refactor: AI Engineering services page content refinement

app/services/[slug]/ExpertisePageClient.tsx

@@ -228,7 +228,7 @@ export default function ExpertisePageClient({
       {pageData.useCases && (
         <UseCasesGrid
           title={expertise.slug === "frontend-development" ? "Frontend Development Use Cases" : expertise.slug === "backend-development" ? "Backend Development Use Cases" : "Use Cases"}
-          subtitle={expertise.slug === "frontend-development" ? "Our frontend development services support" : expertise.slug === "backend-development" ? "Our backend development services support" : "Our services support"}
+          subtitle={pageData.useCasesSubtitle || (expertise.slug === "frontend-development" ? "Our frontend development services support" : expertise.slug === "backend-development" ? "Our backend development services support" : "Real-world applications we help teams build and scale")}
           useCases={pageData.useCases.map((uc) => ({
             ...uc,
             icon: Icons[uc.icon as keyof typeof Icons] || Icons.code,

content/services/ai-agents.mdx

@@ -38,14 +38,16 @@ 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."
   - question: "Can AI agents work with our legacy systems?"
     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."

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)**

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;

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,