);
}
/* ============================================
Case study architecture diagrams (3 distinct)
============================================ */
function CaseArchitecture({ kind }) {
if (kind === 'mustari') {
return (
);
}
if (kind === 'neft') {
return (
);
}
if (kind === 'sigorta') {
return (
);
}
return null;
}
function WorkPageBody() {
const cases = [
{
sector: 'BANKING', deployment: 'Private cloud', region: 'Azerbaijan', kind: 'passport', archKind: 'mustari', hue: 215,
headline: <>From five days to ninety seconds.,
quote: 'The compliance team stopped being a bottleneck and started being strategic.',
workflow: 'Retail account opening: applicants submit a passport, a utility bill, and salary statements via the bank\'s mobile app or branch tablets. The compliance reviewer must run identity verification, sanctions screening across four lists, and income validation — historically a 3-to-5-day manual cycle ending in a binary approve/reject.',
built: 'Müştəri ingests the document set in Azerbaijani, Russian, or English. Multilingual OCR + named-entity extraction emits structured fields; a liveness check runs in-VPC; sanctions screening hits OFAC SDN, EU Consolidated, UN, and the AZ MFA watchlist; a gradient-boosted risk model produces a calibrated score, and an LLM-driven explainer writes the audit-grade dossier — all signed and persisted to WORM storage.',
evaluation: 'Eval set: 24,000 labeled historical applications stratified by branch, applicant cohort, and document quality. Edge buckets explicitly tested: torn pages, low-light selfies, fuzzy-match sanctions hits, and Russian-only utility bills. We track p95 latency and false-clear rate weekly; every release runs the full regression before deployment.',
metrics: [['90s', 'p95 processing time'], ['12k', 'Apps/day capacity'], ['94%', 'Compliance accuracy']],
stack: ['Multilingual OCR', 'Liveness ONNX', 'Sanctions API', 'Azure private cloud', 'Postgres', 'Customer-managed keys'],
},
{
sector: 'OIL & GAS', deployment: 'On-premise', region: 'Azerbaijan', kind: 'geology', archKind: 'neft', hue: 210,
headline: <>Forty years of seismic data, queryable in seconds.,
quote: 'What used to take three geologists a week now takes one prompt.',
workflow: 'Exploration geologists answer questions like "show all wells in the Bakı-Şamaxı block with H₂S readings above 200 ppm" by manually opening scanned reports — 40 years of seismic surveys, well logs, and geological assessments split across Russian and Azerbaijani PDFs. The answer typically takes a senior geologist 2-3 days of archive work and is rarely fully complete.',
built: 'Neft-AI is a hybrid-search RAG agent built on the client\'s on-premise infrastructure. Multilingual embeddings + BM25 over 218k documents, with custom PDF parsing for pre-2010 scans. Cross-encoder reranks the top-50 down to top-14; a refusal-aware LLM answers with page-anchored citations back to the original PDF.',
evaluation: 'We built a 320-query benchmark, expert-graded by senior geologists across four sub-domains: well logs, seismic interpretation, production decline, and HSE history. Pass criterion: relevance + factual grounding + correct citation. New model versions ship only after matching or exceeding the previous version on every sub-domain.',
metrics: [['2.6s', 'p95 query latency'], ['40yr', 'Corpus indexed'], ['218k', 'Documents in scope']],
stack: ['Multilingual embeddings', 'BM25 + vector', 'Custom PDF parsing', 'pgvector on-prem', 'Cross-encoder rerank', 'CMK encryption'],
},
{
sector: 'INSURANCE', deployment: 'Private cloud', region: 'Azerbaijan', kind: 'docs', archKind: 'sigorta', hue: 220,
headline: <>Triage in minutes. Fraud signals nobody saw before.,
quote: 'We started catching patterns the manual process had been missing for years.',
workflow: 'Life insurance claims arrive as scanned hospital records and handwritten Azerbaijani forms. The claims team manually extracts policy numbers, beneficiary details, and medical cause-of-event, then cross-references each claim against the policy contract — work that historically took weeks of triage and routinely missed fraud signals across providers.',
built: 'Sığorta extracts structured claim data with a custom Azerbaijani-medical NER trained on a domain corpus. A policy-contract RAG agent verifies coverage against the underlying contract clauses; a graph fraud detector scores claimant/provider relationships against an 8-year history. A triage router emits one of three queues — fast-track, standard, or investigation — with full reasoning preserved in the audit log.',
evaluation: 'We held out 4,200 historical claims, stratified by sub-line of business and severity, then back-tested the triage router against the human reviewers\' final decisions. We measure decision concordance, time-to-route, and fraud-signal recall against an investigator-validated subset. The graph-fraud component is re-evaluated every quarter as the provider graph evolves.',
metrics: [['76%', 'Faster triage'], ['99.2%', 'Extraction accuracy'], ['Top-3', 'Az. insurer deployed']],
stack: ['Custom NER (Az medical)', 'RAG over policies', 'Graph fraud detection', 'Private cloud · CMK', 'WORM audit log'],
},
];
return (
<>
Three deployments. Three industries.}
lead="What it actually takes to get an AI agent past pilot and into a regulated production environment."
/>
{cases.map((c, i) => )}
);
}
Object.assign(window, { CaseStudy, WorkPageBody });