AI document assistant: internal ChatGPT and contract review
We build an internal ChatGPT over company documents, knowledge bases, spreadsheets, and CRM. The assistant answers with a link to the source and respects access rules. It also works on specific files: checks a contract against your playbook, extracts parties and dates, compares versions, reads scans, and prepares a first draft. Legally significant decisions stay with a person.
What an AI document assistant can handle
The assistant is useful when staff spend hours hunting for answers in policies, contracts, spreadsheets, and CRM, or when a team reads the same kinds of documents by hand. In the first case it answers questions over the knowledge base; in the second it reviews a specific file and extracts data from it. RAG stays as the technical layer underneath. Policy search is easy to test on real employee questions, so an assistant like this is often the first step of AI implementation in a company.
Internal ChatGPT over the knowledge base
Staff ask in plain words about policies, instructions, spreadsheets, and CRM. The assistant builds an answer from several documents, respects access rights, and helps new hires get up to speed without asking a manager.
Answers come with a link to the clause, not a colleague trying to remember.
Contract review against your playbook
We check a contract against your list of acceptable terms: payment terms, penalties, liability, jurisdiction, auto-renewal. The assistant flags deviations and links to the clause.
The lawyer starts from a marked-up contract and decides which risks to accept.
Field and term extraction
Parties, amounts, dates, numbers, payment terms, and other fields go into a spreadsheet, 1C, or CRM.
Manual copying goes down, while uncertain fields go to a person for review.
Version comparison
We show what changed between versions of a contract, policy, or commercial proposal, and which edits change the meaning.
Approval no longer depends on reading two similar files side by side.
Scans and PDFs
We run OCR on scans and tables and test recognition quality on your documents before launch.
The paper archive becomes searchable, and problem scans show up early.
Drafts of letters and documents
AI prepares a first version of a letter, reply, memo, or redline from a template and retrieved material.
Staff edit a draft they can check instead of starting from a blank page.
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.
What the build includes
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Task and data audit
We inspect real tickets, documents, spreadsheets, and access rules.
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Scenario design
We define where AI replies, where it acts, and where a human stays in the loop.
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Prototype
We build a working first version against samples from your actual workflow.
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Integrations
We connect CRM, messengers, databases, documents, or internal APIs.
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Testing
We test on real dialogs, questions, and files, not just friendly demo prompts.
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Launch
We put the system into work with clear roles, logs, and control points.
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Quality monitoring
We review wrong answers, edge cases, escalations, and user behavior.
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Support and iteration
We improve scenarios after launch, once real usage starts showing the truth.
Relevant case work
These projects are close in shape: integrations, knowledge, operations, support, or product AI logic.
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.
AI CRM · RAG · US marketAI CRM
An AI CRM for a US car-dealership network: cars, service, orders, and part compatibility. The whole value is in retrieval and ranking quality.
AI Infrastructure · Telegram Mini-App · EventsKaizen Club · TheNext
AI infrastructure for Margulan Seisembai's business summit in Abu Dhabi — one Telegram Mini-App: tickets, entry, networking, AI avatars, and business diagnostics.
Integrations
Before the build, we check which systems expose APIs, where data lives, and who will keep it current.
- CRM
- Telegram
- Google Sheets
- Notion
- Airtable
- 1C
- Bitrix24
- amoCRM
- PostgreSQL
- Supabase
- OpenAI
- Anthropic
- Custom API
- Vector databases
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.
Timeline and pricing
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
Document search
An assistant over your policies and knowledge base, answers with a source link, role-based access, a test set
from ₸1,500,000 · 3–5 weeks
AI in a CRM or 1C
One feature inside the system: request parsing, draft replies, document sorting
from ₸700,000 · 1–2 weeks
Support
Every month we run the test set, update the knowledge base and review disputed answers
₸100,000–500,000 per month
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.
Frequently asked questions
Regular search finds files by keyword, and someone still has to read them. The assistant understands a question asked in plain words, finds the relevant passages across several documents, writes an answer, and shows where it came from. If the sources do not contain the answer, it says so instead of making one up.
Yes. The assistant checks a contract against your playbook: payment terms, penalties, liability, jurisdiction, auto-renewal. It flags deviations with a link to the clause, extracts key fields, and prepares a summary. Risk decisions and final approval stay with a lawyer.
The assistant produces a draft analysis. The model flags a risk and shows the source, but the final word on legally significant decisions stays with a lawyer or a responsible employee.
We extract the text first, run scans through OCR, split documents into chunks, and build an index. Quality is tested on real questions from your staff. Poor scans get a separate check: sometimes rescanning part of the archive is cheaper than fighting recognition errors.
We agree on a source of truth: where the current version lives, how archived ones are marked, and who owns updates. The index refreshes automatically, and each answer shows the document date. Without that order, AI only exposes the existing mess faster.
Before launch we build a set of real questions with reference answers and run the assistant against it after every change. After launch, disputed answers from the log are reviewed and added to the test set.
Yes. Sources can be separated by role, department, project, or document type. Important access can be logged so the company knows who queried what.
Useful reading
Let’s talk
Send the workflow, the data sources, and the systems you need connected. We will estimate a practical first stage without hand-waving.
I read every request myself.