AI for documents, contracts, and internal files
We build AI search and assistants for contracts, PDFs, instructions, spreadsheets, and internal files. The system can find a clause, compare versions, extract fields, prepare a draft, or answer from the knowledge base. Legally important conclusions and risky decisions stay with a person.
— 01 / TASKS
What ai for documents can handle
Buyers usually come here when documents have started slowing work down: files live in different places, versions have nearly identical names, staff search through private folders, and manual data extraction eats hours.
Contract search
We search by text, meaning, counterparty, dates, amounts, clauses, appendices, and related files.
Legal and operations teams find the right clause faster and can see which file it came from.
Version comparison
We help show what changed between versions of a contract, policy, instruction, or commercial proposal.
Approval work depends less on reading two similar files side by side.
Instruction answers
We connect policies, procedures, FAQ, spreadsheets, and internal pages so employees can ask in plain language.
The team gets an answer with a source instead of a colleague trying to remember where something was written.
Data extraction
We extract company details, dates, amounts, parties, statuses, numbers, payment terms, and other fields into a table or CRM.
Manual copying goes down, while uncertain fields can be sent to a person for review.
Document drafts
AI prepares a first version of a letter, response, note, or document using a template and retrieved material.
Staff start from a draft they can check instead of a blank page.
Knowledge-base assistant
We build an interface where users can ask documents questions, see sources, filter by section, and respect access rules.
Internal knowledge becomes a working tool instead of a file warehouse.
— 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.
RAG · Retrieval · US Market
Automotive RAG Assistant
An AI assistant for automotive operations: cars, service, orders, part compatibility, and internal knowledge across several data sources.
AI Agent · HR · EnterpriseMagnum HR Agent
An AI HR agent for a Kazakhstani retail chain: candidate screening, internal knowledge, vacancy fit, and recruiter workflows.
— 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 / 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 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
Yes. AI can find clauses, compare versions, extract fields, and prepare a summary. Legal judgment, risk assessment, and final approval should stay with a lawyer or responsible employee.
It is safer to treat the output as a draft analysis. The model can flag a risk and show the source, but it should not be the final authority for legally important decisions.
We extract text first, run OCR for scans when needed, split documents into chunks, build an index, and test quality on real questions. Bad scans need a separate OCR check.
Yes. Sources can be separated by role, department, project, or document type. Important access can be logged so the company knows who queried what.
The team needs a source of truth: where the current version lives, how archives are marked, and who owns updates. Without that, AI only exposes the mess faster.
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.