Company knowledge assistant
Finds answers in instructions, policies and internal knowledge, respecting access permissions. Shows the materials behind the answer.
An answer with a sourceAI implementation
AI that helps with everyday work
Help your team find answers, work through documents and prepare customer replies. We bring AI into your tools with clear sources and checks on the result.
We start with a real task and test it on your examples.
Where AI can help
The knowledge you need, when you need it
Document processing
Extracts agreed fields from incoming files, prepares summaries and helps spot missing information. Uncertain data goes to review.
Structured informationSupport and correspondence
Identifies the enquiry topic and drafts a reply using company rules. A person reviews and sends it, handling complex cases directly.
A prepared draftAI within your workflows
Connects text processing to your CRM, accounting or internal service. Actions and permissions stay within the agreed scenario.
The next step in your systemExplore the assistant’s logic
An answer needs a source you can check
Choose a question to see how an assistant can use an internal instruction. The source stays alongside the answer.
Example internal document
Enquiry handling instructions
Enquiries from the website form are recorded in the CRM. The department head assigns the responsible manager. If a question needs technical expertise, the manager refers it to the appropriate specialist and maintains contact with the customer. A team member reviews external replies before sending.
Assistant’s answer
Where do we record an enquiry?
Record an enquiry from the website form in the CRM.
Enquiries from the website form are recorded in the CRM.
Enquiry handling instructions
Interactive illustration with prepared answers. This is a fictional instruction, not Hayrat’s operating terms.
Control is part of the design
A capable tool. Clear boundaries.
Models can make mistakes. We design useful responses alongside rules for verification, access and uncertainty.
Sources and verification
Knowledge workflows include references to documents. We test accuracy, completeness and what happens when an answer is missing.
Role-based access
We agree which data the model and each person can access, where it is processed and which external providers are involved.
Human participation
We define what AI may do independently and where approval is required. Important external actions have agreed controls.
Selected work
Solutions from practice
Solution diagram
01Documents02Semantic search03Sourced answer
Service company knowledge base
Find answers in policies, instructions and resolved tickets.
Solution diagram
01Sales02Time series03Forecast
Purchase forecasting
Plan a clothing store’s assortment using demand data.
Solution diagram
01Question02Answer retrieval03Consultation
Sales assistant
Match answers to questions from a TV channel’s audience.
How we implement
From a useful idea to a working tool
Choose the task
Review the process, materials and current workflow. Agree what AI should improve and how we will check it.
Test real examples
Build a prototype using an agreed sample. Examine good answers, errors and limitations before wider integration.
Integrate into work
Connect the solution to the necessary sources and tools. Set up permissions, output checks and failure handling.
Hand over to the team
Show people how to use the tool and spot mistakes. Hand over the agreed materials and define support arrangements.
Your team receives
- A working scenario in the agreed tools
- Quality criteria and evaluation examples
- Instructions, access and agreed source code
- Visibility into model and external service costs
The Hayrat approach
Useful, with responsibility
We assess the task and the use of AI in light of our Islamic principles. In contractual relationships, we are guided first by Sharia principles while complying with applicable law. Scope, limitations and obligations are made clear.
Before we begin
Do we actually need AI?
Not for every task. If search, clear rules or a conventional integration are sufficient, we will say so during discovery. AI makes sense where its benefit can be tested on real examples.
Will you train a custom model?
That is not always the first step. A suitable existing model connected to permitted sources can often address the task. We choose the model, data approach and any need for further training after discovery.
Where will our documents go?
Before connecting data, we agree on architecture, providers and processing terms. We assess local or private hosting against the task, budget and quality requirements. The same option will not fit every project.
How do you evaluate quality?
We agree on examples and task-specific criteria: answer accuracy, source references, extraction completeness or other measures. We also test difficult cases and situations with insufficient information.
How is pricing structured?
Development and implementation are estimated separately from anticipated model, storage and external service costs. Actual consumption depends on data volume, requests and the chosen solution.
Where could your team use some help?
Describe repetitive work involving text, documents or enquiries. Together we will decide whether AI is a useful fit.
A process description is enough to begin. Confidential documents can follow once the terms are agreed.