SUB-SERVICES — variant B ─────────────────────────────────────── // Alternate card design for the same six services. No icon plate // background; the icon sits top-left of the card in the service's // brand color. The number moves to the bottom-left and pairs with // a "Learn more" affordance on the right that alternates between // the text-link and the older pill button, every other card. const SubServicesAlt = () => { const services = [ { id: "01", slug: "custom-data-collection", parent: "ai-data-collection", title: "Custom Data Collection", desc: "Purpose-built multimodal datasets aligned to specific AI model objectives", icon: "12", color: "#0E1F3E" }, { id: "02", slug: "multilingual-data-sourcing", parent: "ai-data-collection", title: "Multilingual Data Sourcing", desc: "Language-specific datasets aligned to model objectives, domains, and real-world use cases", icon: "25", color: "#0E1F3E" }, { id: "03", slug: "low-resource-language-data", parent: "ai-data-collection", title: "Low-Resource Language Data", desc: "Targeted sourcing for languages, dialects, and regional variants underrepresented in mainstream AI training data", icon: "22", color: "#0E1F3E" }, { id: "04", slug: "speech-and-audio-data-collection", parent: "ai-data-collection", title: "Speech & Audio Data Collection", desc: "Voice and audio datasets that reflect real-world speakers, environments, and use cases", icon: "21", color: "#0E1F3E" }, { id: "05", slug: "multimodal-data-collection", parent: "ai-data-collection", title: "Multimodal Data Collection", desc: "Secure, model-ready datasets across text, image, audio, and video for multimodal AI systems", icon: "13", color: "#0E1F3E" }, { id: "06", slug: "synthetic-data-generation", parent: "ai-data-collection", title: "Synthetic Data Generation", desc: "Human-validated synthetic datasets for edge cases, rare scenarios, and controlled model coverage", icon: "03", color: "#0E1F3E" }]; ; const Card = ({ s, idx }) => { return (
{s.title}

{s.desc}

Learn more
{s.id}
); }; return (
); }; // ── WHY ARGOS / DIFFERENTIATOR ───────────────────────────────────── // Dark navy section, mirrors the Homepage "What we stand for" rhythm. // Three column blocks — content = the three approved paragraphs of // the "Argos differentiator" section, lightly framed with derived // 2-word headings so the layout has structure. (Paragraph bodies // remain verbatim from SERVICE-DESCRIPTIONS.md.) const WhyArgos = () => { const blocks = [ { id: "The risk", body: "AI systems are only as reliable as the data they are built on. Generic, incomplete, or poorly matched datasets limit model accuracy, introduce bias, weaken multilingual performance, and create downstream rework across annotation, fine-tuning, and evaluation." }, { id: "Our approach", body: "Argos Data treats collection as an engineered AI data operation. We define target data requirements, contributor profiles, domain criteria, locale needs, validation rules, and QA checkpoints before collection begins, drawing on three decades of multilingual experience and a vetted global network of 80K+ contributors. Programs are designed around the model's intended use, target users, and operating conditions rather than treating collection as a volume exercise." }, { id: "Why it matters", body: "For enterprise AI teams, this turns data collection into a controlled function of model development. The result is cleaner inputs, stronger relevance to production conditions, and a more reliable foundation for scalable AI programs." } ]; return (
Why Argos

Collection, treated as an engineered data operation.

{blocks.map((v, i) =>
{v.id}

{v.body}

)}
); }; // ── OUTCOME ──────────────────────────────────────────────────────── // Light section, eyebrow + headline + verbatim Outcome paragraph // from SERVICE-DESCRIPTIONS.md. const Outcome = () =>
Outcome

Representative, high-quality datasets aligned to model goals and production use cases.

AI Data Collection gives enterprise AI teams representative, high-quality datasets aligned to model goals and production use cases. The result is cleaner input data, stronger model relevance, improved multilingual and multimodal performance, and a more reliable foundation for scalable AI development.

; // Publish the six section components for the shared svc-template.jsx App. window.SVC_SECTIONS = { SolutionHero, Overview, UseCases, SubServicesAlt, WhyArgos, Outcome };