SaaS
Configured around your processes and integrated into your stack — not the other way around.
AI Solutions
AI Solutions
for eCommerce:
from concept
to deployment
We bring core AI technologies into enterprise-scale processes: designing agents and digital departments, connecting data sources, internal and external systems, adding metrics and KPIs — from pilot to full production.
Qualified team Scalable solutions On-premise deployment
What AI integration means to us
It is not about purchasing a SaaS tool and connecting a chat widget via API. It is about rebuilding your business processes around AI agents — with clear goals, metrics, fallback scenarios, and technology embedded into your existing stack.
We work with real adoption metrics: hours of saved time (FTE), conversion rates, and cost per processed request — not the number of tokens generated.
How this differs from SaaS and generic AI
Generic AI
Scoped to a specific business problem, with fallback scenarios and quality controls.
No-code bots
Production-grade architecture: observability, versioning, governance.
Solutions catalogue
AI Solutions by area
Pre-built AI modules for common enterprise scenarios. Each solution is configured to fit your processes and technology stack.
AI Content Factory
Generation, adaptation, and validation of product content to meet marketplace and retailer requirements.
- Faster content production
- Consistent attribute and rich-content standards
- Scaling across dozens of marketplaces
AI Digital Shelf Analytics
24/7 monitoring of product listings, prices, stock, ratings, and visibility across cities and marketplaces.
- Greater digital shelf control
- Early anomaly detection
- Team action prioritization
AI Marketplace Manager
Managing product listings, moderation, promotions, and reporting across dozens of marketplaces simultaneously.
- Faster SKU go-live on marketplaces
- Reduced manual workload for teams
- Consolidated cross-channel reporting
AI Pricing & Inventory Management
Competitive monitoring, demand forecasting, and pricing scenarios with reasoning.
- Reduced out-of-stock incidents
- Improved forecast accuracy
- Responsive pricing
AI Reviews & Reputation Analysis
Review classification, root-cause analysis of negative feedback, response drafting, and alerts for critical issues.
- Shorter response SLA
- Systematic root-cause analysis of negative feedback
- Managed brand reputation
AI Customer Service
L1 AI agents handling routine inquiries, orders, and returns with escalation to human operators.
- Lower cost of support
- Shorter first-response SLA
- Escalation of complex cases only
AI Retail Media Management
Performance analysis of placements with recommendations on bids and budgets.
- Higher campaign ROMI
- Cross-marketplace summary view
- Budget transparency
AI Search & GEO
Optimizing product data for visibility in AI search and generative systems.
- Increased product visibility
- AI-ready catalog
- Readiness for agentic commerce
RAG & Knowledge Bases
Corporate documents and policies made accessible to employees and systems via AI agents.
- Faster response times
- Unified knowledge base
- Query audit log
What is a Digital Employee
A digital employee is not a "chatbot" or an "AI on an API." It is a semi-autonomous role with a defined area of responsibility, inputs, outputs, KPIs, and integrations with the client's working systems.
AI agents take on repetitive tasks: data collection, content generation, request classification, preparation of recommendations, and automated actions. Decisions that affect business outcomes remain with a human.
Multiple roles compose into a digital department: they exchange data, escalate decisions, delegate tasks, and operate as a unified team.
Digital Roles Catalog
Eight Roles for eCommerce Operations
Each role has a defined area of responsibility, specific inputs, outputs, KPIs, and integrations.
AI Content Manager
Generates, adapts, and validates product content.
Content Factory
AI Digital Shelf Analyst
Monitors product listings, prices, availability, ratings, and visibility 24/7.
Shelf Analytics
AI Marketplace Manager
Manages product listings, moderation, promotions, and reporting across dozens of marketplaces.
Marketplace Operations
AI Pricing Analyst
Tracks competitors, inventory, demand, and suggests pricing actions.
Pricing Analytics
AI Reviews Analyst
Analyses reviews, identifies sources of negative feedback, prepares responses.
Reviews Analytics
AI Support Agent
Answers common questions, assists with orders, statuses, and returns.
Customer Experience Automation
AI Retail Media Manager
Analyses placements and recommends bid and budget optimisations.
Retail Media
AI B2B Sales Assistant
Assists with product selection, quotes, orders, and repeat sales.
B2B Sales
From a Single Agent to a Digital Department
One agent covers an area of responsibility — multiple agents form a digital department. They exchange data, escalate decisions, and operate as a unified team on top of the client's working systems.
Composition is built around a specific business process: a content department, a marketplace operations department, a support and B2B sales department. Each department has its own inputs, outputs, KPIs, and human control checkpoints.
- Digital Content Department — AI Content Manager + AI Digital Shelf Analyst + AI Reviews Analyst — produce, monitor, and improve content based on marketplace feedback.
- Marketplace Operations Department — AI Marketplace Manager + AI Pricing Analyst + AI Retail Media Manager — cover the full operational cycle from product listing to promotions.
- Support & B2B Department — AI Support Agent + AI B2B Sales Assistant — handle communication and commerce with B2C and B2B clients simultaneously.
SourcePIM · DAM
SourceERP · OMS
SourceMarketplace API
SourceReviews · CRM
AgentContent
AgentDigital Shelf
AgentReviews
AgentPricing
AgentSupport
AgentMarketplaces
Human-in-the-loop · governance
OutputListings & content
OutputReports · alerts
OutputActions · responses
AI Integration Architecture
Working across 6 layers: from data sources to delivery channels
A reference solution architecture. Each layer has specific systems, artefacts, and control points.
Layer 01 Data Sources
PIMERPCRMOMSBIMarketplace APIsRetailer DataReviews
Layer 02 Data · Knowledge · RAG
Corporate knowledge baseNormalised dataRAG indexDocuments & policies
Layer 03 AI agents · Orchestration
Multi-agent systemOrchestration rulesTask schedulerAgent memory
Layer 04 Governance
GuardrailsHuman-in-the-loopAudit logsRole-based accessQuality control
Layer 05 Outputs
ListingsReportsRecommendationsResponsesTasksAPI actionsDashboards
Layer 06 Channels
MarketplacesRetailersBrand siteSupportSalesInternal tools
A request passes through all six layers: data → knowledge → agent → governance → output → channel. An audit log is recorded at every step; humans are brought in on critical decisions.
Governance & Security
Enterprise security and controllability from day one
AI is deployed with security, data protection, and corporate policy requirements in mind. Without this, a pilot never leaves the sandbox.
Closed perimeter / on-prem / private cloud
Deployment within the client's infrastructure when required by security policy.
Client data is not used to train external models
Corporate client data stays within the client's perimeter. No external fine-tuning by default.
Human-in-the-loop
On critical decisions, AI prepares an option and a human confirms or adjusts it.
Guardrails and quality control
Rules, limits, and filters for AI agents. Regular quality and metrics reviews.
Audit logs
A full history of AI agent and human actions: what, who, when, and on what basis.
Role-based access control
Permission configuration by role, department, and project. RBAC out of the box.
Protection of personal and commercial data
Compliance with personal data and commercial confidentiality requirements.
Integration with corporate policies
Connection to the client's existing IAM, SIEM, DLP, and DevSecOps practices.
Frequently asked questions
How long does the first implementation take?
4–6 weeks to a pilot launch with a measurable metric. Full production deployment takes 3–6 months depending on integration depth.
Which AI models do you use?
We select based on the task: commercial frontier models (Claude, GPT) for quality; open-source (Llama, Qwen) for on-prem and privacy; custom fine-tuned models for narrow domains.
How do you ensure data security?
On-prem deployment, data isolation, alignment with corporate information security policies, audit logs, and access controls. We do not send client data to public models without explicit consent.
Who is responsible for the outcome?
A dedicated project manager on our side plus a technical lead. Metrics and SLAs are agreed upon before the project starts.
What if the pilot results are not confirmed?
We provide a transparent report on what worked and what did not. The decision to continue is yours; the pilot cost is fixed in advance.
Cases
How AI is embedded in client workflows
Some clients are under NDA. Case studies and project details are available on request.
Fashion retailer
Size analytics
Size mapping and cross-brand/cross-retailer matching. AI identifies discrepancies and suggests corrections.
Impact Reduced returns and sizing errors
FMCG brand
Content factory
Generation and adaptation of visual content, descriptions, and rich content to meet platform and retailer requirements.
Impact Reduced content production time
Marketplace
Product data operations
Automated validation of listings, attributes, content quality, and moderation of incoming SKUs.
Impact Improved catalogue quality
Electronics brand
Digital shelf analytics
Monitoring of listings, prices, availability, visibility, ratings, and competitors across dozens of platforms.
Impact Improved digital shelf control
Retailer
Reviews analytics
AI analysis of reviews, identification of the root causes of negative feedback, and automated drafting of responses for the moderator.
Impact Reduced review response SLA
Ready to discuss your challenge?
Tell us about your process — we will propose an audit, a pilot, or a full integration.