Sales and inbound leads
AI parses leads from WhatsApp, Telegram, the website, email, or CRM: contact details, product, city, urgency, and next step.
Managers get a usable customer record and a suggestion instead of a raw message stream.
AI for departments
AI for departments: sales, HR, and support
We help departments remove repeated manual work where leads, candidates, tickets, documents, and CRM updates live across messengers, spreadsheets, and internal systems. The work starts with one concrete workflow, not a vague company-wide AI rollout.
AI agents, RAG, and internal tools20+ launched projectsThe team behind azamat.ai and Logic Layer LLP
— 01 / TASKS
Where AI helps departments
The best first workflows are usually where teams repeat the same actions every day: triage inbound work, search for information, move data between systems, draft replies, and hand tasks to the next person.
HR and recruiting
An agent collects candidate details, checks required criteria, answers common questions, and routes edge cases to recruiters.
Recruiters spend less time on the first mile and see qualified candidates faster.
Customer support
AI answers from the knowledge base, collects case details, detects escalation moments, and hands the operator a short summary.
Common questions close faster, while sensitive cases do not get trapped in automation.
Documents and back office
The system searches policies, contracts, PDFs, and sheets, extracts fields, prepares drafts, and shows answer sources.
Staff copy less data by hand and find the current rule faster.
CRM and statuses
AI helps fill fields, classify requests, draft follow-ups, change statuses with human confirmation, and leave an action log.
CRM gets closer to the real workflow instead of living apart from conversations.
Management and quality control
We group reports by frequent topics, errors, handoffs, delays, and knowledge-base gaps that need updates.
The department sees not only response speed, but also why the same problems keep repeating.
— 02 / FIT
When a department needs custom AI
Custom development starts to make sense when a basic bot or CRM automation is no longer enough: data lives in several systems, access rules matter, language matters, answer quality matters, and a person still needs to take over at the right moment.
- The department manually triages leads, candidates, tickets, documents, or statuses every day.
- CRM, WhatsApp, Telegram, email, documents, spreadsheets, or an internal API need to work together.
- AI should prepare the next step: a record, draft, status, summary, or handoff to a person.
- Leadership needs logs, quality control, and clear rules for where automation must stop.
— 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
Related department workflow work
These projects are close in shape: teams, requests, messengers, documents, roles, CRM, human handoff, and AI logic over real workflows.
Olzhas — Magnum Knowledge Base
An internal AI assistant for Magnum employees: ask a question in plain language, get an answer pulled from the company knowledge base.
- Built
- Conversational assistant, RAG pipeline over internal knowledge, admin panel for the operations team, onboarding flows.
- Stack
- Python backend · RAG · Multilingual LLM · Admin panel
Magnum Notifications & LMS
Internal notification platform for Magnum: training reminders pulled from the LMS, plus targeted corporate broadcasts to specific stores, departments and offices.
- Built
- LMS sync, training notifications on Telegram and WhatsApp, escalation to managers, admin panel for segmented broadcasts.
- Stack
- Python backend · Telegram · WhatsApp · LMS integration (Moko, Magnum Space) · Admin panel
Kaizen Club · TheNext
AI infrastructure for a three-day business summit in Abu Dhabi — one Telegram Mini-App that carries every attendee from the first click on a ticket to materials after.
- Built
- Telegram Mini-App, sales bot, QR access and smart seating, Kai Tinder networking, AI business diagnostic with PDF, interactive AI avatars, AI talk recaps, custom admin panel.
- Stack
- Telegram Mini-App · Telegram Bot · HeyGen · ElevenLabs · Multi-LLM · Stripe + TipTopPay + PayForm · Bitrix CRM
Compass
A supplier onboarding workflow for Compass, focused on moving operational steps out of scattered manual coordination.
- Built
- Structured onboarding flow, internal workflow logic, status handling, and operator-facing product surfaces.
- Stack
- Workflow product · Internal tools · Operations
— 05 / INTEGRATIONS
Integrations
We usually connect CRM, WhatsApp, Telegram, email, knowledge bases, documents, spreadsheets, internal APIs, and AI services where they genuinely remove load from the department.
CRMWhatsAppTelegramGoogle SheetsNotionAirtable1CBitrix24amoCRMPostgreSQLSupabaseOpenAIAnthropiccustom APIvector databases
— 06 / DATA
Security and data handling
We design the architecture around your requirements: roles, access rules, action logs, source restrictions, and answer checks
- Not every data source has to be sent to a public model. Some logic can stay inside your infrastructure.
- Document access and agent actions can be restricted by role.
- For important decisions, we add human-in-the-loop review: AI prepares the answer or draft, a person confirms it.
- Test environments stay separate from production, so scenarios and prompts can be checked safely.
— 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 start with the department and workflow, not with buying a model or polishing a demo.
- We can connect LLMs, CRM, messengers, documents, interfaces, and access control.
- We design human-in-the-loop where mistakes cost money, reputation, or legal risk.
- The founder stays involved in architecture and hard product calls.
- Our work covers HR, support, knowledge bases, notifications, events, education, and customer workflows.
— 09 / FAQ
FAQ
Which department should we start with?
Usually the one with repeated inbound work: sales, support, HR, or back office. We pick one workflow where the effect can be checked on real data quickly.
Is this a separate bot or part of our CRM?
It depends on the workflow. Sometimes a WhatsApp or Telegram agent is enough. Sometimes AI should sit inside CRM, a work panel, or an internal tool so the team does not switch between windows.
Can it connect to our current CRM or database?
Yes, if there is an API, export, webhook, or another reliable integration path. During discovery we check constraints, data quality, permissions, and the source of truth.
How do we keep control over AI answers?
For risky topics we add escalation rules, human-in-the-loop, action logs, answer sources, and test examples. AI helps, but should not make expensive decisions on its own.
What should we prepare before starting?
The best inputs are 30-100 real requests, conversations, or documents, a list of systems, staff roles, handoff rules, and examples of good team answers.
— 10 / LINKS
Let’s discuss the task
Tell us which department, inbound flow, and systems hold the work today. We will suggest one first AI workflow that can be tested without a vague enterprise rollout.