Building a traceable multilingual learning-data supply chain
A traceable supply chain for multilingual learning data — every generated item carries lineage back to its source, with automated checks and human review before it reaches learners.
What this system does, at a glance.
The problem behind the workflow.
Learning content was assembled from many corpus and dictionary sources with no lineage. When an entry was wrong, no one could trace where it came from or which downstream lessons were affected.
Before redesign, the workflow depended on people to move information between systems, remember context and apply judgment consistently — which does not scale as volume grows.
From a fragmented process to a controlled system.
The same business outcome, re-expressed as a connected, governed workflow.
- Work spread across disconnected tools and channels
- Context re-gathered manually for every request
- No shared source of truth or audit trail
- Slow, inconsistent, error-prone execution
- One workspace with live, connected context
- AI drafts the next action, grounded in real data
- Business rules validate before anything runs
- Human approves; system executes and records it
How the system is put together.
Data & services
Connected gRPC microservices with a single source of truth per domain.
Operations workspace
A focused admin surface where the workflow actually gets done.
Controlled AI
Grounded AI that proposes, never acts alone — every action gated.
Governance
Business rules, approvals and an audit record on every change.
What the operated system looks like.
AI actions vs human approvals.
Every part of the workflow is explicit about who does what — AI, a business rule, a human, or the system.
- Reads context and drafts a proposed action or reply.
- Summarizes long histories into a decision-ready brief.
- Reviews and approves any action that changes business data.
- Overrides or edits the AI proposal before it is applied.
- Validates the proposal against business policy and scope.
- Blocks actions that fail validation and explains why.
- Executes the approved action across connected services.
- Logs every step as an auditable record.
No action that changes business data happens without a rule, an approval and a record.
Built on connected, governed services.
The system is built on Go microservices communicating over gRPC, with a MongoDB data layer and event streaming for realtime updates. Each domain owns its data and exposes an API — no direct database access, so every change is validated and traceable.
Controlled AI is integrated as a proposal layer: it reads grounded context and suggests the next action, but execution always flows through business-rule validation and human approval.
Verified before it ships.
Scoped access
Role- and permission-based access on every action and API.
Build & test gates
Compile and test gates must pass before any release.
Auditable records
Every approved action is logged as tamper-evident evidence.
Delivered and verified through Sofina.
Sofina preserved the approved features, validated completeness, enforced build and test gates, kept a human in approval, and produced delivery evidence for this system.
- 1RequirementsBusiness intent captured
- 2BlueprintValidated system design
- 3ValidateCompleteness & scope check
- 4GenerateBackend + admin generated
- 5CompileBuild gate
- 6TestTest gate
- Human ReviewApproval required
- EvidenceDelivery report
- 9DeployProduction release
Defensible, measurable outcomes.
Where this system goes next.
- Expand the proven workflow into adjacent operations.
- Add deeper AI grounding as more data connects.
- Extend approval and audit coverage to new action types.
- Grow toward a connected AI Business OS.
Want a system like this for your workflow?
Start with an AI Business Audit — we map your workflows and design the first one worth building.