Solution entry · Agents and AI
Deal to Glory
A single-commit hackathon MVP that visualizes a deterministic, agent-styled seller workflow from voice or text input through CRM, illustrative attainment, recognition, and simulated notification stages.
What changed in the design.
Customer-neutral framing keeps the pattern reusable without claiming private results.
Before the solution
A spoken seller update may need to be translated into structured CRM intent, an opportunity action, an illustrative attainment view, recognition language, and a communication payload across separate steps.
What was built
The public MVP implements a sequential TypeScript pipeline with keyword-based intent extraction, a mock or configured Dataverse opportunity stage, illustrative commission and attainment calculation, recognition copy, simulated notification output, Express Server-Sent Events, and browser speech or text input.
Value enabled
The prototype is designed to make a multi-stage seller workflow visible as one narrated sequence, not to establish autonomous AI behavior, production safety, or measured seller impact.
How the public pattern fits together.
Voice or text input moves through deterministic TypeScript stages, a mock or configured CRM and calculation step, and a simulated notification.
The browser captures a seller update through Web Speech or typed input.
Sequential TypeScript stages extract intent and compose the workflow.
A mock or configured Dataverse action is followed by illustrative attainment logic.
Recognition and notification payloads are composed, but the Teams-style send is simulated.
What the project demonstrates.
- Accepts browser Web Speech or text input and streams stage progress with Server-Sent Events.
- Uses deterministic keyword extraction rather than an external AI model for deal intent.
- Runs a mock path or an optional configured Dataverse opportunity update.
- Produces illustrative attainment, recognition, and simulated notification payloads.
- The journal labels the stages deterministic and agent-styled rather than claiming five autonomous AI agents.
- Attainment and commission values are illustrative rather than financial evidence.
- A real Dataverse deployment should add an explicit approval gate before any mutation; the current optional write path does not provide one.
- When Dataverse is configured, speech completion starts the pipeline and the opportunity stage can write without an explicit user approval gate.
- The notification endpoint logs intent and returns success without sending a real Teams message, while the payload marks the channel simulated.
- The server enables unrestricted CORS with no authentication middleware, and browser what-if values are inserted through innerHTML without escaping.
- Date handling is hard-coded around 2026, and the repository has no LICENSE file even though package.json says MIT.
Follow the evidence.
These public sources support the entry. No private customer material or internal evidence was used.
- Deal to Glory public repository Public orchestrator, voice-intake, opportunity, notification, server, browser, package metadata, README, and single-commit history support this entry.
- GitHub public repository metadata Repository created 2026-02-12; last public push 2026-02-12. These are repository facts, not adoption evidence.
- Attribution: Express Upstream web-server dependency; not owned by this repository. License: MIT in package metadata.
- Attribution: @azure/identity Upstream Azure identity dependency used by the optional Dataverse path. License: MIT in package metadata.
- Attribution: TypeScript Upstream language and build dependency; not owned by this repository. License: Apache-2.0 in package metadata.