Intelligent Product Search
Semantic and natural-language search experiences that help customers discover relevant products with less effort.
Build practical AI products that improve customer experiences, retail decisions and operational efficiency.
Logiciel provides AI product development services for retail companies building intelligent customer experiences and operational tools. From AI strategy and data readiness to application development, model integration, evaluation, cloud engineering and managed operations, we help retailers move from isolated experiments to secure, scalable AI products that create measurable value.
Retail companies have access to large volumes of customer, product, inventory, order and operational data, but turning that information into reliable product experiences remains difficult.
Each engagement is designed to create an AI product your internal team can understand, operate and continuously improve.
An AI product roadmap aligned with customer, merchandising and operational priorities.
Clearly defined retail use cases, success criteria and measurable product outcomes.
Senior AI, product, data, software and cloud engineers matched to your environment.
Scalable application architecture and reliable product, customer and inventory data pipelines.
Secure integrations with ecommerce, customer-service and operational systems.
Evaluation frameworks, automated testing and human-review controls where required.
Monitoring, model-cost visibility, documentation and an operating model your team can maintain.
We cover the complete retail AI lifecycle. Customer experience, product data, operational workflows, evaluation and production controls need to work together.
Semantic and natural-language search experiences that help customers discover relevant products with less effort.
Assistants that support product discovery, order questions, inquiry classification, response preparation and escalation.
Recommendation capabilities using approved customer, product and interaction data to improve product relevance.
Product classification, attribute generation, missing-information identification, document extraction and content preparation for review.
Forecasting and analytical products supporting replenishment decisions, inventory visibility and early identification of imbalances.
Controlled AI workflows and agents that use approved tools to support defined operational tasks and exception handling.
Quality evaluation, security, monitoring, cost optimization, incident response and continuous product improvement.
Retail AI Discovery and Validation
A focused engagement identifying the right use case, assessing data readiness and creating a testable product concept with measurable outcomes.
Dedicated AI Product Engineering Squad
A cross-functional team working continuously across retail applications, data, AI, testing, integrations and cloud infrastructure.
Outcome-Based Retail AI Product Development
A defined engagement organized around agreed milestones and measurable customer or operational outcomes.
Assessment of customer journeys, operational workflows, available data, technical feasibility and expected product value.
AI-powered product discovery and recommendation experiences using approved product, customer and interaction data.
Assistants that retrieve approved information, support customer questions, prepare responses and escalate complex requests.
Product classification, attribute enrichment, document processing, content preparation and human-review workflows.
Forecasting, inventory intelligence, promotion insights and natural-language interfaces for approved retail data.
Data pipelines, application integrations and controlled agents connected with ecommerce and operational systems.
Ongoing feature delivery, evaluation, monitoring, model updates, incident response, cost optimization and continuous improvement.
Patterns from our retail, product, AI and data engineering teams that help companies move from isolated experiments to reliable customer and operational products.
Retail AI Value and Readiness Model
How we evaluate customer value, workflow fit, retail-data quality, technical feasibility, operating cost and implementation risk.
Retail AI Quality Framework
A practical approach to measuring search relevance, recommendation quality, response accuracy, consistency, latency, cost and failure handling.
1. Opportunity and Success Definition
We assess the customer or operational problem and define measurable outcomes such as relevance, conversion, adoption, response quality or reduced manual effort.
2. Retail Data and Knowledge Readiness
We evaluate the accuracy, ownership, permissions and accessibility of product, customer, inventory, order and operational data.
3. Product, Architecture and Workflow Engineering
We design and build the application experience, data pipelines, integrations, models, APIs and human-review workflows.
4. Evaluation, Security and Production Hardening
We test AI behavior across customers, products, roles and operational scenarios while strengthening permissions, safeguards, monitoring and cost controls.
5. Controlled Launch and Retail AI Operating Model
We introduce capabilities through pilot users, feature flags, selected stores or phased deployments and provide evaluation practices and operational runbooks.
Move beyond disconnected experiments with a senior AI product engineering team that can take your retail use case from discovery through production.
Services can include AI strategy, data engineering, product search, recommendations, customer-service automation, forecasting, evaluation, cloud infrastructure and managed operations.
AI can improve product discovery, personalization, customer support, catalog management, demand forecasting and operational decision support.
Yes. Logiciel can integrate AI-powered search, recommendations, assistants, product enrichment and operational automation into established retail platforms.
The required data depends on the use case and may include product, customer, inventory, order, interaction or operational information.
No. Predictable workflows such as payment calculations, tax logic and inventory reservation are often better handled through conventional software.
Logiciel defines representative test cases and quality criteria for accuracy, relevance, consistency, latency, cost and failure handling.
Your organization retains ownership of the source code, application architecture, data pipelines, automated tests, documentation and agreed deliverables.
Yes. Ongoing support can include feature delivery, production monitoring, AI evaluation, model updates, incident response and continuous product improvement.