diff --git a/app/services/[slug]/ExpertisePageClient.tsx b/app/services/[slug]/ExpertisePageClient.tsx index 23ac2f2..5dff5d8 100644 --- a/app/services/[slug]/ExpertisePageClient.tsx +++ b/app/services/[slug]/ExpertisePageClient.tsx @@ -228,7 +228,7 @@ export default function ExpertisePageClient({ {pageData.useCases && ( ({ ...uc, icon: Icons[uc.icon as keyof typeof Icons] || Icons.code, diff --git a/content/services/ai-agents.mdx b/content/services/ai-agents.mdx index 5510768..0aed5ad 100644 --- a/content/services/ai-agents.mdx +++ b/content/services/ai-agents.mdx @@ -38,7 +38,7 @@ relatedExpertise: - backend-development - ai-security faqs: - - question: "What's the difference between an AI agent and a chatbot?" + - question: "What is the difference between an AI agent and a chatbot?" answer: "Chatbots respond to queries. Agents take action. An AI agent can research information, update databases, send emails, schedule meetings, and execute multi-step workflows autonomously—not just suggest what you should do." - question: "How do you ensure AI agents are safe in production?" answer: "We implement multiple safety layers: action constraints that limit what agents can do, sandboxed execution environments, approval workflows for sensitive actions, comprehensive logging, and rollback capabilities when things go wrong." @@ -46,6 +46,8 @@ faqs: answer: "Yes. Our engineers specialize in building integration layers that connect modern AI capabilities with existing enterprise systems—even those without modern APIs." - question: "How long until we have a working agent?" answer: "Most clients see a working prototype within the first week. Production deployment typically takes 2-4 weeks, depending on the complexity of your workflows and integration requirements." + - question: "What kind of tasks are best suited for AI agents?" + answer: "AI agents excel at tasks that are repetitive but require judgment—research and data gathering, document processing, customer inquiry routing, lead qualification, and multi-system workflows. If a task involves multiple steps, uses several tools, and currently requires human decision-making at each stage, it's a strong candidate for an AI agent." cta: title: "Ready to Deploy AI Agents?" description: "Talk to our team about automating your complex workflows with AI agents. We'll show you what's possible—and what's production-ready today." diff --git a/content/services/ai-engineering.mdx b/content/services/ai-engineering.mdx index bae6c9b..8eb4d1b 100644 --- a/content/services/ai-engineering.mdx +++ b/content/services/ai-engineering.mdx @@ -1,6 +1,6 @@ --- title: "AI Engineering Services" -headline: "AI Engineering Services" +headline: "Enterprise AI Engineering" headlineAccent: "Production-Ready AI Systems" tagline: "Build, deploy, and scale reliable AI systems." description: | @@ -19,7 +19,7 @@ capabilities: description: "Implement CI/CD for models, automated evaluation, versioning, monitoring, and rollback strategies." icon: "code" - title: "AI Application Development" - description: "Embed AI capabilities into web, mobile, and backend systems — search, recommendations, copilots, automation, and analytics." + description: "Embed AI into web, mobile, and backend systems — from semantic search to personalized recommendations and real-time decision engines." icon: "smartphone" - title: "AI System Monitoring & Optimization" description: "Track model accuracy, latency, cost, drift, and reliability in production environments." @@ -62,25 +62,26 @@ process: - number: 4 title: "Monitoring & Continuous Improvement" description: "Deploying with monitoring, evaluation, and optimization systems in place." +useCasesSubtitle: "Real-world applications we help teams build and scale" useCases: - icon: "search" title: "Intelligent Search & Recommendations" - description: "AI-powered search and recommendation systems" + description: "Hybrid search combining keyword and vector retrieval, with ranking models that learn from user behavior" - icon: "brain" title: "AI Copilots & Productivity Tools" - description: "Internal AI assistants and workflow automation" + description: "Domain-specific assistants that accelerate workflows, answer questions from internal knowledge, and automate repetitive tasks" - icon: "chart" title: "Predictive Analytics & Forecasting" - description: "Data-driven predictions and business intelligence" + description: "Demand forecasting, churn prediction, and resource optimization models that inform business decisions" - icon: "document" title: "Document Processing & Extraction" - description: "Information extraction and document understanding" + description: "Automated extraction, classification, and summarization for contracts, invoices, and unstructured data" - icon: "shield" title: "Fraud Detection & Anomaly Detection" - description: "Real-time anomaly detection and risk assessment" + description: "Scoring models that identify suspicious patterns in transactions, logins, and user behavior" - icon: "users" title: "Conversational AI & Automation" - description: "Chatbots and conversational workflow automation" + description: "Customer-facing chatbots and voice agents with context retention and multi-turn dialogue capabilities" whyChoose: reasons: - "Strong focus on production AI, not prototypes" @@ -131,13 +132,13 @@ faqs: - question: "What's the difference between AI engineering and AI consulting?" answer: "AI engineering focuses on building and operating AI systems in production, while consulting focuses on strategy and recommendations. We are engineering-focused." - question: "Do you work with existing AI systems?" - answer: "Yes. We help modernize, optimize, secure, and scale existing AI and ML systems, including legacy deployments." + answer: "Absolutely. We specialize in modernizing legacy ML infrastructure, optimizing model performance, and bringing security and observability to systems already in production." - question: "Can you support LLM-based applications?" - answer: "Yes. We build, integrate, deploy, and operate LLM-powered systems, including copilots, search, and automation tools." + answer: "LLM integration is a core strength. We build retrieval-augmented generation (RAG) systems, fine-tune models for domain-specific tasks, and deploy LLM-powered features with proper guardrails and cost controls." - question: "Do you provide post-deployment support?" - answer: "Yes. We offer ongoing monitoring, optimization, and operational support for AI systems in production." + answer: "Production is where our work truly begins. We provide ongoing monitoring, performance optimization, incident response, and continuous model improvement as your system evolves." - question: "Do you work with startups and enterprises?" - answer: "Yes. We support both startups building AI-first products and enterprises integrating AI into existing systems with compliance requirements." + answer: "Both. Startups benefit from our speed and pragmatic architecture decisions. Enterprises trust us to navigate compliance, security reviews, and integration with existing infrastructure." cta: title: "Talk to Our AI Engineering Team" description: "Discuss your AI system requirements, existing infrastructure, or production challenges with our engineering team. Let's build reliable AI systems together." @@ -148,10 +149,8 @@ seo: description: "Premium AI engineering services for production AI systems. Model development, MLOps, data pipelines, and AI application development from experienced engineers." --- -## Get Started With Procedure +## Your AI System, Built for Production -Whether you need AI system architecture, model development, MLOps, or AI application integration — we're here to help. +Most AI projects fail between prototype and production. We bridge that gap with engineering discipline — reliable data pipelines, observable model serving, and infrastructure that scales with your business. -**→ [Schedule a call with our AI engineering team](/contact-us)** - -This is AI engineering for production reliability, security, and business impact. +**→ [Talk to an AI engineer](/contact-us)** diff --git a/lib/content-types.ts b/lib/content-types.ts index 5b61f8a..420e043 100644 --- a/lib/content-types.ts +++ b/lib/content-types.ts @@ -150,6 +150,7 @@ export interface ExpertiseFrontmatter extends BaseFrontmatter { description: string; icon?: string; }>; + useCasesSubtitle?: string; useCases?: Array<{ icon: string; title: string; diff --git a/lib/content.ts b/lib/content.ts index 14851e7..a3c2dec 100644 --- a/lib/content.ts +++ b/lib/content.ts @@ -562,6 +562,7 @@ export interface ExpertisePageForListing { description: string; icon?: string; }>; + useCasesSubtitle?: string; useCases?: Array<{ icon: string; title: string; @@ -675,6 +676,7 @@ export function getExpertiseForListing( testimonials: frontmatter.testimonials || [], whoWeWorkWith: frontmatter.whoWeWorkWith, process: frontmatter.process, + useCasesSubtitle: frontmatter.useCasesSubtitle, useCases: frontmatter.useCases, whyChoose: frontmatter.whyChoose, qualityMatters: frontmatter.qualityMatters,