Document analysis
Document analysis is designed around your users, operational rules and measurable business objectives.
Emerging Technology
AI-Powered Software Solutions
Apply AI to real business processes with control
Apply AI responsibly to documents, search, support and content workflows.
Data
Model
Action
Classify document
Summarize thread
Route request
LLM integration
Document classification
Human-in-the-loop outputs
Data security assessment
Service overview
What are AI-Powered Software Solutions?
Apply AI responsibly to documents, search, support and content workflows.
Matched with the right problem and data, AI can speed up access to knowledge and support content-heavy work such as document analysis, classification, summarization and intelligent search.
We define human review, source citation, access rights and output quality limits, focusing on measurable business goals rather than claims that cannot be verified.
- Reduce manual work and avoidable errors
- Create one reliable source of operational data
- Improve visibility and accountability
Scope
What we build
Classification
Classification is designed around your users, operational rules and measurable business objectives.
Customer-service assistants
Customer-service assistants is designed around your users, operational rules and measurable business objectives.
Summarization
Summarization is designed around your users, operational rules and measurable business objectives.
Intelligent search
Intelligent search is designed around your users, operational rules and measurable business objectives.
Workflow support
Workflow support is designed around your users, operational rules and measurable business objectives.
Delivery model
How we work
- 01
Scenario assessment
We prioritize LLM, classification, and document needs together.
- 02
Data preparation
We organize training and reference document sets.
- 03
Prototype development
We code a limited-scope pilot flow.
- 04
Quality measurement
We measure accuracy, hallucination risk, and latency.
- 05
Human review design
We define approval steps for risky outputs.
- 06
Controlled expansion
After a successful pilot, we expand scope in phases.
System architecture
Example system architecture
We design a controlled stack from user interfaces through API, backend and data layers, with CRM, ERP, payments and third-party services connected where they belong.
Corporate documents
CRM / support
Application interface
LLM orchestration
Document processing
Classification service
Human review queue
Search index
Audit logs
Corporate documents
CRM / support
Capabilities
Technologies we use
- Node.js
- TypeScript
- PostgreSQL
- LLM APIs
- Authentication
- Cloud
- Next.js
- React
Our approach
Why Pars Medya?
Pars Medya combines business analysis, product design and software engineering in one accountable team. We communicate clearly, avoid unnecessary complexity and build maintainable systems that can grow with your organization and international ambitions.
Right problem selection
We apply LLMs to repetitive content work—not every problem needs AI.
Document efficiency
Structured information is extracted from long documents.
Classification support
Requests route by topic and priority; final decisions can stay with people.
Responsible use
We do not claim unverifiable success or ranking guarantees.
Access control
Sensitive data classification and permissions are assessed before the project.
Outcomes
What this delivers for your business
Faster information access
Semantic search across corporate documents saves time.
Assisted classification
Incoming requests pre-classify toward the right team.
Document summaries
Long contracts and reports appear as working summaries.
Human oversight
Critical outputs can pass through a review queue.
Measurable quality
Accuracy and coverage metrics are tracked during pilot.
Process-integrated AI
Classification and summary results feed into workflows.
Use cases
Where this service fits
Growing organizations
Teams replacing spreadsheets and disconnected tools with managed workflows.
Multi-team operations
Processes requiring shared data, approvals and clear responsibilities.
Digital products
Customer-facing services that need secure and scalable technology.
How we engage
From discovery to pilot, from pilot to ongoing support
Every project starts with a small, clear step. We decide on the next stage together once you have seen the results of the previous one.
- 01
Discovery workshop
We clarify goals, users, current workflows and integration needs together.
- Process and needs map
- Scope and priority list
- Technical approach and risks
- Phased roadmap and proposal
Price: quoted to your needs
Discuss this stage - 02
Fixed-scope pilot
We deliver the most valuable part identified in discovery as a pilot with scope and timeline agreed up front.
- Written scope and timeline
- Regular demos and feedback
- A tested, usable first release
- Agreed success criteria
Price: quoted to your needs
Discuss this stage - 03
Monthly support & development
After launch we monitor, maintain and keep developing the system in planned increments.
- Maintenance, updates and security checks
- Monitoring and issue tracking
- Monthly development plan
- Regular reporting
Price: quoted to your needs
Discuss this stage
FAQ
Questions teams usually ask
Are AI outputs always correct?+
No. Depending on the use case, source citation, validation, human approval and quality thresholds have to be designed in.
How is company data protected?+
Data classification, permissions, provider terms and retention policies are assessed before the project, and a suitable architecture is chosen for sensitive data.
How long does a ai-powered software solutions project take?+
Timing depends on scope, roles, integrations and migration needs. After discovery we provide a realistic phased plan.
Do you provide support after launch?+
Yes. Monitoring, maintenance and further development can continue under an agreed support model.
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Share your document, classification, or knowledge assistant need—we will propose a responsible LLM plan.