Crewly | Devpost

Inspiration

Tradespeople lose hours every week to “back office” work: calls/texts, finding leads, checking whether a job is even feasible, and tracking supplies. We wanted to build something that makes a solo contractor feel like they have an office manager so they can spend their time on the work they’re actually paid (and proud) to do.

What it does

Crewly is an AI-powered operations layer for blue-collar pros. Our framework:

Poke/Message

onboard tradesperson
orchestrates everything

Voice Agent

handles incoming calls from potential clients
schedules appointments
gives quotes based on materials and situation

Inventory Agent

keeps track of current inventory with cost
looks at cheapest prices using BrightData
reorders low inventory using BrowserBase Stagehand

Outreach Agent

looks on sites like Nextdoor and Craigslist to get leads on potential clients using BrowserBase Stagehand

Marketing Agent

using OpenAI API to generate business cards and brochures

Logging Agent

keeps track of client correspondence and customer satisfaction

Mapping Agent

feasibility and ordering of tasks
tracking scope of outreach

Website Agent

uses Vercel and OpenAI API to spawn and deploy company websites

Document Agent

generates and signs documents

How we built it

We built Crewly as a tool-driven agent system: a chat interface orchestrates an MCP tool server that exposes business actions like feasibility checks, pricing floor calculation, and inventory logic. We used structured tool inputs/outputs so the agent can reliably chain steps: collect job details → validate feasibility → estimate a minimum viable price → propose next actions. We also added Modal configuration within our MCP to allow for our agents to deploy secure and concurrent sandboxes to run code in.

Challenges we ran into

Tool wiring and reliability: making sure the agent passes the right units/types (miles vs meters, hours vs minutes). Environment + auth issues: handling missing API keys and avoiding circular imports in a multi-module tools package. Especially in handling environment with fragile dependencies, it was easy to run into issues when one part of the workflow was not properly set up. Keeping outputs safe and consistent: ensuring tools return predictable outputs.

Accomplishments that we're proud of

A working end-to-end flow from a single message to actionable ops steps.

Modular tools that are easy to extend (distance validation, concurrent sandbox runs, inventory hooks).

Clean tool semantics + documentation so the agent behaves more like a real assistant and less like a demo.

What we learned

Tool design matters as much as the model: clear names, tight schemas, and good docstrings dramatically improve correctness. “Ops” is a chain, not a single feature—small automations compound into real time saved. Debuggability is everything: structured outputs and health checks make iteration fast.

What's next for Crewly

Generalize beyond trades into any contractor's staff team to lead intake, quoting, scheduling, follow-ups, lightweight CRM, and parts/procurement plus tighter integrations (maps, calendars, messaging) so the AI can run more of the workflow autonomously.