ChatGPT API for Translation Services
Budget: $30 – $250 USD
SimplyTranslate / SimplyLoc
Junior Developer Implementation Brief
Build a production-ready API that can be connected to ChatGPT for translation, certification, notarisation, apostille quotes, order creation, and order tracking.
Prepared for Junior developer / implementation contractor
Business SimplyTranslate / SimplyLoc
Primary goal Expose core business workflows as API actions callable from ChatGPT
Version v1.0 - implementation starter
What success looks like
A customer can describe their document in natural language inside ChatGPT, receive a quote in ZAR, place an order, and later check order status - all by calling your backend API.
1. Project summary
Build the first production version of a backend service for SimplyTranslate that supports the following user journey: get a quote, create an order, upload documents later, and check order status. The initial scope should be API-first. Do not spend time building a public web frontend unless it is required for admin/testing.
Business assumptions
• The main service categories are translation, certified translation, sworn translation, notarisation, and apostille-related services.
• Pricing is returned in South African rand (ZAR).
• The first ChatGPT integration target is a Custom GPT or ChatGPT app using REST actions, not a generic website chatbot.
• Operations can be partially manual behind the scenes at launch. The API must be reliable even if fulfilment is manual.
2. Scope
In scope for v1
• Backend API in FastAPI.
• OpenAPI schema kept accurate and exportable.
• Endpoints for supported languages, quote generation, order creation, and order status lookup.
• Simple persistence layer using PostgreSQL or Supabase Postgres.
• Basic admin-safe data model for quotes and orders.
• Environment-based configuration.
• Authentication for protected endpoints where needed.
• Deployment to a small always-on cloud instance.
• Logging, validation, and error handling.
Out of scope for v1
• Complex dashboard UI.
• Automated translation fulfilment pipeline.
• Complex CRM automation.
• Advanced billing/subscription logic.
• Multi-tenant architecture.
3. Recommended stack
Layer Recommendation Notes
API framework FastAPI Use Pydantic models, autogenerated docs, strong validation, and async-ready structure.
Database PostgreSQL Use Supabase Postgres or managed Postgres. Avoid in-memory storage outside local dev.
ORM / SQL SQLAlchemy or SQLModel Keep schema explicit; Alembic migrations recommended.
Auth API key initially Enough for ChatGPT action access and internal admin endpoints at first.
Storage Supabase Storage or S3 Needed once document uploads are added.
Hosting Hetzner / Render / Railway Choose the simplest always-on option with HTTPS and environment variables.
Process manager Docker + Uvicorn/Gunicorn Use containerized deployment to reduce environment drift.
4. User journeys to support
# Journey System behavior Result
1 Get quote User provides source language, target language, document type, urgency, certification type, and either word count or page count. System returns quote_id, price_zar, turnaround, notes.
2 Create order User accepts quote and provides customer name, email, optional phone. System creates order linked to quote and returns order_id.
3 Check status User supplies order ID. System returns current status and estimated delivery.
4 List languages System returns supported language pairs or languages. Useful for validation and UI assistance.
5. API specification
Required endpoints
Method Path Purpose Auth
GET /languages Return supported languages. Public or API key
POST /quote Create and return a quote. Public or API key
POST /orders Create order from quote. Public or API key
GET /orders/{order_id} Get order status. API key or secure token
GET /health Health check for uptime monitoring. Public
Request / response rules
• Use JSON for all request and response bodies except future upload endpoints.
• Return 4xx errors for bad input and 5xx errors only for true server failures.
• Every response should be deterministic and easy for ChatGPT to parse.
• Use machine-friendly field names such as source_language, target_language, certification_type, price_zar.
• Keep operationId values stable because ChatGPT actions rely on them.
6. Data model
Minimum tables
Table Key fields Description Required Notes
quotes id, source_language, target_language, document_type, word_count, page_count, service_level, certification_type, price_zar Stores generated quotes. Yes Persist for audit and reuse.
orders id, quote_id, customer_name, email, phone, status, estimated_delivery Customer order linked to a quote. Yes Use status enum.
audit_logs id, event_type, payload, created_at Basic observability and debugging. Nice to have Helpful for support.
Suggested status values
• pending - order created but not yet picked up internally
• in_review - team reviewing files or requirements
• in_progress - translation/work is underway
• awaiting_customer - customer action required
• completed - delivered
• cancelled - closed without completion
7. Pricing logic
Implement pricing in a dedicated service module, not inline inside route handlers. The initial pricing engine can be rule-based and loaded from config or a database later.
Rule Example Implementation note
Base word/page rate R1.50 per word or R180 per page Use either word_count or page_count; require at least one.
Document type multiplier Contracts higher than certificates Store as mapping, e.g. contract = 1.2.
Urgency multiplier same_day more expensive Enum-based multiplier.
Certification addon apostille adds fixed fee Fixed add-on amount.
Minimum charge e.g. R350 Apply after base price calculation.
Developer note
The exact pricing numbers can stay configurable. The important part is to design the code so business rules can be changed without rewriting the API.
8. Security and validation
• Validate all required fields using Pydantic.
• Reject unsupported languages and identical source/target combinations.
• Do not expose internal stack traces in production responses.
• Protect any admin or internal routes with API key or proper auth.
• Store secrets in environment variables only.
• Enable HTTPS in production.
• Do not log sensitive customer documents or raw personal data unnecessarily.
9. ChatGPT integration requirements
The API must be designed so it can be connected to ChatGPT through a Custom GPT action or a future ChatGPT app. This means the OpenAPI schema must be kept accurate, clear, and stable.
Requirements for ChatGPT actions
• Each endpoint should have a clear summary and operationId.
• Parameter names must be descriptive and consistent.
• Schemas must use enums where business choices are fixed.
• Responses should be concise and structured; avoid mixing narrative text with key fields.
• The backend URL and OpenAPI spec must be reachable from the public internet if used directly by ChatGPT.
Recommended action names
• getSupportedLanguages
• getTranslationQuote
• createOrder
• getOrderStatus
10. Deployment requirements
Requirement Expected implementation
Containerization Provide Dockerfile and docker-compose or simple run instructions.
Environment config Use .env.example with all required variables.
Database migrations Use Alembic or equivalent migration flow.
HTTPS Use platform SSL or reverse proxy.
Monitoring At minimum: health endpoint, error logs, restart visibility.
Backups Enable managed DB backups if using a managed database.
11. Deliverables expected from the junior developer
• FastAPI project with clean folder structure.
• Database models and migrations.
• Working endpoints matching the agreed OpenAPI schema.
• Validation and error handling.
• Local development setup instructions.
• Production deployment instructions.
• Postman collection or curl examples.
• Exported OpenAPI schema file.
• Basic test coverage for quote creation and order creation.
Suggested folder structure
app/
main.py
api/
routes_languages.py
routes_quotes.py
routes_orders.py
core/
config.py
security.py
models/
quote.py
order.py
schemas/
quote.py
order.py
services/
pricing.py
order_service.py
db/
session.py
base.py
alembic/
tests/
Dockerfile
docker-compose.yml
requirements.txt
.env.example
12. Acceptance criteria
Item Must pass Notes
POST /quote works Valid request returns quote_id, price_zar, turnaround Rejects invalid language or missing counts.
POST /orders works Creates order linked to quote Returns order_id and status.
GET /orders/{id} works Returns status for existing order 404 for unknown ID.
OpenAPI spec is correct Imports cleanly into ChatGPT actions tooling Operation IDs are stable.
Deployment is persistent App stays online after restart Health endpoint available.
13. Phase 2 roadmap (not required for v1)
• Document upload endpoint and cloud storage.
• Payment link generation.
• Email notifications.
• Admin dashboard.
• CRM integration.
• More granular pricing tables and service bundles.
14. Final instruction to the developer
Prioritize correctness, clean structure, and deployability over visual polish. Build the API as if ChatGPT is the frontend. Keep routes small, business logic separate, configuration clean, and documentation accurate. The v1 goal is not to automate the entire business; it is to expose the business's highest-value workflows in a reliable API.
Junior Developer Implementation Brief
Build a production-ready API that can be connected to ChatGPT for translation, certification, notarisation, apostille quotes, order creation, and order tracking.
Prepared for Junior developer / implementation contractor
Business SimplyTranslate / SimplyLoc
Primary goal Expose core business workflows as API actions callable from ChatGPT
Version v1.0 - implementation starter
What success looks like
A customer can describe their document in natural language inside ChatGPT, receive a quote in ZAR, place an order, and later check order status - all by calling your backend API.
1. Project summary
Build the first production version of a backend service for SimplyTranslate that supports the following user journey: get a quote, create an order, upload documents later, and check order status. The initial scope should be API-first. Do not spend time building a public web frontend unless it is required for admin/testing.
Business assumptions
• The main service categories are translation, certified translation, sworn translation, notarisation, and apostille-related services.
• Pricing is returned in South African rand (ZAR).
• The first ChatGPT integration target is a Custom GPT or ChatGPT app using REST actions, not a generic website chatbot.
• Operations can be partially manual behind the scenes at launch. The API must be reliable even if fulfilment is manual.
2. Scope
In scope for v1
• Backend API in FastAPI.
• OpenAPI schema kept accurate and exportable.
• Endpoints for supported languages, quote generation, order creation, and order status lookup.
• Simple persistence layer using PostgreSQL or Supabase Postgres.
• Basic admin-safe data model for quotes and orders.
• Environment-based configuration.
• Authentication for protected endpoints where needed.
• Deployment to a small always-on cloud instance.
• Logging, validation, and error handling.
Out of scope for v1
• Complex dashboard UI.
• Automated translation fulfilment pipeline.
• Complex CRM automation.
• Advanced billing/subscription logic.
• Multi-tenant architecture.
3. Recommended stack
Layer Recommendation Notes
API framework FastAPI Use Pydantic models, autogenerated docs, strong validation, and async-ready structure.
Database PostgreSQL Use Supabase Postgres or managed Postgres. Avoid in-memory storage outside local dev.
ORM / SQL SQLAlchemy or SQLModel Keep schema explicit; Alembic migrations recommended.
Auth API key initially Enough for ChatGPT action access and internal admin endpoints at first.
Storage Supabase Storage or S3 Needed once document uploads are added.
Hosting Hetzner / Render / Railway Choose the simplest always-on option with HTTPS and environment variables.
Process manager Docker + Uvicorn/Gunicorn Use containerized deployment to reduce environment drift.
4. User journeys to support
# Journey System behavior Result
1 Get quote User provides source language, target language, document type, urgency, certification type, and either word count or page count. System returns quote_id, price_zar, turnaround, notes.
2 Create order User accepts quote and provides customer name, email, optional phone. System creates order linked to quote and returns order_id.
3 Check status User supplies order ID. System returns current status and estimated delivery.
4 List languages System returns supported language pairs or languages. Useful for validation and UI assistance.
5. API specification
Required endpoints
Method Path Purpose Auth
GET /languages Return supported languages. Public or API key
POST /quote Create and return a quote. Public or API key
POST /orders Create order from quote. Public or API key
GET /orders/{order_id} Get order status. API key or secure token
GET /health Health check for uptime monitoring. Public
Request / response rules
• Use JSON for all request and response bodies except future upload endpoints.
• Return 4xx errors for bad input and 5xx errors only for true server failures.
• Every response should be deterministic and easy for ChatGPT to parse.
• Use machine-friendly field names such as source_language, target_language, certification_type, price_zar.
• Keep operationId values stable because ChatGPT actions rely on them.
6. Data model
Minimum tables
Table Key fields Description Required Notes
quotes id, source_language, target_language, document_type, word_count, page_count, service_level, certification_type, price_zar Stores generated quotes. Yes Persist for audit and reuse.
orders id, quote_id, customer_name, email, phone, status, estimated_delivery Customer order linked to a quote. Yes Use status enum.
audit_logs id, event_type, payload, created_at Basic observability and debugging. Nice to have Helpful for support.
Suggested status values
• pending - order created but not yet picked up internally
• in_review - team reviewing files or requirements
• in_progress - translation/work is underway
• awaiting_customer - customer action required
• completed - delivered
• cancelled - closed without completion
7. Pricing logic
Implement pricing in a dedicated service module, not inline inside route handlers. The initial pricing engine can be rule-based and loaded from config or a database later.
Rule Example Implementation note
Base word/page rate R1.50 per word or R180 per page Use either word_count or page_count; require at least one.
Document type multiplier Contracts higher than certificates Store as mapping, e.g. contract = 1.2.
Urgency multiplier same_day more expensive Enum-based multiplier.
Certification addon apostille adds fixed fee Fixed add-on amount.
Minimum charge e.g. R350 Apply after base price calculation.
Developer note
The exact pricing numbers can stay configurable. The important part is to design the code so business rules can be changed without rewriting the API.
8. Security and validation
• Validate all required fields using Pydantic.
• Reject unsupported languages and identical source/target combinations.
• Do not expose internal stack traces in production responses.
• Protect any admin or internal routes with API key or proper auth.
• Store secrets in environment variables only.
• Enable HTTPS in production.
• Do not log sensitive customer documents or raw personal data unnecessarily.
9. ChatGPT integration requirements
The API must be designed so it can be connected to ChatGPT through a Custom GPT action or a future ChatGPT app. This means the OpenAPI schema must be kept accurate, clear, and stable.
Requirements for ChatGPT actions
• Each endpoint should have a clear summary and operationId.
• Parameter names must be descriptive and consistent.
• Schemas must use enums where business choices are fixed.
• Responses should be concise and structured; avoid mixing narrative text with key fields.
• The backend URL and OpenAPI spec must be reachable from the public internet if used directly by ChatGPT.
Recommended action names
• getSupportedLanguages
• getTranslationQuote
• createOrder
• getOrderStatus
10. Deployment requirements
Requirement Expected implementation
Containerization Provide Dockerfile and docker-compose or simple run instructions.
Environment config Use .env.example with all required variables.
Database migrations Use Alembic or equivalent migration flow.
HTTPS Use platform SSL or reverse proxy.
Monitoring At minimum: health endpoint, error logs, restart visibility.
Backups Enable managed DB backups if using a managed database.
11. Deliverables expected from the junior developer
• FastAPI project with clean folder structure.
• Database models and migrations.
• Working endpoints matching the agreed OpenAPI schema.
• Validation and error handling.
• Local development setup instructions.
• Production deployment instructions.
• Postman collection or curl examples.
• Exported OpenAPI schema file.
• Basic test coverage for quote creation and order creation.
Suggested folder structure
app/
main.py
api/
routes_languages.py
routes_quotes.py
routes_orders.py
core/
config.py
security.py
models/
quote.py
order.py
schemas/
quote.py
order.py
services/
pricing.py
order_service.py
db/
session.py
base.py
alembic/
tests/
Dockerfile
docker-compose.yml
requirements.txt
.env.example
12. Acceptance criteria
Item Must pass Notes
POST /quote works Valid request returns quote_id, price_zar, turnaround Rejects invalid language or missing counts.
POST /orders works Creates order linked to quote Returns order_id and status.
GET /orders/{id} works Returns status for existing order 404 for unknown ID.
OpenAPI spec is correct Imports cleanly into ChatGPT actions tooling Operation IDs are stable.
Deployment is persistent App stays online after restart Health endpoint available.
13. Phase 2 roadmap (not required for v1)
• Document upload endpoint and cloud storage.
• Payment link generation.
• Email notifications.
• Admin dashboard.
• CRM integration.
• More granular pricing tables and service bundles.
14. Final instruction to the developer
Prioritize correctness, clean structure, and deployability over visual polish. Build the API as if ChatGPT is the frontend. Keep routes small, business logic separate, configuration clean, and documentation accurate. The v1 goal is not to automate the entire business; it is to expose the business's highest-value workflows in a reliable API.