AI Innovation Analytics Workflow with Vercel AI SDK and DeepSeek R1
Singapore enterprise AI initiatives require automated evaluation of multi-agency AI project submissions to drive sector-wide digital transformation. Build an automated evaluation workflow using Vercel AI SDK and DeepSeek R1 to analyze enterprise project proposals, quantify technical feasibility scores, and extract architectural risks into structured JSON datasets.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a Next.js / TypeScript AI analytics engine using Vercel AI SDK and DeepSeek R1 reasoning to evaluate Smart Nation enterprise AI submissions.
How work is evaluated
Evaluates structured JSON extraction and technical feasibility scoring precision using Vercel AI SDK and DeepSeek R1.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zod_schema_match
Checks if output conforms to predefined Zod schema constraints
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
reasoning_depth
Completeness of risk identification • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Utilize Vercel AI SDK generateObject and streamObject functions for structured inference
Integrate DeepSeek R1 reasoning models for deep technical proposal analysis
Analyze enterprise AI adoption metrics and collaboration models in Singapore
Build a real-time web evaluation dashboard with dynamic streaming responses
Reference links and supporting material
Synthetic enterprise AI project proposals submitted for Singapore public sector transformation grants.
Challenge setup
Evaluates structured JSON extraction and technical feasibility scoring precision using Vercel AI SDK and DeepSeek R1.
Challenge input
JSON containing project architecture specification and business goals
AI SDK
Offers typed structured output generation and UI streaming for Next.js
DeepSeek R1
Delivers extended chain-of-thought analysis for complex technical proposal evaluations
Evaluated output
JSON validated against Zod schema containing feasibility_score, identified_risks, and alignment_rating
- Checks if output conforms to predefined Zod schema constraints
- Completeness of risk identification • target: 0.9 • range: 0-1
- Benchmark: Structured Reasoning Benchmark
- Reasoning Depth target: 0.9
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Vercel AI SDK · Optional
- Credo AI · Optional
- DeepSeek R1 · Required
- Vercel AI SDK · Optional
Evaluation contract
- zod_schema_match · Weight 1
- reasoning_depth · Weight 1
Metrics
- reasoning_depth · Target 0.9
Benchmarks
- Structured Reasoning Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing project architecture specification and business goals
Output: JSON validated against Zod schema containing feasibility_score, identified_risks, and alignment_rating
Execution harness: JavaScript
Evaluation tools
- AI SDK · Client/Server AI SDK
- DeepSeek R1 · Reasoning Model
Recipe freeze marker
No recipe freeze time is recorded.