// ── 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

Designed around your model objective.

Each program is designed around the model objective, task type, domain requirements, annotation guidelines, and reviewer expertise. Argos Data's preferred delivery environment is Argos Myriad — the Argos Data Platform — where SmartSuite, our customizable tooling environment, supports custom workflow configuration, embedded QA controls, scalable task distribution, and secure access management. Where clients require work to be done inside their own platforms or through more manual file exchanges, Argos Data adapts the operating model to the program.

; // ── 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 = [ "Natural Language Processing (NLP) Annotation for text classification, named entity recognition, sentiment, intent, factuality, toxicity, and safety labeling", "Speech & Audio Annotation for transcription, speaker labeling, intent classification, accent and dialect review, and audio metadata", "Multimodal Annotation for datasets that combine text, image, audio, video, and contextual inputs", "Human Preference Modeling for the systematic discipline of capturing structured human preference signals", "Reinforcement Learning from Human Feedback (RLHF) & Direct Preference Optimization (DPO) for the operational workflows that produce preference and feedback signals", "Supervised Fine-Tuning (SFT) for instruction-response datasets, demonstrations, and domain-specific training examples", "Retrieval-Augmented Generation (RAG) data preparation for cleaning, structuring, and validating enterprise knowledge assets", "Active Learning & Intelligent Annotation for targeted review workflows that prioritize the highest-value data for model improvement", ]; return (
Use cases

Where Data Annotation and Human Feedback 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: "natural-language-processing-nlp-annotation", parent: "data-annotation", title: "Natural Language Processing (NLP) Annotation", desc: "Human-in-the-loop text annotation that helps AI systems understand language, intent, meaning, and context", icon: "stack", bg: "#0E1F3E", fg: "#EEE36B" }, // navy + yellow { id: "02", slug: "speech-and-audio-annotation", parent: "data-annotation", title: "Speech & Audio Annotation", desc: "Human-in-the-loop audio labeling that improves speech recognition, voice AI, and audio intelligence systems", icon: "arcs", bg: "#30AA74", fg: "#FFFFFF" }, // green + white { id: "03", slug: "multimodal-annotation", parent: "data-annotation", title: "Multimodal Annotation", desc: "Structured multimodal labeling for training and evaluating AI systems across text, image, audio, and video", icon: "half-arcs", bg: "#EEE36B", fg: "#0E1F3E" }, // yellow + navy { id: "04", slug: "human-preference-modeling", parent: "data-annotation", title: "Human Preference Modeling", desc: "The systematic methodology for capturing structured human preference signals at scale", icon: "speech", bg: "#002042", fg: "#30AA74" }, // navy-deep + green { id: "05", slug: "reinforcement-learning-from-human-feedback-rlhf-and-direct-preference-optimization-dpo", parent: "data-annotation", title: "Reinforcement Learning from Human Feedback (RLHF) & Direct Preference Optimization (DPO)", desc: "The operational workflows that produce reviewer-calibrated preference and feedback signals at scale", icon: "layers", bg: "#000000", fg: "#EEE36B" }, // black + yellow { id: "06", slug: "supervised-fine-tuning-sft", parent: "data-annotation", title: "Supervised Fine-Tuning (SFT)", desc: "Instruction-tuned datasets for task-specific model adaptation and production AI performance", icon: "orbit", bg: "#3CA276", fg: "#0E1F3E" }, { id: "07", slug: "retrieval-augmented-generation-rag", parent: "data-annotation", title: "Retrieval-Augmented Generation (RAG)", desc: "Human-in-the-loop data preparation for retrieval-augmented LLM workflows", icon: "orbit", bg: "#3CA276", fg: "#0E1F3E" }, { id: "08", slug: "active-learning-and-intelligent-annotation", parent: "data-annotation", title: "Active Learning & Intelligent Annotation", desc: "Human-in-the-loop annotation workflows for continuous model refinement and higher-value review", 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: "natural-language-processing-nlp-annotation", parent: "data-annotation", title: "Natural Language Processing (NLP) Annotation", desc: "Human-in-the-loop text annotation that helps AI systems understand language, intent, meaning, and context", icon: "19", color: "#0E1F3E" }, { id: "02", slug: "speech-and-audio-annotation", parent: "data-annotation", title: "Speech & Audio Annotation", desc: "Human-in-the-loop audio labeling that improves speech recognition, voice AI, and audio intelligence systems", icon: "02", color: "#0E1F3E" }, { id: "03", slug: "multimodal-annotation", parent: "data-annotation", title: "Multimodal Annotation", desc: "Structured multimodal labeling for training and evaluating AI systems across text, image, audio, and video", icon: "23", color: "#0E1F3E" }, { id: "04", slug: "human-preference-modeling", parent: "data-annotation", title: "Human Preference Modeling", desc: "The systematic methodology for capturing structured human preference signals at scale", icon: "16", color: "#0E1F3E" }, { id: "05", slug: "reinforcement-learning-from-human-feedback-rlhf-and-direct-preference-optimization-dpo", parent: "data-annotation", title: "Reinforcement Learning from Human Feedback (RLHF) & Direct Preference Optimization (DPO)", desc: "The operational workflows that produce reviewer-calibrated preference and feedback signals at scale", icon: "08", color: "#0E1F3E" }, { id: "06", slug: "supervised-fine-tuning-sft", parent: "data-annotation", title: "Supervised Fine-Tuning (SFT)", desc: "Instruction-tuned datasets for task-specific model adaptation and production AI performance", icon: "01", color: "#0E1F3E" }, { id: "07", slug: "retrieval-augmented-generation-rag", parent: "data-annotation", title: "Retrieval-Augmented Generation (RAG)", desc: "Human-in-the-loop data preparation for retrieval-augmented LLM workflows", icon: "14", color: "#0E1F3E" }, { id: "08", slug: "active-learning-and-intelligent-annotation", parent: "data-annotation", title: "Active Learning & Intelligent Annotation", desc: "Human-in-the-loop annotation workflows for continuous model refinement and higher-value review", icon: "14", 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 need more than labeled data. They need consistent, context-aware human feedback that reflects the task, user intent, domain, language, safety expectations, and quality standards the model must meet in production." }, { id: "Our approach", body: "Argos Data combines multilingual depth, domain specialists, and structured quality governance across annotation and feedback programs. We define annotation schemas, grading rubrics, reviewer qualifications, calibration processes, validation rules, and QA checkpoints before production begins. SmartSuite supports custom workflow configuration and embedded quality controls, while Argos Myriad enables large-scale annotation management, task distribution, audit trails, role-based access, and real-time visibility across complex programs." }, { id: "Why it matters", body: "For enterprise AI teams, this turns annotation and feedback into an engineered operation rather than a manual labeling task — connecting reviewer judgment directly to model accuracy, alignment, and reliability in production." } ]; return (
Why Argos

Annotation and feedback, treated as engineered AI operations.

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

{v.body}

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

High-quality labels and human feedback signals that improve model accuracy, alignment, and reliability.

Data Annotation & Human Feedback gives enterprise AI teams high-quality labeled datasets and structured human feedback signals that improve model accuracy, alignment, safety, and reliability. The result is stronger model performance, more consistent outputs, reduced errors, and a production-ready 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 };