RAG Development Services

<style> :root{--primary:#6061f6;--accent:#c5ef48;--dark:#111827;--body:#4b5563;--muted:#6b7280;--bg-light:#f8f9fa;--bg-white:#ffffff;--bg-tint:#fafbff;--border:rgba(0,0,0,0.06);--shadow-sm:0 4px 24px rgba(0,0,0,0.05);--shadow-md:0 12px 40px rgba(96,97,246,0.10);--shadow-lg:0 20px 60px rgba(96,97,246,0.14);--radius-card:20px;--radius-sm:12px;--radius-pill:999px} *{font-family:'Inter',sans-serif} .lca-h2{font-size:clamp(1.5rem,3vw,2.25rem);font-weight:400;color:var(--dark);margin:0 0 1rem 0;letter-spacing:-0.02em;line-height:1.2} .lca-h2 strong{font-weight:700;color:var(--primary)} .lca-h3{font-size:clamp(0.95rem,1.8vw,1.1rem);font-weight:600;color:var(--dark);margin:0 0 0.5rem 0;line-height:1.3} .lca-body{font-size:clamp(0.875rem,1.4vw,0.975rem);color:var(--body);line-height:1.7;margin:0} .lca-split{display:grid;grid-template-columns:1fr 2fr;gap:4rem;align-items:start} .lca-bento{display:grid;grid-template-columns:1fr 2fr;gap:3rem;align-items:start} .lca-bento-heading{position:sticky;top:2rem} .lca-grid-2{display:grid;grid-template-columns:repeat(2,1fr);gap:24px} .lca-grid-3{display:grid;grid-template-columns:repeat(3,1fr);gap:24px} .lca-card{background:var(--bg-white);border-radius:var(--radius-card);border:1px solid var(--border);box-shadow:var(--shadow-sm);padding:28px 24px;position:relative;overflow:hidden;transition:background 0.25s ease,box-shadow 0.25s ease} .lca-card::before{content:'';position:absolute;left:0;top:0;width:3px;height:0;background:var(--primary);border-radius:20px 0 0 20px;transition:height 0.25s ease} .lca-card:hover::before{height:100%} .lca-card:hover{background:var(--bg-tint);box-shadow:var(--shadow-md)} .lca-icon-wrap{width:48px;height:48px;border-radius:14px;background:rgba(96,97,246,0.08);display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-bottom:16px} .lca-icon-wrap svg{width:24px;height:24px;color:var(--primary)} .lca-callout{background:var(--bg-light);border-left:3px solid var(--primary);border-radius:0 var(--radius-sm) var(--radius-sm) 0;padding:24px 28px} .lca-callout-dark{background:var(--dark);border-radius:var(--radius-card);padding:28px 24px;color:#fff} .lca-callout-dark .lca-body{color:rgba(255,255,255,0.7)} .lca-callout-dark .lca-h3{color:#fff} .lca-pill{display:inline-block;font-size:0.75rem;font-weight:600;padding:4px 12px;border-radius:var(--radius-pill);text-transform:uppercase;letter-spacing:0.05em} .lca-pill-green{background:rgba(197,239,72,0.15);color:#4d7c0f} .lca-step-num{display:inline-flex;align-items:center;justify-content:center;width:36px;height:36px;border-radius:50%;background:rgba(96,97,246,0.08);color:var(--primary);font-weight:700;font-size:0.9rem;flex-shrink:0} @media(max-width:991px){.lca-grid-3{grid-template-columns:repeat(2,1fr)}} @media(max-width:767px){.lca-split,.lca-bento{grid-template-columns:1fr;gap:2rem}.lca-bento-heading{position:static}.lca-grid-2,.lca-grid-3{grid-template-columns:1fr}} </style> <div class='section_why-rag'> <div class='padding-global padding-section-large'> <div class='container-large'> <div class='lca-split'> <div> <h2 class='lca-h2'>RAG bridges the gap between LLM knowledge and your <strong>data.</strong></h2> <p class='lca-body' style='margin-top:1rem'>Out-of-the-box LLMs don't know your business. RAG retrieves relevant information from your private data sources at query time, enabling AI to answer accurately from your documents.</p> </div> <div class='lca-callout'> <p class='lca-body'>This is the architecture behind every enterprise AI product that actually works — not just generates plausible text, but provides correct, grounded, citable answers from your proprietary information.</p> </div> </div> </div> </div> </div> <div class='section_when-rag' style='background:var(--bg-light)'> <div class='padding-global padding-section-large'> <div class='container-large'> <div class='lca-bento'> <div class='lca-bento-heading'> <h2 class='lca-h2'>When we choose <strong>RAG.</strong></h2> <p class='lca-body' style='margin-top:1rem'>The scenarios where retrieval-augmented generation is the right architecture.</p> </div> <div class='lca-grid-2'> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M19.5 14.25v-2.625a3.375 3.375 0 00-3.375-3.375h-1.5A1.125 1.125 0 0113.5 7.125v-1.5a3.375 3.375 0 00-3.375-3.375H8.25m0 12.75h7.5m-7.5 3h12'/></svg></div><h3 class='lca-h3'>AI needs to answer from internal documents</h3><p class='lca-body'>Policies, procedures, documentation, records. If the answer lives in your files and you want AI to find it, RAG is the architecture.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M9 12.75L11.25 15 15 9.75m-3-7.036A11.959 11.959 0 013.598 6 11.99 11.99 0 003 9.749c0 5.592 3.824 10.29 9 11.623 5.176-1.332 9-6.03 9-11.622 0-1.31-.21-2.571-.598-3.751h-.152c-3.196 0-6.1-1.248-8.25-3.285z'/></svg></div><h3 class='lca-h3'>Accuracy and citation matter</h3><p class='lca-body'>Some use cases can tolerate AI approximation. Others cannot. RAG systems can cite sources, quote directly, and ground every answer in retrievable evidence.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M12 6v6h4.5m4.5 0a9 9 0 11-18 0 9 9 0 0118 0z'/></svg></div><h3 class='lca-h3'>Knowledge base changes frequently</h3><p class='lca-body'>LLM training is static. RAG retrieval is dynamic. When your information updates regularly, RAG keeps answers current without retraining models.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M12 9v3.75m9-.75a9 9 0 11-18 0 9 9 0 0118 0zm-9 3.75h.008v.008H12v-.008z'/></svg></div><h3 class='lca-h3'>Hallucination risk must be minimized</h3><p class='lca-body'>Legal, medical, financial, compliance domains cannot tolerate fabricated answers. RAG constrains AI to answer from provided context, dramatically reducing hallucination.</p></div> </div> </div> </div> </div> </div> <div class='section_what-rag'> <div class='padding-global padding-section-large'> <div class='container-large'> <h2 class='lca-h2' style='text-align:center;margin-bottom:0.5rem'>What we build with <strong>RAG.</strong></h2> <p class='lca-body' style='text-align:center;max-width:600px;margin:0 auto 3rem'>From document Q&A to enterprise knowledge bases — RAG systems grounded in your data.</p> <div class='lca-grid-3'> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M19.5 14.25v-2.625a3.375 3.375 0 00-3.375-3.375h-1.5A1.125 1.125 0 0113.5 7.125v-1.5a3.375 3.375 0 00-3.375-3.375H8.25m0 12.75h7.5m-7.5 3h12'/></svg></div><h3 class='lca-h3'>Document Q&A Systems</h3><p class='lca-body'>Natural language interface to your documents. Ask questions, get answers with source citations. Manuals, policies, research, reports become searchable and queryable.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M20.25 6.375c0 2.278-3.694 4.125-8.25 4.125S3.75 8.653 3.75 6.375m16.5 0c0-2.278-3.694-4.125-8.25-4.125S3.75 4.097 3.75 6.375m16.5 0v5.25c0 2.278-3.694 4.125-8.25 4.125s-8.25-1.847-8.25-4.125v-5.25'/></svg></div><h3 class='lca-h3'>Internal Knowledge Bases</h3><p class='lca-body'>Company knowledge made accessible. Institutional knowledge captured in documents becomes instantly retrievable. New employees get answers in minutes instead of hunting through files.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M20.25 8.511c.884.284 1.5 1.128 1.5 2.097v4.286c0 1.136-.847 2.1-1.98 2.193-.34.027-.68.052-1.02.072v3.091l-3-3c-1.354 0-2.694-.055-4.02-.163a2.115 2.115 0 01-.825-.242m9.345-8.334a2.126 2.126 0 00-.476-.095 48.64 48.64 0 00-8.048 0c-1.131.094-1.976 1.057-1.976 2.192v4.286c0 .837.46 1.58 1.155 1.951m9.345-8.334V6.637c0-1.621-1.152-3.026-2.76-3.235A48.455 48.455 0 0011.25 3c-2.115 0-4.198.137-6.24.402-1.608.209-2.76 1.614-2.76 3.235v6.226c0 1.621 1.152 3.026 2.76 3.235.577.075 1.157.14 1.74.194V21l4.155-4.155'/></svg></div><h3 class='lca-h3'>RAG-Powered Customer Support</h3><p class='lca-body'>Support agents and chatbots that answer from your actual documentation. Product guides, troubleshooting procedures, policy documents grounded in your content.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M21 21l-5.197-5.197m0 0A7.5 7.5 0 105.196 5.196a7.5 7.5 0 0010.607 10.607z'/></svg></div><h3 class='lca-h3'>Enterprise Search + AI</h3><p class='lca-body'>Search that understands intent and synthesizes results. Not just finding documents, but answering questions by combining information across sources.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M17.25 6.75L22.5 12l-5.25 5.25m-10.5 0L1.5 12l5.25-5.25m7.5-3l-4.5 16.5'/></svg></div><h3 class='lca-h3'>Code Documentation Assistants</h3><p class='lca-body'>AI that answers questions about your codebase. READMEs, inline comments, architecture docs — developers get answers about internal systems without hunting through repos.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M9 12.75L11.25 15 15 9.75m-3-7.036A11.959 11.959 0 013.598 6 11.99 11.99 0 003 9.749c0 5.592 3.824 10.29 9 11.623 5.176-1.332 9-6.03 9-11.622 0-1.31-.21-2.571-.598-3.751h-.152c-3.196 0-6.1-1.248-8.25-3.285z'/></svg></div><h3 class='lca-h3'>Compliance & Legal Document Analysis</h3><p class='lca-body'>Queries against contracts, regulations, policies. Find relevant clauses, compare across documents, answer compliance questions with citations.</p></div> </div> </div> </div> </div> <div class='section_who-rag' style='background:var(--bg-light)'> <div class='padding-global padding-section-large'> <div class='container-large'> <div class='lca-bento'> <div class='lca-bento-heading'> <h2 class='lca-h2'>Who RAG development is <strong>for.</strong></h2> <p class='lca-body' style='margin-top:1rem'><span class='lca-pill lca-pill-green'>Ideal Fit</span></p> </div> <div> <div style='display:flex;align-items:flex-start;gap:1rem;margin-bottom:1.5rem'><span class='lca-step-num'>1</span><div><h3 class='lca-h3'>Knowledge-Heavy Organizations</h3><p class='lca-body'>Institutional knowledge trapped in documents — policies, procedures, research. That knowledge is hard to access, inconsistently applied, trapped in files only some people know how to find.</p></div></div> <div style='display:flex;align-items:flex-start;gap:1rem;margin-bottom:1.5rem'><span class='lca-step-num'>2</span><div><h3 class='lca-h3'>Customer Support Teams</h3><p class='lca-body'>You answer the same questions repeatedly. Answers exist in documentation but finding them takes time. RAG surfaces information from your support content accurately and instantly.</p></div></div> <div style='display:flex;align-items:flex-start;gap:1rem;margin-bottom:1.5rem'><span class='lca-step-num'>3</span><div><h3 class='lca-h3'>Professional Services Firms</h3><p class='lca-body'>Deliverables depend on accessing and synthesizing information from documents. Faster, more comprehensive retrieval directly improves output quality.</p></div></div> <div style='display:flex;align-items:flex-start;gap:1rem;margin-bottom:1.5rem'><span class='lca-step-num'>4</span><div><h3 class='lca-h3'>Regulated Industries</h3><p class='lca-body'>You need answers grounded in official documentation. AI that can cite sources is more valuable than AI that sounds confident. Compliance demands accuracy.</p></div></div> <div class='lca-callout-dark' style='margin-top:24px'><h3 class='lca-h3'>Not the right fit if</h3><p class='lca-body'>You need purely creative generation without factual grounding. Or your documents are entirely handwritten images without OCR preprocessing capability.</p></div> </div> </div> </div> </div> </div>

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We extract text and structure from most business document formats.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M2.25 18L9 11.25l4.306 4.307a11.95 11.95 0 015.814-5.519l2.74-1.22m0 0l-5.94-2.28m5.94 2.28l-2.28 5.941'/></svg></div><h3 class='lca-h3'>How do you handle large document collections?</h3><p class='lca-body'>Vector databases are designed for scale. We've built RAG over tens of thousands of documents. Performance depends on chunking strategy, embedding model, and index structure.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M9 12.75L11.25 15 15 9.75m-3-7.036A11.959 11.959 0 013.598 6 11.99 11.99 0 003 9.749c0 5.592 3.824 10.29 9 11.623 5.176-1.332 9-6.03 9-11.622 0-1.31-.21-2.571-.598-3.751h-.152c-3.196 0-6.1-1.248-8.25-3.285z'/></svg></div><h3 class='lca-h3'>How accurate are RAG systems?</h3><p class='lca-body'>RAG dramatically reduces hallucination compared to raw LLM queries. Accuracy depends on retrieval quality and generation quality. We measure and optimize both during development.</p></div> <div class='lca-card'><div class='lca-icon-wrap'><svg viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='1.5'><path stroke-linecap='round' stroke-linejoin='round' d='M12 6v6h4.5m4.5 0a9 9 0 11-18 0 9 9 0 0118 0z'/></svg></div><h3 class='lca-h3'>How do you keep RAG updated as documents change?</h3><p class='lca-body'>Automated sync pipelines detect changes and update embeddings. The system runs on schedules or triggers on document updates. We design update flows based on how frequently your content changes.</p></div> </div> </div> </div> </div> </div> <div class='section_process'> <div class='padding-global padding-section-large'> <div class='container-medium'> <h2 class='lca-h2' style='text-align:center;margin-bottom:0.5rem'>RAG development <strong>process.</strong></h2> <p class='lca-body' style='text-align:center;max-width:550px;margin:0 auto 3rem'>From document assessment to production deployment — building RAG systems that deliver accurate answers.</p> <div class='lca-steps'> <div class='lca-step'><div class='lca-step-timeline-num'>1</div><div class='lca-step-content'><h3 class='lca-h3'>Document Assessment</h3><p class='lca-body'>Inventory your data sources, assess document types and quality, identify preprocessing needs. Understanding your content determines retrieval architecture.</p><div class='lca-step-tags'><span>1-2 weeks</span></div></div></div> <div class='lca-step'><div class='lca-step-timeline-num'>2</div><div class='lca-step-content'><h3 class='lca-h3'>Chunking & Embedding Strategy</h3><p class='lca-body'>Documents split into retrievable chunks and converted to vector embeddings. Chunk size, overlap, and embedding model all affect retrieval quality. We test strategies against your actual queries.</p><div class='lca-step-tags'><span>1 week</span></div></div></div> <div class='lca-step'><div class='lca-step-timeline-num'>3</div><div class='lca-step-content'><h3 class='lca-h3'>Retrieval Architecture</h3><p class='lca-body'>Vector database selection, index configuration, hybrid search approaches, filtering and metadata handling. Architecture decisions balance accuracy, speed, and cost.</p><div class='lca-step-tags'><span>1-2 weeks</span></div></div></div> <div class='lca-step'><div class='lca-step-timeline-num'>4</div><div class='lca-step-content'><h3 class='lca-h3'>Generation Pipeline</h3><p class='lca-body'>Integration of retrieval with LLM generation. Prompt engineering, output formatting, citation generation, confidence scoring. The generation layer makes retrieval useful to users.</p><div class='lca-step-tags'><span>1-2 weeks</span></div></div></div> <div class='lca-step'><div class='lca-step-timeline-num'>5</div><div class='lca-step-content'><h3 class='lca-h3'>Testing & Optimization</h3><p class='lca-body'>Systematic testing with real queries. Measuring retrieval accuracy, answer quality, response time. Iterative optimization of chunking, prompts, and retrieval parameters.</p><div class='lca-step-tags'><span>1-2 weeks</span></div></div></div> <div class='lca-step'><div class='lca-step-timeline-num'>6</div><div class='lca-step-content'><h3 class='lca-h3'>Integration & Deployment</h3><p class='lca-body'>Building the RAG system into your application — API endpoints, user interfaces, admin tools. Deploying with monitoring for performance and quality over time.</p><div class='lca-step-tags'><span>1-2 weeks</span></div></div></div> </div> </div> </div> </div> <div class='section_tech-stack' style='background:var(--bg-light)'> <div class='padding-global padding-section-large'> <div class='container-large'> <h2 class='lca-h2' style='text-align:center;margin-bottom:0.5rem'>Vector database <strong>comparison.</strong></h2> <p class='lca-body' style='text-align:center;max-width:600px;margin:0 auto 2.5rem'>Choosing the right retrieval backend for your scale and deployment requirements.</p> <div class='lca-table-wrapper'> <table class='lca-comp-table'> <thead><tr><th>Database</th><th>Best For</th><th>Deployment</th></tr></thead> <tbody> <tr><td><strong>Pinecone</strong></td><td>Managed simplicity</td><td>Cloud</td></tr> <tr><td><strong>Weaviate</strong></td><td>Hybrid search</td><td>Cloud, self-hosted</td></tr> <tr><td><strong>Chroma</strong></td><td>Development, smaller scale</td><td>Self-hosted</td></tr> <tr><td><strong>Qdrant</strong></td><td>Performance at scale</td><td>Cloud, self-hosted</td></tr> <tr><td><strong>pgvector</strong></td><td>PostgreSQL integration</td><td>Self-hosted</td></tr> </tbody> </table> </div> </div> </div> </div> <div class='section_pricing'> <div class='padding-global padding-section-large'> <div class='container-large'> <h2 class='lca-h2' style='text-align:center;margin-bottom:0.5rem'>RAG <strong>investment ranges.</strong></h2> <p class='lca-body' style='text-align:center;max-width:600px;margin:0 auto 3rem'>From single document collection to enterprise-scale RAG platform.</p> <div class='lca-pricing-grid'> <div class='lca-price-card'><p class='lca-price-tier'>Simple RAG System</p><p class='lca-price-range'>$10K – $25K</p><p class='lca-price-timeline'>4-8 weeks</p><p class='lca-price-desc'>Single document collection with basic retrieval and generation pipeline.</p><ul class='lca-price-features'><li>Up to 1,000 documents</li><li>Basic retrieval and generation</li><li>Simple query interface</li><li>Standard hosting</li></ul></div> <div class='lca-price-card popular'><span class='lca-price-badge'>Most Common</span><p class='lca-price-tier'>Mid-Complexity RAG</p><p class='lca-price-range'>$25K – $60K</p><p class='lca-price-timeline'>8-14 weeks</p><p class='lca-price-desc'>Multiple document sources with advanced retrieval, citations, and admin tools.</p><ul class='lca-price-features'><li>Multiple document sources</li><li>Hybrid search with metadata filtering</li><li>UI with citations and source viewing</li><li>Admin tools for document management</li><li>Integration with existing systems</li></ul></div> <div class='lca-price-card'><p class='lca-price-tier'>Enterprise RAG Platform</p><p class='lca-price-range'>$60K – $150K+</p><p class='lca-price-timeline'>12-22 weeks</p><p class='lca-price-desc'>Large-scale document processing with enterprise security, multiple indexes, and custom integrations.</p><ul class='lca-price-features'><li>Large-scale document processing</li><li>Multiple specialized indexes</li><li>Enterprise security and access controls</li><li>Advanced analytics and quality monitoring</li><li>Ongoing optimization</li></ul></div> </div> </div> </div> </div> <script> (function(){var steps=document.querySelectorAll('.lca-steps .lca-step');if(!steps.length)return;var observer=new IntersectionObserver(function(entries){entries.forEach(function(entry){if(entry.isIntersecting){entry.target.classList.add('lca-visible');}});},{threshold:0.15});steps.forEach(function(step){observer.observe(step);});})(); </script>

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.lca-testimonial-metric-label{font-size:0.8rem;color:var(--muted)} .lca-faqs-grid{display:grid;grid-template-columns:1fr 2fr;gap:4rem;align-items:start} .lca-faq-list{display:flex;flex-direction:column} .lca-faq-item{border-bottom:1px solid #eaeaea} .lca-faq-trigger{display:flex;justify-content:space-between;align-items:center;padding:1.5rem 0;cursor:pointer;width:100%;background:none;border:none;text-align:left} .lca-faq-trigger:hover h3{color:var(--primary)} .lca-faq-trigger h3{font-size:1.05rem;font-weight:600;color:var(--dark);margin:0;padding-right:1.5rem;transition:color 0.2s;line-height:1.4} .lca-faq-arrow{width:24px;height:24px;flex-shrink:0;transition:transform 0.3s cubic-bezier(0.4,0,0.2,1);color:var(--primary)} .lca-faq-item[data-open='true'] .lca-faq-arrow{transform:rotate(180deg)} .lca-faq-collapse{overflow:hidden;height:0;transition:height 0.3s cubic-bezier(0.4,0,0.2,1)} .lca-faq-answer{padding:0 0 1.5rem 0} .lca-faq-answer p{font-size:0.975rem;color:var(--body);margin:0;line-height:1.7} @media(max-width:767px){.lca-testimonials-grid,.lca-faqs-grid{grid-template-columns:1fr;gap:2rem}} </style> <div class='section_case-studies' style='background:var(--bg-light)'> <div class='padding-global padding-section-large'> <div class='container-large'> <h2 class='lca-h2' style='margin-bottom:2.5rem'>LowCode Agency, in action with <strong>RAG.</strong></h2> <div class='lca-testimonials-grid'> <div class='lca-testimonial-card'><span class='lca-testimonial-tag'>Legal Education</span><h3 class='lca-testimonial-title'>BarEssay — Legal Knowledge RAG</h3><p class='lca-testimonial-desc'>RAG system indexing study materials and practice answers, enabling students to ask natural language questions with cited answers from their actual prep materials.</p><div class='lca-testimonial-metrics'><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>Instant</span><span class='lca-testimonial-metric-label'>answers from 1000s of pages</span></div><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>70%</span><span class='lca-testimonial-metric-label'>focus on weak areas</span></div></div></div> <div class='lca-testimonial-card'><span class='lca-testimonial-tag'>Nonprofit / RAG</span><h3 class='lca-testimonial-title'>CHIIP — Funding Documentation RAG</h3><p class='lca-testimonial-desc'>RAG system consolidating funder documentation across sources, enabling staff to ask questions about specific funders and receive accurate, cited answers.</p><div class='lca-testimonial-metrics'><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>Single</span><span class='lca-testimonial-metric-label'>interface for all funders</span></div><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>40%</span><span class='lca-testimonial-metric-label'>reduction in research time</span></div></div></div> <div class='lca-testimonial-card' style='grid-column:1/-1'><span class='lca-testimonial-tag'>Enterprise / Knowledge</span><h3 class='lca-testimonial-title'>Internal Knowledge Base — Client Project</h3><p class='lca-testimonial-desc'>RAG-powered knowledge base indexing internal documentation, policies, and process guides across multiple systems — making institutional knowledge instantly queryable by anyone in the organization.</p><div class='lca-testimonial-metrics'><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>Seconds</span><span class='lca-testimonial-metric-label'>to answer onboarding questions</span></div><div class='lca-testimonial-metric'><span class='lca-testimonial-metric-value'>Consistent</span><span class='lca-testimonial-metric-label'>answers from official docs</span></div></div></div> </div> </div> </div> </div> <div class='section_faqs'> <div class='padding-global padding-section-large'> <div class='container-large'> <div class='lca-faqs-grid'> <div><h2 class='lca-h2'>We get asked this <strong>all the time.</strong></h2><p class='lca-body' style='margin-top:1rem'>Straightforward answers about RAG systems.</p></div> <div class='lca-faq-list'> <div class='lca-faq-item' data-open='false'><button class='lca-faq-trigger'><h3>How is RAG different from just asking ChatGPT?</h3><svg class='lca-faq-arrow' fill='none' viewBox='0 0 24 24' stroke='currentColor' stroke-width='2'><path stroke-linecap='round' stroke-linejoin='round' d='M19 9l-7 7-7-7'/></svg></button><div class='lca-faq-collapse'><div class='lca-faq-answer'><p>ChatGPT answers from its training data. It doesn't know your documents, policies, products, or data. RAG gives the AI access to your specific information, enabling accurate answers about your business.</p></div></div></div> <div class='lca-faq-item' data-open='false'><button class='lca-faq-trigger'><h3>What if my documents are confidential?</h3><svg class='lca-faq-arrow' fill='none' viewBox='0 0 24 24' stroke='currentColor' stroke-width='2'><path stroke-linecap='round' stroke-linejoin='round' d='M19 9l-7 7-7-7'/></svg></button><div class='lca-faq-collapse'><div class='lca-faq-answer'><p>RAG can be built with security-first architecture. Options include private vector databases, on-premise LLMs, enterprise API agreements, and access controls that mirror your document permissions.</p></div></div></div> <div class='lca-faq-item' data-open='false'><button class='lca-faq-trigger'><h3>How do you handle contradictory information?</h3><svg class='lca-faq-arrow' fill='none' viewBox='0 0 24 24' stroke='currentColor' stroke-width='2'><path stroke-linecap='round' stroke-linejoin='round' d='M19 9l-7 7-7-7'/></svg></button><div class='lca-faq-collapse'><div class='lca-faq-answer'><p>We design retrieval to surface relevant context and generation prompts to handle ambiguity appropriately — presenting multiple perspectives, noting dates, acknowledging conflicts. The system can be configured to prefer authoritative sources.</p></div></div></div> <div class='lca-faq-item' data-open='false'><button class='lca-faq-trigger'><h3>What about documents in multiple languages?</h3><svg class='lca-faq-arrow' fill='none' viewBox='0 0 24 24' stroke='currentColor' stroke-width='2'><path stroke-linecap='round' stroke-linejoin='round' d='M19 9l-7 7-7-7'/></svg></button><div class='lca-faq-collapse'><div class='lca-faq-answer'><p>Modern embedding models handle multiple languages. Retrieval works across languages — an English question can retrieve relevant Spanish documents. Generation responds in the query language using multilingual sources.</p></div></div></div> <div class='lca-faq-item' data-open='false'><button class='lca-faq-trigger'><h3>Can RAG integrate with our existing search?</h3><svg class='lca-faq-arrow' fill='none' viewBox='0 0 24 24' stroke='currentColor' stroke-width='2'><path stroke-linecap='round' stroke-linejoin='round' d='M19 9l-7 7-7-7'/></svg></button><div class='lca-faq-collapse'><div class='lca-faq-answer'><p>Yes. RAG can augment or replace existing search. We design integration based on your current systems — sometimes RAG runs alongside traditional search, sometimes it replaces it, sometimes hybrid approaches work best.</p></div></div></div> </div> </div> </div> </div> </div> <script> (function(){var d=300;function o(i){var c=i.querySelector('.lca-faq-collapse');if(!c)return;i.dataset.open='true';c.style.overflow='hidden';c.style.height='0px';requestAnimationFrame(function(){c.style.height=c.scrollHeight+'px';setTimeout(function(){if(i.dataset.open=='true'){c.style.height='auto';}},d);});}function f(i){var c=i.querySelector('.lca-faq-collapse');if(!c)return;i.dataset.open='false';c.style.overflow='hidden';c.style.height=c.getBoundingClientRect().height+'px';requestAnimationFrame(function(){c.style.height='0px';});}var w=document.querySelectorAll('.lca-faq-list');w.forEach(function(l){var items=Array.prototype.slice.call(l.querySelectorAll('.lca-faq-item'));items.forEach(function(i){var t=i.querySelector('.lca-faq-trigger');var c=i.querySelector('.lca-faq-collapse');if(!t||!c)return;i.dataset.open='false';c.style.overflow='hidden';c.style.height='0px';c.style.transition='height '+d+'ms cubic-bezier(0.4, 0, 0.2, 1)';t.addEventListener('click',function(e){e.preventDefault();var s=i.dataset.open=='true';items.forEach(function(x){if(x!==i&&x.dataset.open=='true')f(x);});s?f(i):o(i);});});});})(); </script>