[{"data":1,"prerenderedAt":199},["ShallowReactive",2],{"svc-detail-generative-ai-llm":3,"sub-work-generative-ai-llm":153,"ill-services\u002Fgenerative-ai-llm":198},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":69,"benefits":85,"process":91,"tools":107,"industries":118,"faq":125,"relatedWork":141,"relatedServices":145,"seo":150},"generative-ai-llm","Generative AI & LLM","Generative AI & LLM development services","Put GPT-class models to work inside your product and operations.","Generative AI and LLM services put models such as GPT, Claude, Gemini and open-source LLMs to work inside your software: custom assistants and copilots, AI search, writing and summarisation tools, recommendation and personalisation engines. We handle prompt engineering, multi-model orchestration, evaluation and cost control so features ship reliably and stay accurate as models change.","Custom GPT-powered applications, AI copilots, search engines and enterprise GenAI tools, integrated into your product with the security, evaluation and cost controls an enterprise needs.","spark",[12,16,21,25,29,33,37,41,45,49,53,57,61,65],{"slug":13,"name":14,"text":15},"custom-gpt-applications","Custom GPT-powered applications","Purpose-built applications on GPT, Claude or open models with your data, workflows and interface, not a generic chat window.",{"slug":17,"name":18,"text":19,"page":20},"ai-copilots","AI copilots","In-product copilots that draft, explain, analyse and act inside your SaaS or internal tools, with context from the user’s data.",true,{"slug":22,"name":23,"text":24},"ai-assistants","AI assistants","Task-focused assistants for teams (sales, legal, finance, engineering) grounded in your documents and systems.",{"slug":26,"name":27,"text":28},"ai-search-engines","AI search engines","Natural-language search over products, content or knowledge with synthesised answers and citations.",{"slug":30,"name":31,"text":32},"ai-writing-systems","AI writing systems","Brand-consistent drafting for proposals, reports, product descriptions and support replies with review workflows.",{"slug":34,"name":35,"text":36},"enterprise-genai-tools","Enterprise GenAI tools","Secure internal GenAI platforms with access control, audit logs, model routing and usage governance.",{"slug":38,"name":39,"text":40},"multi-model-orchestration","Multi-model orchestration","Route each request to the best model by quality, cost and latency, with fallbacks and evaluation.",{"slug":42,"name":43,"text":44},"prompt-engineering","Prompt engineering","Systematic prompt design, versioning and evaluation harnesses so quality is measured, not guessed.",{"slug":46,"name":47,"text":48},"ai-personalisation-engines","AI personalisation engines","Content, offers and experiences tailored per user with LLMs and behavioural data.",{"slug":50,"name":51,"text":52},"ai-summarisation","AI summarisation","Summaries of calls, documents, threads and reports at the length and format each audience needs.",{"slug":54,"name":55,"text":56},"ai-recommendation-systems","AI recommendation systems","Hybrid recommenders combining embeddings, behavioural signals and LLM explanations.",{"slug":58,"name":59,"text":60},"ai-content-generation","AI content generation","Scaled content pipelines with brand guardrails, fact checks and human approval.",{"slug":62,"name":63,"text":64},"ai-translation","AI translation","Domain-tuned translation and localisation with glossary control and quality review.",{"slug":66,"name":67,"text":68},"ai-documentation-tools","AI documentation tools","Generate and maintain technical and process documentation from code, tickets and conversations.",[70,73,76,79,82],{"title":71,"text":72},"In-product copilot","Ship an assistant inside your app that answers, drafts and acts on the user’s data: a feature customers pay for.",{"title":74,"text":75},"Search that answers","Replace keyword search with answers and citations across help centres, catalogues and knowledge bases.",{"title":77,"text":78},"Drafting at scale","Proposals, reports, listings and replies drafted from your data in your voice, reviewed by people.",{"title":80,"text":81},"Summaries everywhere","Meeting, ticket and document summaries pushed to the right place automatically.",{"title":83,"text":84},"Personalised experiences","Recommendations and content adapted to each user with explanations they trust.",[86,87,88,89,90],"New product capabilities shipped in weeks","Answers with sources instead of searching","Predictable token spend and latency","Quality measured with evaluation sets, release over release","Data stays inside your boundary",[92,95,98,101,104],{"step":93,"text":94},"Use-case and model selection","Define the job, quality bar and constraints; compare candidate models on your data, not benchmarks.",{"step":96,"text":97},"Prompt and retrieval design","Design prompts, context, tools and grounding, with an evaluation set built from real examples.",{"step":99,"text":100},"Build the API layer","A robust service with streaming, caching, rate limits, observability and cost tracking.",{"step":102,"text":103},"Evaluate and harden","Automated and human evaluation, red-teaming for prompt injection and unsafe output, and fallbacks.",{"step":105,"text":106},"Ship and iterate","Release behind flags, watch usage and quality dashboards, and upgrade models when the evals say so.",[108,109,110,111,112,113,114,115,116,117],"OpenAI","Anthropic Claude","Google Gemini","Hugging Face","LangChain","LangSmith","LlamaIndex","Pinecone","FastAPI","Python",[119,120,121,122,123,124],"saas-technology","professional-services","retail-ecommerce","finance","healthcare","real-estate-construction",[126,129,132,135,138],{"q":127,"a":128},"Open models or APIs?","We choose per use case: hosted APIs for speed and quality, open models such as Llama, Mistral and Qwen when data residency, cost at scale or offline use matters. Many systems mix both, and we build so that switching models later is a configuration change, not a rewrite.",{"q":130,"a":131},"How do you stop the model making things up?","Ground answers in retrieved, approved content; constrain scope and tone; require citations where facts matter; and measure hallucination rate on an evaluation set. For high-stakes outputs we add a human review step. No system is perfect, so we design the failure mode to be visible.",{"q":133,"a":134},"How is our data protected?","Private deployments or no-training API tiers, encryption in transit and at rest, per-user access filtering, secret management and audit logging. We document the data flow before writing code and can keep everything inside your cloud account or on-prem when required.",{"q":136,"a":137},"What will it cost to run?","Token spend depends on volume, model and how much context each request needs. We estimate from your expected usage during design, then reduce cost with caching, smaller models for simple steps and prompt optimisation. Cost dashboards are part of every delivery.",{"q":139,"a":140},"How quickly can we ship a first feature?","A well-scoped feature such as summarisation or an in-app assistant can reach a production pilot in four to eight weeks. The evaluation set and API layer built for the first feature make the next ones faster. We recommend a small pilot group and clear success metrics from the start, so the decision to roll out is based on evidence rather than a demo.",[142,143,144],"saas-onboarding-assistant","ecommerce-personalisation-plugin","enterprise-rag-knowledge-base",[146,147,148,149],"rag-systems","ai-integration","ai-fine-tuning","ai-saas-product-development",{"title":151,"description":152},"Generative AI & LLM Development Services | AIYatch","Generative AI and LLM services: custom GPT applications, AI copilots, search, summarisation, personalisation and multi-model orchestration with evaluation.",[154,172,185],{"path":155,"title":156,"description":157,"industry":158,"services":159,"results":161,"cover":171,"coverAlt":171},"\u002Fwork\u002Fecommerce-support-assistant","ChatGPT-powered support assistant for an online retailer","A GPT-based assistant grounded in order data and help-centre content that now handles the majority of first-line customer inquiries.","Retail & E-commerce",[160,146,4],"ai-chatbots",[162,165,168],{"value":163,"label":164},"80%","of inquiries resolved without an agent",{"value":166,"label":167},"60%","faster first response",{"value":169,"label":170},"+40%","customer satisfaction (CSAT)",null,{"path":173,"title":174,"description":175,"industry":176,"services":177,"results":178,"cover":171,"coverAlt":171},"\u002Fwork\u002Fsaas-onboarding-assistant","Onboarding assistant for a B2B SaaS product","An in-app assistant that guides new users through setup, answers product questions and nudges toward activation.","SaaS & Technology",[160,146,4],[179,182],{"value":180,"label":181},"+27%","14-day activation rate",{"value":183,"label":184},"-35%","onboarding-related support tickets",{"path":186,"title":187,"description":188,"industry":189,"services":190,"results":191,"cover":171,"coverAlt":171},"\u002Fwork\u002Fenterprise-rag-knowledge-base","RAG knowledge base for an enterprise services firm","A retrieval-augmented assistant over thousands of internal documents, with citations and role-based access.","Professional Services",[146,147,4],[192,195],{"value":193,"label":194},"~70%","faster answers to policy and process questions",{"value":196,"label":197},"100%","answers with source citations","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cpath d=\"M82 292H188A12 12 0 0 1 200 304V340A12 12 0 0 1 188 352H181L184 368L166 352H82A12 12 0 0 1 70 340V304A12 12 0 0 1 82 292Z\"\u002F>\u003Cline x1=\"90\" y1=\"312\" x2=\"180\" y2=\"312\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"90\" y1=\"328\" x2=\"150\" y2=\"328\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cpath d=\"M180 104Q180 150 226 150Q180 150 180 196Q180 150 134 150Q180 150 180 104Z\"\u002F>\u003Ccircle cx=\"180\" cy=\"150\" r=\"8\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cpath d=\"M148 218Q148 232 162 232Q148 232 148 246Q148 232 134 232Q148 232 148 218Z\"\u002F>\u003Cpath d=\"M250 100H438L470 132V380H250Z\"\u002F>\u003Cpath d=\"M438 100V132H470\"\u002F>\u003Cline x1=\"282\" y1=\"160\" x2=\"432\" y2=\"160\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"282\" y1=\"194\" x2=\"402\" y2=\"194\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"282\" y1=\"228\" x2=\"442\" y2=\"228\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"282\" y1=\"262\" x2=\"412\" y2=\"262\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"282\" y1=\"296\" x2=\"432\" y2=\"296\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"282\" y1=\"330\" x2=\"340\" y2=\"330\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"356\" cy=\"330\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973684760]