Internal knowledge assistants
Help staff locate policies, instructions, and technical information through permission-aware retrieval and visible source references.
Artificial Intelligence & Intelligent Systems
We develop and integrate AI for a wide range of business needs, from local and offline models to cloud-based AI services. Our work includes intelligent agents, RAG systems, model training, fine-tuning, and specialized AI applications. We also adapt and optimize AI systems for the hardware and infrastructure available to each business.
Make organizational knowledge easier to use
A team may have the answer somewhere in its documents but still struggle to find it at the right moment. Intelligent systems can connect that knowledge to everyday work: an internal assistant, a specialized review tool, or an agent that prepares an action for approval. We begin with the business decision and the limits on data use.
Intelligence for specific business situations
Specialized AI applications
Support classification, document review, or domain-specific assistance with examples and evaluation criteria defined by the business.
Agents in existing workflows
Prepare records, gather information, and call approved tools. Keep human responsibility explicit when an action changes business data.
Controlled AI environments
Choose local, offline, online, or mixed operation according to confidentiality, connectivity, hardware, and maintenance needs.
Give the assistant the right information
An organizational assistant should respect who can see each document and how current it is. RAG provides a route from approved knowledge to a response, but source quality and retrieval evaluation still matter. A hypothetical policy assistant should point to the relevant instruction rather than manufacture a new company rule.
Choose the model around the organization
A larger model is not automatically a better business fit. We consider language, domain accuracy, available memory and accelerators, operating cost, and data boundaries. Deploying an existing model, fine-tuning it, or training a new one require different evidence, datasets, and infrastructure.
Introduce intelligence with accountable decisions
Start with a bounded use case and expand only when the evidence supports it.
Choose a business task
Identify the person, decision, information sources, and cost of an incorrect result.
Evaluate with real examples
Compare candidate approaches on representative inputs, including cases where the system should decline or ask for review.
Integrate and govern
Connect approved software interfaces, assign ownership, and plan monitoring, knowledge updates, and model changes.
Business value with operational context
Find relevant knowledge
Connect answers to the organizational sources behind them.
Support routine work
Use approved automation while preserving review boundaries.
Choose a realistic operating model
Match capability to hardware, privacy, and ownership.
Possible architectural approaches
- RAG
- AI agents
- Local / Online
- Model evaluation
- API
- Human review
Questions worth asking
Where should an organization start with AI?
Choose a repeated task with accessible information, a clear owner, and a result that can be evaluated. A bounded pilot reveals more than an undefined assistant for every task.
Can confidential information stay internal?
A local or controlled deployment may support that requirement. We assess models, licenses, tools, storage, logging, and hardware before confirming the architecture.
Does every use case need fine-tuning?
No. Retrieval, workflow design, and an existing model may be sufficient. Fine-tuning needs a specific objective, suitable examples, and an evaluation plan.
Can AI make operational changes by itself?
Only within an explicitly designed scope. Sensitive or consequential actions can require human approval, limited permissions, and recorded execution.
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Our Office in Tehran
Unit 2, No. 9, 4th Alley, Jalal Al-e Ahmad Street, Gisha, Tehran, IranClear Next Steps
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