AI SYSTEMS & PRODUCT ENGINEERING · HONG KONG
Polymath Productions designs AI knowledge workflows, custom SaaS and connected business software for established B2B teams serving Hong Kong. We begin with the workflow, existing systems and people who own each decision. Delivery is remote; this page does not imply a Hong Kong office.
This is Polymath Productions at reglerproductions.com, led by Paul Konstantin Regler and Yegar Sahaduta.
BUYER FIT
The typical fit is an established B2B company with recurring work, valuable internal knowledge and separate systems. A useful project has a workflow owner, access to the relevant data and a result that can be checked.
For teams that need to search approved policies, contracts or project records, a retrieval-augmented generation (RAG) workflow can return an answer with source passages for review. The evaluation set should include the languages actually used in the documents and questions. Permission filtering and stale-document handling are design requirements.
For work split across CRM, ERP, email and documents, an integration can prepare a next action, record status and route exceptions to a named owner. APIs, identity and vendor terms decide what is feasible. Writes to a system of record can remain behind human approval.
A B2B portal or internal platform can bring role-based access, dashboards and APIs into one workflow. The first release should serve a defined user group and process; a white-label version makes sense only when branding and operating responsibilities are part of the product brief.
Production work needs failure alerts, quality checks, integration maintenance and cost visibility. Managed AI engineering provides separately scoped ongoing capacity for evaluation and improvement after the initial system is delivered.
DESIGN QUESTIONS
List the documents and systems in scope, their owners and the permissions each user should inherit. Decide how records are refreshed and which information must stay outside the model workflow.
Identify actions that require a person to approve, reject or correct them. Define what happens when a source is missing, an API fails or an answer lacks evidence.
Agree on representative questions or cases, acceptance measures, logs and owners. Review real examples before widening access. Set responsibility for monitoring, costs and maintenance after launch.
Illustrative brief: a team asks a question over approved project files, reviews the cited source passages, and sends an approved update to its CRM. This describes a possible scope, not a delivered customer result.
PROJECT SCOPE
An architecture sprint maps the workflow, access, integration risks and a pilot plan. A proof of value tests a bounded use case. Production work adds the controls, operations and handover needed for daily use. SaaS and white-label products require a separate product scope.
NEXT STEP
QUESTIONS
Potentially, if suitable interfaces, access and vendor terms exist. We map data and approval boundaries before implementation and include exception handling in the scope.
It can be evaluated against the languages present in your approved material. Retrieval quality, permissions and answer evidence need to be tested on representative questions before a production commitment.
No Hong Kong office or blanket regulatory certification is claimed. Delivery is remote. Legal, security and data handling requirements are reviewed for each project with the client.
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