// ── 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, target tasks, domain requirements, language needs, and reviewer expertise. Most programs are delivered through Argos Myriad — the Argos Data Platform — with SmartSuite providing the customizable task environment, embedded QA controls, and secure expert workflows. For clients who prefer to operate inside their own platforms or through structured file exchange, programs are configured to integrate accordingly.

; // ── 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 = [ "Creating instruction-response datasets for supervised fine-tuning (SFT)", "Developing prompt-response pairs, demonstrations, rewrites, and model-preferred examples", "Building domain-specific training data for enterprise workflows and specialized use cases", "Producing multilingual and locale-specific training data for global AI systems", "Generating preference datasets that feed RLHF, DPO, and human preference modeling workflows", "Validating training data for quality, consistency, safety, relevance, and downstream usability", ]; return (
Use cases

Where LLM Training Data Services 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: "llm-pre-training", parent: "llm-training-data", title: "LLM Pre-Training", desc: "Domain-specific pre-training data services for building stronger, more relevant LLM foundations", icon: "stack", bg: "#0E1F3E", fg: "#EEE36B" }, // navy + yellow { id: "02", slug: "reinforcement-learning-from-human-feedback-rlhf", parent: "llm-training-data", title: "Reinforcement Learning from Human Feedback (RLHF)", desc: "End-to-end RLHF training datasets built from expert human feedback for model alignment and optimization", icon: "arcs", bg: "#30AA74", fg: "#FFFFFF" }, // green + white { id: "03", title: "Low-Resource\nLanguage Data", desc: "Domain-specific low-resource data for model performance.", icon: "half-arcs", bg: "#EEE36B", fg: "#0E1F3E" }, // yellow + navy { id: "04", title: "Speech & Audio\nData Collection", desc: "Human voice and audio data that reflects real-world speakers, environments, and use cases.", icon: "speech", bg: "#002042", fg: "#30AA74" }, // navy-deep + green { id: "05", title: "Multimodal\nData Collection", desc: "Controlled multimodal data collection for secure, consistent, model-ready AI pipelines.", icon: "layers", bg: "#000000", fg: "#EEE36B" }, // black + yellow { id: "06", title: "Synthetic\nData 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: "llm-pre-training", parent: "llm-training-data", title: "LLM Pre-Training", desc: "Domain-specific pre-training data services for building stronger, more relevant LLM foundations", icon: "07", color: "#0E1F3E" }, { id: "02", slug: "reinforcement-learning-from-human-feedback-rlhf", parent: "llm-training-data", title: "Reinforcement Learning from Human Feedback (RLHF)", desc: "End-to-end RLHF training datasets built from expert human feedback for model alignment and optimization", icon: "09", color: "#0E1F3E" }, { id: "03", 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: "06", 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: "LLM performance depends on the quality, relevance, and consistency of the data used to shape model behavior. Generic or poorly governed training data introduces noisy signals, weak domain adaptation, hallucination risk, and unreliable outputs in production." }, { id: "Our approach", body: "Argos Data combines multilingual depth, domain specialists, and structured operational governance to deliver training data that holds up under enterprise review. We define task criteria, data formats, reviewer qualifications, and validation rules before production begins. Datasets are consistent, auditable, and ready for downstream model development." }, { id: "Why it matters", body: "For enterprise AI teams, this makes training data a measurable input into model performance — connecting reviewer expertise and quality controls directly to instruction following, domain accuracy, and multilingual reliability in production." } ]; return (
Why Argos

Training data, treated as an engineered AI 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

High-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement.

LLM Training Data Services give enterprise AI teams high-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement. The result is stronger instruction following, better domain performance, improved multilingual reliability, reduced model error, and more dependable LLM behavior in production environments.

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