Applied AI / Agents / Commerce intelligence

AI woven through the business, not bolted on as a chatbot.

An AI layer across KitchyPro's customer service, commerce, marketing, knowledge and operations, built to reduce repetitive work and make the business easier to scale.

AI assistants
Knowledge & RAG
Commerce intelligence
Content intelligence
Agent workflows
Evaluation & observability

Main project case study AI services

The AI brief

Give every repetitive decision a better intelligence layer.

The AI work was designed as a connected business capability rather than a single chatbot. Assistants, retrieval, content intelligence, product discovery, operational decision support and agent-style workflows can share approved business context while preserving human control around important actions.

SHARED AI CONTEXTKITCHYPROKnowledge · tools · models · review

Customer service01

Commerce02

Marketing03

Content04

Operations05

Knowledge06

Business-wide AI systems

Six connected capabilities, each tied to a real operating surface.

The objective is operational leverage: faster service, better retrieval, stronger merchandising, faster content cycles and clearer decisions without hiding uncertainty.

01
Customer AI assistant
Answer common product and order questions from approved business knowledge, capture intent and route cases that require a person.
Conversation + handoff
02
Knowledge & retrieval
Ground assistants and internal tools in product, policy, campaign and operating knowledge instead of relying on model memory alone.
RAG + source context
03
Commerce intelligence
Support product discovery, comparison, recommendation, merchandising and customer-intent interpretation across the catalog.
Discovery + recommendation
04
Content intelligence
Accelerate research, creative ideation, product messaging, localization and reusable marketing production while retaining review gates.
Research → draft → review
05
Operational copilots
Summarize signals, classify work, surface anomalies and prepare decisions across advertising, COD operations and customer-service workflows.
Decision support
06
Agent orchestration
Connect model calls, business tools, retrieval, validation and human approval into explicit workflows with visible state and recovery paths.
Tools + guardrails + state

Agent orchestration

The model is only one node in the workflow.

Useful AI systems need context, tools, validation, state and a clear human control point. The orchestration layer makes those handoffs explicit.

01Intent
02Context
03Model
04Tools
05Validation
06Human gate
07Action

AGENT / ORCHESTRATION01

ADD AI EVIDENCEAgent orchestration mapai/agent-orchestration.webp1920 × 1080

Agent orchestration mapShow model calls, tools, retrieval, validation and action state.

TOOLS / TRACE02

ADD AI EVIDENCETool execution & handoffsai/tool-execution.webp1600 × 1000

Tool execution & handoffsShow a real tool-call or workflow trace with sensitive data removed.

AI assistants

Conversation that knows the business, and knows when to hand off.

Customer-facing and internal assistants can use approved product, policy and operating knowledge, collect structured intent and hand consequential cases to people instead of pretending confidence.

I need a compact wood storage solution for a small room.I can narrow the catalog by dimensions, room use and budget, then show matching products. If delivery or order exceptions are involved, I can route the case to support.Illustrative interaction, not a production transcript

ASSISTANT / UI03

ADD AI EVIDENCEAssistant conversation evidenceai/assistant-conversation.webp1200 × 1600

Assistant conversation evidenceAdd a production-safe conversation, support workflow or assistant UI screenshot.

Knowledge & RAG

Ground intelligence in business truth, not model memory.

Retrieval gives assistants and copilots access to product details, policies, campaign knowledge and operating documentation while preserving a path back to sources.

Approved sourcesCatalog · policies · FAQs · operations

→

Index & retrieveChunk · embed · rank · cite

→

Model contextRelevant evidence only

→

Answer / actionReviewable output

RAG / KNOWLEDGE04

ADD AI EVIDENCEKnowledge retrieval evidenceai/knowledge-rag.webp1920 × 1080

Knowledge retrieval evidenceAdd knowledge-base, retrieval trace or source-grounding evidence.

EVAL / GROUNDING05

ADD AI EVIDENCEGrounding evaluationai/evaluation-grounding.webp1600 × 1000

Grounding evaluationShow evaluation results for answer quality, source use or hallucination control.

Commerce intelligence

Turn a growing catalog into an easier conversation and decision.

AI can support product discovery, comparison, recommendation, merchandising and intent classification while respecting inventory, policy and COD constraints supplied by the commerce system.

Semantic product discovery
Intent-aware filtering
Product comparison
Recommendation support
Catalog classification
COD qualification support

COMMERCE / AI06

ADD AI EVIDENCEProduct recommendation systemai/commerce-recommendation.webp1600 × 1000

Product recommendation systemAdd recommendation, semantic search or merchandising evidence.

CATALOG / INTELLIGENCE07

ADD AI EVIDENCECatalog intelligenceai/catalog-intelligence.webp1400 × 1400

Catalog intelligenceAdd classification, attribute extraction or product-enrichment evidence.

Content & marketing intelligence

Accelerate the loop from signal to idea to reviewed content.

AI can compress repetitive research and production steps while keeping brand, market and product context visible. The system is designed to support people, not to publish unchecked output by default.

01SignalCustomer / ad / catalog
02ResearchContext + evidence
03IdeationAngles + concepts
04DraftCopy + variants
05ReviewHuman gate
06DeployChannel-specific
07LearnFeed outcomes back

CONTENT / COPILOT08

ADD AI EVIDENCEContent copilotai/content-copilot.webp1600 × 1000

Content copilotAdd research, briefing or drafting workflow evidence.

CONTENT / LOCALIZATION09

ADD AI EVIDENCELocalization workflowai/localization-workflow.webp1600 × 1000

Localization workflowAdd Arabic/French/local-market adaptation evidence where applicable.

MARKETING / COPILOT10

ADD AI EVIDENCEMarketing intelligence copilotai/marketing-copilot.webp1600 × 1000

Marketing intelligence copilotAdd campaign analysis, creative ideation or decision-support evidence.

Model & provider layer

Route the task to the right model, do not design the business around one logo.

These provider tiles illustrate the model ecosystem the architecture can integrate with. They do not claim that every provider shown is used in KitchyPro production. Add official logo assets when the production provider list is finalized.

OpenAI

General-purpose reasoning, multimodal and tool-use workflows

Anthropic

Reasoning and long-context workflows

Google Gemini

Multimodal reasoning and Google ecosystem integrations

xAI

Reasoning, tool use and real-time information workflows

Perplexity

Research, retrieval and citation-oriented answer workflows

Multi-model ecosystem

One AI architecture. Multiple intelligence engines.

The product layer stays independent from any single model vendor. Each task can be matched to the most suitable engine as reasoning, multimodality, research, latency and cost requirements change. These logos represent the integration landscape the architecture can accommodate, not a claim that every provider is active in production at the same time.

BUSINESS CONTEXTROUTING LAYERBEST-FIT MODEL

01OpenAIReasoning · tools

02AnthropicLong context · reasoning

03Google GeminiMultimodal · Google ecosystem

04xAIFrontier models · reasoning

05PerplexityResearch · grounded answers

TASKMODEL ROUTERQUALITY / COST / LATENCY / CAPABILITY

MODELS / ROUTING11

ADD AI EVIDENCEModel routing evidenceai/model-routing.webp1920 × 1080

Model routing evidenceAdd provider routing, model selection, fallback or cost-control evidence.

Human control & AI quality

Scale useful intelligence without scaling silent mistakes.

The page treats guardrails, evaluation, escalation and observability as product components rather than fine print.

Approved knowledge

Ground responses in controlled business context and retrievable sources.

Guardrails

Define what the system can answer, which tools it can call and when it must stop.

Human control

Keep consequential actions reviewable and preserve explicit escalation paths.

Evaluation

Measure quality, failures, cost and business usefulness instead of judging demos by feel.

SAFETY / REVIEW12

ADD AI EVIDENCEGuardrails & review controlsai/guardrails-review.webp1600 × 1000

Guardrails & review controlsAdd approval, escalation, refusal or tool-permission evidence.

EVAL / QUALITY13

ADD AI EVIDENCEAI evaluation dashboardai/evaluation-dashboard.webp1600 × 1000

AI evaluation dashboardAdd quality, task-success or regression evaluation evidence.

Observability

See what the AI did, why it did it and what it cost.

Production AI needs traces, quality signals, latency, model usage, tool outcomes, escalation counts and cost context. Evidence slots are ready for those real operational views.

Grounded answer rate

Add evaluated assistant traces and source-use evidence.

Human handoff quality

Add escalation and resolution evidence.

Workflow success rate

Add agent-run and tool-execution evidence.

Latency / cost

Add model-routing and usage exports.

OPS / OBSERVABILITY14

ADD AI EVIDENCEAI observability dashboardai/ai-observability.webp1600 × 1000

AI observability dashboardAdd traces, latency, tool outcomes and model-usage evidence.

OPS / COST15

ADD AI EVIDENCEAI cost & routingai/cost-dashboard.webp1600 × 1000

AI cost & routingAdd token, request, provider or cost-allocation evidence.

Evidence board

Replace every placeholder with the actual system as evidence becomes available.

The layout is deliberately evidence-first. Screenshots, traces, architecture diagrams and measured outcomes can be added without redesigning the page.

COPILOT / DECISION16

ADD AI EVIDENCEDecision copilotai/decision-copilot.webp1600 × 1000

Decision copilotAdd a real operations or growth decision-support interface.

AGENT / TRACE17

ADD AI EVIDENCEAgent workflow traceai/workflow-trace.webp1600 × 1000

Agent workflow traceAdd a sanitized multi-step execution trace.

OPS / BEFORE-AFTER18

ADD AI EVIDENCEBefore / after operationsai/before-after-operations.webp1920 × 1080

Before / after operationsShow where AI removed repetitive handling without inventing time-saved numbers.

IMPACT / VERIFIED19

ADD AI EVIDENCEAI contribution evidenceai/ai-growth-evidence.webp1920 × 1080

AI contribution evidenceAdd verified conversion, productivity, support or business-impact metrics when attribution is defensible.

Scale context

AI supports the growth system. It should not steal credit it cannot prove.

KitchyPro's broader growth program reached the scale context below according to client-provided project history. The AI case study keeps that context visible while separating it from AI-specific attribution.

System walkthrough / Optional motion

Use motion when the system is easier to understand as a flow.

The video slot is ready for a chatbot demo, agent workflow, RAG trace, model-routing explanation or end-to-end business AI walkthrough. It never autoplays.

AI / SYSTEM WALKTHROUGH01

OPTIONAL AI VIDEO EVIDENCEai-system-walkthrough.webm

Add a concise narrated product or architecture walkthrough; controls remain user initiated.

AI delivery scope

From assistant UX to orchestration, evaluation and operational control.

01AI opportunity and workflow mapping
02Customer-service AI assistants
03Knowledge-base and RAG architecture
04Product discovery and recommendation logic
05Content-intelligence workflows
06Marketing and advertising copilots
07Operational AI copilots
08Agent and tool orchestration
09Human-review and escalation controls
10Evaluation, monitoring and cost controls
11AI evidence and improvement framework

Intelligence should compound.

Build AI that becomes part of how the business actually operates.

Bring the workflows, business knowledge and operating constraints. Smartec can turn them into assistants, retrieval systems, copilots and agent workflows with measurable controls.

Discuss an AI project View the main project case study