Complex cases
Information is fragmented across systems, documents, notes and communication.
Internal operational workflows
Applied AI for difficult operational work.
We build focused AI solutions for workflows where documents, cases, exceptions and human judgment still create expensive manual work.
Focused scope. Clear handover. No unnecessary platform build.
What we do
The expensive work is often between the systems.
Companies already have systems to record and manage operational work. But difficult cases still require people to interpret documents, reconcile conflicting information, apply policy, investigate unusual situations, and decide what to do when the standard rule is not enough.
Exceptions
Standard rules handle normal cases. Experienced employees handle everything else.
Operational knowledge
Important judgment exists in historical decisions but is not captured cleanly in SOPs.
Where the AI layer sits
- Existing systems
- Difficult cases & exceptions
- Evidence-backed AI analysis
- Human review
- Decision / action
Current focus
Operational exceptions are where valuable knowledge tends to hide.
Standard cases are increasingly handled by rules, software and automation. The difficult cases remain with experienced employees.
Those exceptions often contain valuable operational knowledge: why a normal rule did not apply, what evidence mattered, who authorized the decision, and whether the same situation has happened before.
Arvoryn is currently exploring how AI can help reconstruct these historical decisions and identify recurring exceptions, inconsistent decisions and gaps between documented policy and actual practice.
- 01Policy
- 02Historical cases, notes and approvals
- 03Decision reconstruction
- 04Recurring exception patterns
- 05Human review
Initial area of investigation: industrial warranty and after-sales claims.
Approach
Start with one bounded problem.
- Choose one workflow
- Define the relevant dataset
- Frame one clear question
- Build one analysis or prototype
- Hand over the result with documentation and ownership
Work around existing systems rather than replacing them. No platform commitment. No multi-year transformation program.
Typical starting point: a historical export of cases, documents, notes or decisions rather than a live production integration.
Selected technical work
Technical foundations for controlled operational AI.
Arvoryn is a new initiative built on prior applied-AI engineering work. These open-source examples show technical patterns relevant to our approach: controlled workflows, evidence retrieval, structured reasoning and human review.
- Structured reasoning
- Evidence retrieval
- Controlled workflows
- Human review
Governed AI workflows
A multi-step LLM workflow with explicit state transitions, structured evaluations, persisted outputs, reviewer feedback and human oversight. Demonstrated through an open-source interview orchestration system.
View sourceEvidence-oriented document retrieval
A two-stage retrieval architecture that first finds a broad set of potentially relevant documents and then reranks them to identify the evidence most relevant to the question. Demonstrated through an open-source document QA system.
View sourceCriteria-driven case assessment
A workflow combining structured and unstructured case information with explicit decision criteria, rationale generation and human review. Demonstrated through an open-source clinical-trial matching system.
View sourceThese projects predate Arvoryn and are shown as technical evidence rather than customer engagements.
Prior professional experience
Applied AI and machine learning beyond the demonstrations.
Before Arvoryn, founder Babak Hosseini designed and delivered applied AI and machine-learning systems in industry, including GenAI workflows, analytical assistants, recommendation systems and predictive models.
GenAI workflows
Summarization and information extraction from operational communication.
Decision-support analytics
Natural-language access to business and operational data.
Predictive ML
Recommendation and customer-behaviour models built against real business data.
Scope and principles
Principles for difficult operational work.
- Human ownership — AI proposes; domain owners decide.
- Evidence before automation — Important outputs remain traceable to source evidence.
- Existing systems stay in place — Improve the workflow layer rather than replacing the operating stack.
- Established models first — Use existing AI models and services when they are sufficient.
- Batch before real-time — Start with historical data and bounded analysis unless live integration is genuinely required.
Arvoryn is not focused on replacing core business systems, automating high-stakes decisions without human ownership, or building open-ended AI platforms.
About
Built for narrow problems. Grounded in applied engineering.
Arvoryn is an independent applied-AI initiative founded by Dr. Babak Hosseini.
Babak has a PhD in Computer Science and a background in applied AI delivery, machine learning and software engineering. His work spans GenAI systems, information retrieval, data-driven decision support and production-oriented ML.
Arvoryn applies that technical background to deliberately narrow operational problems where a focused solution can create value without requiring a large transformation program.
Contact
Have a workflow where the difficult work is still manual?
If you have a recurring operational workflow involving cases, documents, exceptions or manual review, we can start by examining one bounded problem and a small historical dataset.