AI for HR and recruiting
AI in HR is useful for the boring first mile, not for replacing recruiters: collect a form, check must-have requirements, answer a candidate, find a policy, remind a recruiter about the next step. Final people decisions stay with people.
— 01 / TASKS
What ai for hr can handle
The buyer here is usually tired of repetition: the same candidate questions, incomplete forms, manual requirement checks, employee questions, and onboarding material scattered across documents.
First screening
The agent collects basic details, checks must-have requirements, asks for missing information, and sends the recruiter a structured candidate card.
Recruiters read a short summary with flags and follow-up questions instead of a raw chat.
Candidate replies
AI answers common questions about schedule, location, documents, stages, and status without making promises a person should confirm.
Candidates get simple information faster, and recruiters stop writing the same reply all day.
HR knowledge base
We build search across policies, benefits, leave, sick days, onboarding, and internal rules with role-aware access.
Employees can find an answer without digging through old PDFs and chat threads.
Onboarding
AI guides a new hire through the checklist, answers from company material, and shows HR where the person is stuck.
Onboarding depends less on one manager remembering every small step.
Employee questions
We automate the first line of internal HR questions while sending disputes, personal cases, and sensitive topics to people.
The HR team sheds routine work without losing control of delicate situations.
Recruiter notifications
We configure reminders for incomplete forms, overdue replies, new candidates, and manual checks.
The process depends less on someone noticing a chat message in time.
— 02 / FIT
When custom AI is worth it
Custom development is useful when an off-the-shelf tool does not understand your data, access rules, systems, or responsibility boundaries.
You have specific documents, CRM fields, roles, branches, or internal rules.
Several systems must be connected while keeping a clear source of truth.
Action logs, testing, and control over disputed answers matter.
You need a working prototype first, then a careful path to production.
— 03 / PROCESS
What the build includes
Task and data audit
We inspect real tickets, documents, spreadsheets, and access rules.
Scenario design
We define where AI replies, where it acts, and where a human stays in the loop.
Prototype
We build a working first version against samples from your actual workflow.
Integrations
We connect CRM, messengers, databases, documents, or internal APIs.
Testing
We test on real dialogs, questions, and files, not just friendly demo prompts.
Launch
We put the system into work with clear roles, logs, and control points.
Quality monitoring
We review wrong answers, edge cases, escalations, and user behavior.
Support and iteration
We improve scenarios after launch, once real usage starts showing the truth.
— 04 / WORK
Relevant case work
These projects are close in shape: integrations, knowledge, operations, support, or product AI logic.
AI Agent · HR · Enterprise
Magnum HR Agent
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
AI Agent · Events · Participant SupportAlmaty Marathon
An AI support agent for one of the largest running events in Central Asia, answering participant questions and routing edge cases.
— 05 / INTEGRATIONS
Integrations
Before the build, we check which systems expose APIs, where data lives, and who will keep it current.
CRMWhatsAppTelegramGoogle SheetsNotionAirtable1CBitrix24amoCRMPostgreSQLSupabaseOpenAIAnthropiccustom APIvector databases
— 07 / TIMELINE
Timeline and working format
Fast audit
2-3 business days when sample data and a process owner are available.
Prototype
1-2 weeks for a narrow scenario with a limited integration set.
MVP
3-6 weeks when the system needs real integrations and team access.
Production
Timeline depends on integrations, data quality, and security requirements.
— 08 / PRICING
Pricing
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
Discovery
A paid review of the task, data, risks, and first sensible scope.
Prototype
We test the scenario on a small data set before debating it in theory.
MVP
We build a working version with UI, integrations, and basic quality control.
Production system
We harden the system for access control, logs, operations, and support.
Support
We monitor quality, fix issues, and add new scenarios after launch.
— 09 / azamat.ai
Why azamat.ai
We build AI systems around real operations, not a polished demo prompt.
We can connect LLMs, retrieval, product interfaces, CRM, messengers, and internal APIs.
The founder stays involved in architecture and key decisions.
Our case work covers HR, RAG, events, education, mobile AI products, and internal tools.
We work with teams in Kazakhstan, Central Asia, the US, and Europe.
— 10 / FAQ
FAQ
No. AI can collect information, flag mismatches, prepare a summary, and suggest questions. Decisions about hiring, rejection, transfer, or disciplinary topics should stay with a person.
We define must-have requirements and data sources first. The agent compares candidate answers with those rules, marks gaps, and gives the recruiter an explainable summary.
Yes, if there is a reliable integration path. We discuss message templates, consent, conversation storage, and when the dialog should move to a recruiter.
We use an approved knowledge base, test questions, answer logs, and escalation. For salary, conflicts, and personal data, manual confirmation is usually the right default.
Yes. Kazakhstan teams often need Russian and English, sometimes Kazakh for specific flows. We test quality on real questions, not just polished demo prompts.
Tell us what you're building.
Start with a few detailsWe reply within one business day. Then Azamat joins every first call personally, so you get an honest scope, budget, and fit from the person responsible for delivery.