AI document assistant
We build an internal ChatGPT-style assistant over company documents, knowledge bases, spreadsheets, CRM, and files. The assistant finds the relevant clause, shows the source, respects access rules, and helps staff get answers without digging through folders and chats.
— 01 / TASKS
This assistant is useful when employees or customers ask questions across a large set of internal material: instructions, contracts, catalogs, policies, emails, CRM, and knowledge bases. RAG stays as the technical layer underneath, while the team gets a usable search and chat interface over company data.
We map the current "search across instructions and policies" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
The team spends less time sorting work by hand and gets clearer next steps.
We map the current "chat with company documents" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Users get faster answers while hard cases still reach a person.
We map the current "internal chatgpt" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Managers can see statuses, errors, and scenarios that need improvement.
We map the current "knowledge answers for managers" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
Data stays inside the working system instead of spreading through private chats.
We map the current "employee onboarding support" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
After the MVP, the scenario can grow without rebuilding the whole system.
We map the current "source control and citation" workflow: who asks, where the data lives, what counts as a good answer, and when a person should step in.
The company gets quality control, not just another bot.
— 02 / FIT
Custom development is useful when an off-the-shelf tool does not understand your data, access rules, systems, or responsibility boundaries.
— 03 / PROCESS
We inspect real tickets, documents, spreadsheets, and access rules.
We define where AI replies, where it acts, and where a human stays in the loop.
We build a working first version against samples from your actual workflow.
We connect CRM, messengers, databases, documents, or internal APIs.
We test on real dialogs, questions, and files, not just friendly demo prompts.
We put the system into work with clear roles, logs, and control points.
We review wrong answers, edge cases, escalations, and user behavior.
We improve scenarios after launch, once real usage starts showing the truth.
— 04 / WORK
These projects are close in shape: integrations, knowledge, operations, support, or product AI logic.
An AI assistant for automotive operations: cars, service, orders, part compatibility, and internal knowledge across several data sources.
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.
— 05 / INTEGRATIONS
Before the build, we check which systems expose APIs, where data lives, and who will keep it current.
— 06 / DATA
We design the architecture around your requirements: roles, access rules, action logs, source restrictions, and answer checks
— 07 / TIMELINE
2-3 business days when sample data and a process owner are available.
1-2 weeks for a narrow scenario with a limited integration set.
3-6 weeks when the system needs real integrations and team access.
Timeline depends on integrations, data quality, and security requirements.
— 08 / PRICING
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
A paid review of the task, data, risks, and first sensible scope.
We test the scenario on a small data set before debating it in theory.
We build a working version with UI, integrations, and basic quality control.
We harden the system for access control, logs, operations, and support.
We monitor quality, fix issues, and add new scenarios after launch.
— 09 / azamat.ai
— 09 / FAQ
We start with a short review of the workflow and sample data. Then we estimate the first stage, timeline, and risks.
We start with a short review of the workflow and sample data. Then we estimate the first stage, timeline, and risks.
We use test questions, real examples, answer logs, and escalation rules. After launch, disputed cases are reviewed.
We start with a short review of the workflow and sample data. Then we estimate the first stage, timeline, and risks.
Yes. Sources and actions can be separated by role, with logs for important document access.
— 10 / LINKS
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.