Inbound lead processing
AI reads the request and extracts need, contact details, product interest, urgency, and missing questions.
The manager understands what to do with the lead and what first reply to prepare.
AI for Sales · Kazakhstan
AI for sales teams
AI for sales should help a manager understand the lead, next step, and risk of losing the deal faster. It should not turn the team into an automated spam machine. We usually start with inbound leads, CRM statuses, conversation history, and the rules good managers already follow.
AI agents, RAG, and internal tools20+ launched projectsThe team behind azamat.ai and Logic Layer LLP
— 01 / TASKS
What ai for sales can handle
The buyer here cares about revenue and sales discipline: leads wait too long, CRM fields are messy, follow-ups get forgotten, and conversation quality appears only after a call review or complaint.
Lead classification
We configure rules for segment, priority, source, deal size, and likely next step.
Strong leads do not drown in noise, and sales leads can see inbound quality more clearly.
Manager suggestions
AI suggests arguments, questions, risks, and materials based on the product, customer history, and deal stage.
New managers reach a decent conversation faster, while experienced managers spend less time preparing.
Follow-up drafts
We prepare message drafts after a call, meeting, or quiet period, matching the tone, stage, and promises already made.
Follow-up no longer depends on one person finding a free minute and remembering every detail.
CRM control
We check empty fields, stuck statuses, mismatches between messages and CRM, and overdue tasks.
The funnel gets cleaner without a daily manual audit of every record.
Dialog analysis
We identify common objections, risky promises, missed questions, and moments where a manager needs coaching.
Sales leads see more than CRM numbers. They see the quality of the actual conversation.
— 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.
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
Magnum HR Agent
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
- Built
- Conversational agent, admin panel, knowledge ingestion, eligibility logic, age logic, notification flows.
- Stack
- Internal HR systems · Infobip · WhatsApp · Multilingual LLM
— 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
— 06 / 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.
— 07 / 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.
— 08 / 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.
— 08 / FAQ
FAQ
Can AI connect to CRM?
Yes, if the CRM has an API, webhooks, export, or another stable exchange path. Early on we define which fields AI may fill directly and which ones it should only suggest to a manager.
Will AI write to customers automatically?
It can, but it should not always do that. For first launches, draft mode is often safer: AI prepares the message, the manager reviews and sends it. Auto-replies fit narrow, tested scenarios.
How do we control dialog quality?
We need criteria: response speed, qualification completeness, promise accuracy, next step, and tone. AI can flag issues, while important cases should still be reviewed by a sales lead.
Can it process WhatsApp?
Yes. We choose the integration, account for templates, limits, consent, and manager handoff. It is especially important not to mix employees' private chats with the work system.
What do we need to start?
A lead export, examples of good and bad conversations, funnel stages, CRM fields, and the rules a manager should follow after first contact are enough for a useful first review.
— 09 / LINKS
Let’s discuss the task
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.