Developer Centre

Everything you need to evaluate and ship an MVP integration of SolvedBy.Ai inside your SaaS, without waiting on long email threads.
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Start here

If you’re new to SolvedBy.AI, the fastest path is:
1
Read the Quick Start Guides (understand inputs/outputs and the “happy path”)
Use the API Docs + Example Datasets/Payloads (start building payloads and validating responses)
2
Use the UI Wireframes + Storyboards (turn integration into a clear UI + workflow backlog)
3
Use the FAQ Pack and other documents in your LLM (get instant answers across product, integration, security, and commercials)
4

Downloadable resources

API docs + Quick Start Quides + example datasets & payloads

Use these to implement the API integration quickly and validate your payload generator and response handling.

  • Forecasting pack - Zip
  • Demand pack - Zip
  • Scheduling pack - ZIP

How to use it:

  • Use the example payloads as the basis for your payload builder and contract tests.
  • Use the example datasets as fixtures for sandbox validation and automated regression tests.
  • Paste payload examples into your LLM to generate: types, validators, test fixtures, and negative test cases.

UI wireframes & storyboards

Use these to avoid designing “AI UX” from scratch and to speed your implementation planning. These patterns have been used in other workforce management platforms and proven with end users.

  • Navigation UI
  • Forecasting UI
  • Demand UI
  • Scheduling Basic and Required UI
  • Scheduling Advanced UI

How to use it:

  • Turn each storyboard tile into user stories and acceptance criteria.
  • Use the wireframes to identify exactly what your platform must implement: setup UI, run states, results review, overrides, publish/audit flows.
  • Paste screenshots (or the text from the wireframes) into your LLM to generate: backlog tickets, UX copy options, and QA test scenarios.

FAQ Pack

Use this if you want a complete reference for how the AIs work, integration patterns, security/compliance, commercials, and implementation responsibilities.

  • Full FAQ bank (JSON)
  • Full FAQ bank (CSV)

How to use it:
Upload the JSON/CSV into ChatGPT/Gemini (if file upload is enabled) and ask it to generate: integration plans, payload checklists, test cases, UI user stories, and commercial packaging recommendations.

Using these resources with LLMs (recommended workflow)

This partner site is designed to work alongside your existing AI‑assisted development workflow. We are continuing to develop it into a fully automated self service platform that integrates with your IDE and product management tools to speed your decision making and integration.

Suggested approach

  1. Upload the FAQ JSON/CSV into your LLM
  2. Add the relevant Quick Start PDF + the matching API ZIP examples
  3. Ask your LLM to produce:
    • an integration blueprint (data → API → UI → analytics)
    • a payload mapping plan (your entities → SBAI payload fields)
    • test fixtures + negative test cases
    • a sprint backlog derived from the wireframes/storyboards

Copy/paste prompts you can use immediately

Prompt 1: “What should we build?”

We are a [VERTICAL] SaaS. We want to embed SolvedBy.AI [Forecasting/Demand/Scheduling]. Here are our constraints and data model. Using the uploaded FAQs + Quick Start + sample payloads, generate (1) a minimum viable integration plan, (2) UI screens we must implement, (3) analytics we should capture, and (4) risks/mitigations.

Prompt 2: “Generate our integration backlog”

Using the uploaded wireframes and storyboards, create epics and user stories with acceptance criteria for: setup, run monitoring, results review, overrides, publish/audit, and reporting. Make it appropriate for [SME/Enterprise] customers and [# locations / # users].

Prompt 3: “Create a test plan from the examples”

Using the sample payloads and expected outputs, generate contract tests, fixtures, and 10 negative test cases that validate our payload generator and our UI error/empty states.

Ask your LLM to propose more prompts

At the end of any session, add:

Suggest 10 follow‑up prompts I should use to explore this partner resource pack further (commercials, security, implementation workload, analytics, and UI workflows).
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TL;DR for AI Forecasting

Need a faster answer?

If you want us to sanity-check your integration scope before you build, request a partnership call today.
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FAQ

Yes. The partner site is designed to include structured definitions that support validation and faster integration (e.g., required fields, optional fields, and common error cases). If any element is partner-specific (e.g., your own IDs and mappings), the examples show the pattern and where your values plug in.

Yes. Where relevant, the partner site includes examples for Forecasting, Demand/Requirements Planning, Scheduling (one-step and two-step), and the other “Decide” engines (e.g., stock, inventory, food production). The goal is to give your team real, working reference payloads for each workflow.

Because most integration time is lost on UX and workflow decisions, not on writing HTTP calls. The toolkit removes ambiguity by showing proven UI patterns for configuration, execution states, results review, and manual override—so partners can ship faster and avoid redesign cycles during pilots.

Yes. We provide example JSON inputs and outputs to help your engineering team implement request/response handling quickly and correctly. These examples are intended to be copy/paste friendly for payload generation, validation, and automated testing.

They are implementation references that map UI actions and states to the API interaction pattern partners need to implement. This includes a mapping matrix such as: UI action/state → API endpoint(s) → expected response(s) → UI state.

Suggested prompt:
“Using these example JSON payloads from the partner portal, generate TypeScript types + Zod validators (or C#/Python equivalents), plus unit tests and 3 negative test cases.”

The Demand endpoint converts workload/forecast data and configuration into staffing requirements per role and time interval. It is the bridge between forecasting and scheduling.

Yes. The mapping artefacts explicitly cover:

  • partial outputs
  • empty outputs
  • validation errors
  • error-flagged-but-usable response
  • …and special attention for scheduling outputs that may contain unassigned shifts, so the UI can present “what’s filled vs what needs manual completion” clearly.

Partners typically implement:

  • A payload builder that compiles supply + constraints (people/resources, contracts/skills, availability, rules) and demand/requirements
  • Review of data gaps (what you capture vs what scheduling needs) and decisions on new fields
  • Ingestion of scheduling outputs (proposals and/or allocations)
  • UI to review/approve/edit and then publish into your system of record

Yes. We provide copy/paste prompts that help you use ChatGPT or Gemini to scope integration, generate payload builders, draft test plans, and turn the portal’s wireframes into sprint backlogs.

Use the Engine name for technical scope, integration work, payloads, and platform architecture (because Engines are the modular units you embed). Use the Solution name only when you want a customer-facing description of the use case. Many partners also choose their own UI label (e.g., “Schedule Assist”) while the underlying capability remains “Scheduling AI”.

Partners typically implement:

  • A way to ingest historic time series (and incremental updates)
  • A UI/workflow to select the “feature” to forecast (e.g., sales, orders, work orders, failures) and attach optional drivers (exogenous variables)
  • A mechanism to bind datasets/fields to identifiers (location/SKU/asset/team)
  • Storage and UX to display forecasts and make them usable (charts, exports, downstream modules)
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