AI Automation

We implement AI solutions for process automation, analytics, and user support: assistants, knowledge-base search (RAG), classification and routing, summaries, reporting, and integrations.

AUTOMATE

ANALYZE

ASSIST

LLM RAG INTEGRATIONS

What AI automation is

AI automation applies machine learning and LLM assistants to remove repetitive work: sorting and handling requests, extracting data from emails and documents, drafting replies, searching internal knowledge bases, quality control, and analytics.

We build it as a controlled production system: access rights, auditing, prompt versioning and testing, guardrails, restricted data sources, and measurable KPIs (accuracy, speed, time saved).

Speed

Reduce request handling time by 2–10× with automation and routing.

Quality

Consistent tone and answers, fewer errors, and measurable quality control.

Integrations

CRM/ERP, email, ticketing, portals, databases, APIs, and webhooks.

Security

RBAC, audit logs, data minimization, policies, and controlled context.

Who it’s for

If you have repetitive requests, lots of documents, or manual workflows, AI automation delivers quick impact: faster support, better consistency, and more capacity for your team.

Support and service desk teams (email/tickets/chat)
Organizations with high volumes of documents and submissions

Teams that need analytics on requests and operational flows
Projects where integrations and access control are critical

What we do

From pilot to production: integrations, security, metrics, documentation, and ongoing improvements.

Process audit & quick pilot

Define scenarios, data sources, risks, and KPIs. Build a pilot for 1–2 use cases.

AI assistant for user support

Auto-drafts, templates, tone control, multilingual replies, escalation, and QA.

RAG knowledge-base search

Answers grounded in your documentation: rules, manuals, FAQs, policies—with source control.

Extraction & classification

Emails/forms/docs: extract fields, classify topics, set priority, and route tasks.

Integrations & automation

CRM/ERP, service desk, email, databases, webhooks, queues, and reporting.

Security & operations

RBAC, audit logs, monitoring, testing, data policies, updates, and support.

AI STACK

RAG

TOOLS

CONTROL

Manageable prompts

Templates, versioning, A/B tests, QA checks, and security guardrails.

Reliable integrations

Queues, retries, idempotency, structured logs, and monitoring.

Key capabilities

Features that most often deliver fast and measurable results.

Request classification, routing, and prioritization
RAG answers grounded in internal documentation and knowledge
Automatic summarization for tickets, calls, meetings, and emails
Structured data extraction from documents and messages
Multilingual replies and consistent communication style
Security: RBAC, minimal context, audit trails
Quality metrics: accuracy, coverage, robustness
Automation via APIs, webhooks, schedulers, and queues

How we work

Typical path: 1–2 weeks for a pilot, 3–6 weeks for production (depending on integrations and data).

Scenarios & data

Define tasks, allowed sources, constraints, access, and KPIs.

Pilot & prototype

Build a pilot for 1–2 scenarios: prompts, RAG, integrations, and logging.

Integrations & security

Connect CRM/email/tickets/APIs; implement roles, auditing, and policies.

Quality testing

Test suite, hallucination reduction, validations, and quality metrics.

Go-live & improvement

Monitoring, optimization, updates, expansion to new scenarios, and reporting.

RESULT

SPEED

QUALITY

CONTROL

What you get

AI solution aligned to your workflows (pilot + production)
Integrations with your systems and data sources
RAG knowledge base (if needed) with access rules
Logs, monitoring, and tracked quality metrics
Documentation and operational runbooks
Roadmap for expansion and new use cases

AI automation use cases

Common scenarios and outcomes after implementation.

Automating inbound support email

−60%

Email Support LLM

Проблема

Hundreds of emails/month: manual sorting, replies, and SLA risk.

Решение

Topic/priority classification, data extraction, auto-drafts with escalation rules.

Результат

Faster response and fewer missed requests.

RAG search across policies and knowledge base

+2×

RAG Knowledge Policies

Проблема

People search docs manually; answers vary; mistakes happen.

Решение

RAG with approved sources + controlled access + references to sources.

Результат

Consistent answers, fewer errors, faster onboarding.

Operational analytics and reporting for management

+30%

Analytics Dashboard Insights

Проблема

No visibility: top topics, bottlenecks, critical issues, trends.

Решение

Semantic topics, trends, dashboards, and improvement recommendations.

Результат

Data-driven prioritization and improvement roadmap.

RISK CHECK

Common mistakes when implementing AI

Pitfalls that prevent AI from delivering results—and how we avoid them.

No clear KPIs and scenarios

“Implement AI” without defined use cases or measurable quality targets.

Последствие:

The project stays a demo with no real business impact.

Connecting everything without access rules

Giving the model overly broad access to documents and systems.

Последствие:

Higher data leakage risk and violation of least privilege.

No quality control and tests

No evaluation set, accuracy tracking, or regression tests.

Последствие:

Quality fluctuates; errors and hallucinations appear.

No logging and audit trail

Not storing requests/responses/sources/prompt versions.

Последствие:

Hard to investigate incidents and improve quality.

Ignoring integration reliability

No queues, retries, idempotency, or monitoring for integrations.

Последствие:

Processing breaks, duplicated actions, and data loss.

Security and compliance are an afterthought

No data policy, masking, retention controls, or context limits.

Последствие:

Regulatory risk and loss of user trust.

We can start with a pilot (1–2 scenarios) and define measurable metrics before scaling.

Pricing

Pricing depends on the number of scenarios, data sources, and integrations. We typically start with a pilot.

Pilot

from €900

1–2 scenarios, basic integrations, measurable metrics.

Production solution

from €2,900

Roles, logs, QA tests, and reliable integrations.

Support & improvement

from €290 / month

Monitoring, enhancements, new scenarios, SLA.

NEXT STEP

Want to validate AI impact quickly?

Describe 1–2 tasks— we’ll propose a pilot, metrics, and an implementation plan.

FAQ

Can AI be secure and answer only from our data?

Yes. We use RAG on approved sources, RBAC, audit logging, and strict context limits so answers are grounded in allowed data.

What should we start with: an assistant or analytics?

Usually with 1–2 support/document workflows where impact is easy to measure. Analytics is added in parallel once data is available.

What systems can you integrate with?

Email (IMAP/SMTP), service desk/ticketing, CRM/ERP, databases, file storage, APIs, webhooks, and queues.

How long does implementation take?

Pilot: 1–2 weeks. Production: typically 3–6 weeks depending on integrations and security requirements.

Questions? Contact us