AI & Automation

Automation and AI for Operational Efficiency

Eliminate manual work in processes and deploy models where there is a volume of requests, documents, and inquiries — with measurable impact.

We don’t sell “GPT implementation”. We design the department’s task, data, quality control, and the point where a human remains in the loop. Pilot → metrics → scale.

stack

RAG
agents
classification
documents
workflow
eval

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CaseIntegrated in 10 companiesAI / ERP

Our Product

AIDIR

AI Director runs the company: tasks, processes, analytics

The most powerful solution on the market for business process automation.

X‑Labs’ own product: we built AIDIR and continue to develop it ourselves — this is not a third‑party platform implementation.

AI platform for business management: CRM, sales funnel, tasks, documents and finance — with AI Director acting as an operational manager.

The central module of AIDIR – an operational director following the “human in the loop” (HITL) principle: the system proposes, a person approves, then the system acts.

CRM – deal funnel
Analytics

Tasks
Projects, sub‑tasks, time tracking and manager review — integrated with CRM and goals.
Processes
Unified CRM and ERP layer: sales, documents, finance, inventory, personnel and inbound from WhatsApp and Telegram.
Analytics
Owner dashboard, funnel, period comparison and reports. AI Director gathers signals and proposes a plan.

Request Demo Learn More About AIDIR Discuss Implementation

01
Signal
Monitors goals and KPIs: schedule lag, stuck metrics, inactive owner, signals from CRM and tasks.
02
Plan
Prepares a structured recovery plan for the goal – not fragmented advice, but a concrete “propose solution”.
03
Approval
Owner or administrator approves the plan or rejects it with a reason. Without a human, the system does not act.
04
Execution
After approval, tasks are assigned to executors, deals in CRM move to the next step. Every decision is recorded in the audit.

Why This Has Its Own Page

Rule-based automation and AI models are different tools. Often both are needed: rules cover 80% of the flow, models handle the unstructured tail. Here’s a map of tasks and outcomes, without hype.

Where It Delivers Impact

01
Support and Service
Faster first response, reduced load on L1, more consistent answer quality.
- — Classification and routing of inquiries
- — Draft responses with operator review
- — Search across knowledge base and policies
- — Summaries of long threads and tickets
02
Sales and Account Management
Fewer “forgotten” leads, higher follow‑up speed.
- — Lead qualification using rules + model
- — Guidance on next steps in CRM
- — Preparation of client briefs from deal history
03
Operations and Back‑Office
Less manual entry, fewer accounting errors.
- — Extracting data from invoices, acts, requests
- — Auto‑filling records and completeness checks
- — Escalations per SLA
04
Logistics and Field Teams
Higher utilization, clearer deviation visibility.
- — Prioritizing requests
- — Route suggestions (where data exists)
- — Analysis of deviations from plan
05
HR and Internal Services
Reduced load on HRBP and helpdesk.
- — Onboarding Q&A on policies
- — Routing internal requests
- — Draft job descriptions / letters (with review)

When to Use Rules vs Models

	Automation (rules / workflow)	AI (models / agents)
Better for	Clear statuses, forms, approvals, schedules	Text, documents, ambiguous queries, meaning search
Predictability	High	Requires monitoring and quality assessment
Data	Structured	Text, files, dialogue history
Risk	Rule errors	Hallucinations, leaks, drift

We start with the process. We add a model when rules hit unstructured data or variability.

Integrate a Model or Build Your Own

Integrate models / APIs — When

Standard task (summary, classification, draft response)
Pilot needed in weeks, not months
Domain does not require unique first‑token precision
Human‑in‑the‑loop is possible

Custom / fine‑tuning / own pipeline — When

Strict quality and audit requirements
Industry‑specific language or internal jargon
Data cannot be sent to external APIs without a secure environment
Agents with tools and policies are needed

We offer both approaches — and help choose based on timeline, risk and TCO, not model hype.

From Pilot to Production

01
Task and Metric
What we change: time, error rate, processing cost, CSAT.
02
Data and Access
Sources, quality, PII, policies.
03
Pilot on a Narrow Scenario
1–2 streams, baseline vs after.
04
Quality Control
Evaluation set, thresholds, escalation to human.
05
Process Integration
UI in the work system, monitoring, rule adjustments.

What We Discuss in the Demo Results

Processing time per inquiry / document
Share of auto‑closed or auto‑routed cases
Manual entry errors
Load on L1 / back‑office
Time to first response
Process transaction cost (benchmark)

Is this an implementation of an off‑the‑shelf model “as is”?

No. First we define the department’s task and metric. Then we use a ready API, fine‑tuning or rules. We never launch a model into production without a quality framework.

Can we start with a pilot?

Yes. Typically 1–2 streams, baseline vs after, evaluation set and escalation threshold to a human. After that – a decision on scaling.