AI infrastructure for enterprise

AI infrastructure partner for enterprise

We build an internal AI layer that works across your existing systems—ERP, CRM, WMS, and MES. AI does not replace people or disrupt infrastructure: it automates the communications, documents, and management operations where time and money are lost.

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On-premises and private cloud

Deployment without external APIs. All data, models, and integrations remain inside the company perimeter.

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Complete data control

Information does not leave the client infrastructure. The design supports applicable data-protection, security, and regulatory requirements.

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Fits the existing landscape

The AI layer is added on top of ERP, CRM, WMS, and MES—without replacing systems or disrupting current processes.

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Measurable KPI impact

We speak the language of CFOs and COOs: SLA savings, fewer errors, and greater throughput.

Frequently asked questions
Security: every solution stays within the company perimeterHow security works → Concerned that AI may make things up?How we reduce LLM hallucinations →
We replace inefficient processes, not people. We speak the language of CFOs, COOs, and CIOs: measurable financial impact rather than technology hype. A single AI operational layer is embedded in real processes for real-time control, analysis, and automation. It can be deployed on premises or in closed AI environments without external APIs.

Six AI-layer modules

From deploying proprietary LLMs to cybersecurity: a full AI-infrastructure lifecycle within the company perimeter.

Processes

Process control

Challenge: processes stall, SLAs are breached, and execution lacks transparency.

Approach: connect to corporate systems, track deviations, notify owners, execute actions, and log delays.

  • −25–40% SLA breaches
  • −30% stalled tasks
  • +15–35% process speed
Sales

Communications and sales

Challenge: errors in conversations, lost leads, and inconsistent communication quality.

Approach: analyse calls, chats, emails, and tickets; detect script deviations; assess quality; and provide recommendations to employees.

  • +18–30% sales conversion
  • −40–60% communication errors
  • +20–25% standards quality
Analytics

Losses and bottlenecks

Challenge: money and leads are lost between departments and process stages.

Approach: build a loss map from real processes, identify bottlenecks, quantify their cost, and propose automation.

  • −15–35% operational losses
  • −20–50% repeat contacts
  • +5–15% recovered deals
Quality

Execution quality

Challenge: procedures are followed only formally and errors go unnoticed.

Approach: compare actual execution with procedures and SOPs, audit processes, detect violations, and produce quality reports.

  • −30–50% operational errors
  • −20–40% incidents
  • +15–30% procedure compliance
Knowledge

Documents and knowledge

Challenge: knowledge is fragmented, decisions are duplicated, and experience is lost.

Approach: analyse contracts, procedures, and corporate data; search for and generate documents; create a unified knowledge base; and control standards.

  • Faster access to knowledge
  • Fewer document errors
  • Reuse of company expertise
People

HR and people support

Challenge: long onboarding, formal-only training, and a heavy burden on mentors.

Approach: support employees at every stage, create personalised onboarding paths, provide an AI mentor, and automate eligibility for operations.

  • Faster time to productivity
  • −70% mentor workload
  • −15–25% employee churn

Measurable impact

Figures supported by real deployments in development, banking, logistics, and manufacturing.

−25–40%
SLA breaches
+18–30%
Sales conversion
−15–35%
Operational losses
−30–50%
Operational errors
−70%
Mentor workload
−15–25%
Employee churn
+15–35%
Process speed
−20–40%
Incidents

Case studies

Concrete cases where we built AI infrastructure with measurable economic impact.

Online pharmacy (EU) · AI marketing · GDPR

AI marketing and reactivation for an online pharmacy

Value: retention and reactivation automation through customer segmentation, triggered campaigns, and personalised communications across CRM and the data warehouse.

How we apply AI: a closed AI perimeter with a fail-closed PII guard, an outbound allow-list, EU data residency, and provider-side zero retention. Campaigns pass an offline evaluation harness before release.

Impact: marketing scenarios receive automated regression checks before launch while personal data never leaves the perimeter in clear text.

Telecom · CRM business analysis · On-premises AI

AI business-analysis perimeter for an enterprise CRM rollout

Value: accelerate telecom CRM business analysis by reconstructing as-is processes, RACI, counterparty registers, and glossaries from email, chat, and document systems.

How we apply AI: an on-premises privacy perimeter with multimodal scan recognition, personal-data de-identification, reversible-token vaults, RAG, and adversarial verification of low-confidence facts.

Impact: business-analysis artefacts carry confidence scores and traceability; personal data and trade secrets remain inside the perimeter.

Video surveillance + access control · Multi-tenant SaaS

AI-native SaaS platform for video surveillance and access control

Value: cameras and access controllers from multiple vendors connect through unified adapters, with billing, analytics, and roles for many independent tenants.

How we apply AI: schema-per-tenant database isolation, role-based access, vendor-agnostic adapters, and an automated architect-plus-executor deployment workflow.

Impact: one codebase serves many tenants under strict data isolation, with releases executed through an orchestrated pipeline rather than manually.

Fintech · B2B platform · Greenfield development

BelVEB BusinessHub — a secure B2B transaction platform

Value: automate the full B2B transaction lifecycle with verification, security, and banking-instrument integrations.

How we apply AI: greenfield architecture, banking-system integrations, and a full lifecycle from design through launch within the bank perimeter.

Impact: a platform for Bank BelVEB supports verified sellers and buyers and secure transactions using banking instruments.

belvebhub.by

Science · Multi-agent AI · RAG

AI agent for anticancer-compound research

Value: accelerate scientific research through structure modelling, literature analysis, and automated review drafting across several research teams.

How we apply AI: a multi-agent RAG system on corporate data, semantic search, review generation, and ligand–target interaction modelling.

Impact: a high-accuracy multi-agent system for private laboratory data in organometallic anticancer research.

Healthcare · RAG · On-premises LLM

Clinical AI assistant based on RAG + LLM

Value: real-time clinician support using clinical guidance and a local patient-history database, with no data sent to external APIs.

How we apply AI: a Telegram bot, local embeddings, RAG search across internal documents, and image analysis of X-rays and reports.

Impact: the design validates the importance of keeping patient data inside the company for privacy and security.

seraph.ai-automation.llc

Healthcare · Scale-up

Talon.by — digital medical appointments

Value: national-scale healthcare digitisation through a unified electronic-queue platform, paid-service enablement, and government-system integrations.

Impact: the project has been deployed at more than 1,000 healthcare facilities across the Republic of Belarus.

International startup · AI optimisation

Kuda.party — a leisure-discovery platform

Value: optimise a startup’s development across iOS, Android, and RuStore with AI tools that accelerate the team and lower cost.

Impact: development costs are three times lower than comparable market offerings, and the application is available in three stores.

Oil trading · Document workflows · NDA

AI automation of primary-document processing

Value: automate incoming document workflows: collect offers, analyse them semantically, match them against a database, generate natural-language briefs, and export to CRM.

How we apply AI: an AI pipeline with knowledge-base matching, natural-language search, and a chat-based operational interface. Data remains inside the perimeter.

Impact: counterparty-offer processing is removed from operators, and a pilot on real client data is possible.

Industry applications

AI does not replace CRM, ERP, WMS, or MES—it adds an AI layer that removes the limitations of conventional automation.

Industrial and manufacturing

Procedure control, fewer errors, and faster employee training.

Finance, banking, and insurance

Communication control, compliance, and work with documents and contact centres.

Retail and property development

Lead management, improved sales quality, and lower front-line losses.

Logistics and transportation

Chain control, fewer disruptions, and more predictable operations.

Telecommunications

Support automation, SLA control, and lower service workload.

IT and technology companies

Faster delivery and lower load on support and project teams.

Gaming and digital services

User support, a lower support load, and audience retention.

Security by architecture

Security is a design decision, not an afterthought: deployment, data handling, model access, and integrations are selected around the client’s risk profile.

Perimeter

Data stays in control

We can deploy on premises or in a private cloud, keeping models, data, and integrations inside the intended boundary.

Access

Least privilege and traceability

Integrations are scoped to the task, access is role-based, and actions can be logged for audit and investigation.

Operations

Safe change paths

We test scenarios before release, define failure behaviour, and separate experimental work from production operations.

Reliability and hallucination control

A fluent answer is not evidence. The system is designed to retrieve supporting sources, separate execution from review, and surface uncertainty rather than invent certainty.

Sources

Ground answers in evidence

Where the task requires factual output, the answer is built from verified materials with traceability to the source.

Review

Separate creator and controller

The actor that produces work does not approve it alone. Independent checks focus on evidence and acceptance criteria.

Escalation

Stop when evidence is insufficient

Uncertain or unsupported claims are marked for review rather than presented as established facts.

Custom AI development

Standard products do not cover complex enterprise processes and integrations.

Integrations

Enterprise-system integrations

ERP, CRM, WMS, MES, and internal systems. AI agents tailored to departments and functions.

Security

On-premises and closed AI perimeters

Deployment without external APIs and complete data control within the company.

Automation

End-to-end automation

Complex business processes from request to result, with models adapted to corporate data.

Result: AI is embedded into the existing IT architecture; solutions fit real processes rather than templates; data remains fully controlled; scaling does not require changes to core systems.

Start with an AI-readiness audit

We will assess your processes, calculate deployment economics, and prepare a roadmap without unnecessary spend.

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