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Meerir MVP: AI Airspace Decision-Support Platform

Services Covered:

AI and Machine Learning • Large Language Model (LLM) Development • Data Engineering
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Industry
Industry

Aviation

Duration
Duration

3 months

Budget
Budget

200K - 999K

Start Date
Start Date

1 June 2025

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
81/100
Strong

Project Capability Score (PCS) estimates how capable HatchWorks AI is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

DronePort Network

mapOklahoma City, United States of America

Project Summary

HatchWorks AI designed, developed, and deployed the Meerir MVP for a commercial drone infrastructure company, ingesting ADS-B Exchange air traffic data and establishing data pipelines and a warehouse. The MVP integrates a large language model and chatbot interface to allow natural-language queries of air traffic data, and was managed via an agile delivery model with sprints and regular demos. The client praised the team's technical quality, responsiveness, and project management.

Key Challenges

  • Build an AI agent to analyze aviation data

Project Deliverables

  • Meerir MVP (production-ready)
  • Batch ingestion of ADS-B Exchange (Wingbits) air traffic data and data pipelines
  • Data warehouse and structured analytics layer
  • Large language model integration and chatbot interface for natural-language queries
  • Embedded visualizations returned with chatbot responses
  • Public-facing landing page and basic user management (sign-up, authentication)
  • Deployment to the client's GCP environment and QA/UAT

Project Solution

HatchWorks AI designed, developed, and deployed the Meerir MVP over a 16-week engagement. Work included ingesting and batch-processing ADS-B Exchange air traffic data across multiple geographies, establishing data pipelines and a warehouse, integrating a large language model to interpret natural-language queries, and building a chatbot that returns text responses with embedded visualizations. The scope also covered a public landing page, basic sign-up and authentication with single-role access, core data visualizations, QA and user acceptance testing, and production deployment to the client's GCP environment following a crawl-walk-run roadmap.

Project Outcome

  • Production-ready Meerir MVP hosted in GCP enabling natural-language queries of air traffic data
  • Wingbits ADSB data ingested via a stable batch process and structured in a data warehouse
  • Operational chatbot powered by a large language model that answers predefined questions with two to three embedded visualizations
  • Basic user management (sign-up, authentication) implemented and tested
  • All planned sprints completed with working features demonstrated; user acceptance testing confirmed functionality and performance

Platforms

  • CloudCloud
  • WebWeb
  • AI/ML PlatformAI/ML Platform

Tech Stack

  • Google Cloud Platform (GCP)Google Cloud Platform (GCP)

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“HatchWorks AI has delivered a production-ready Meerir MVP, enabling users to query air traffic data in natural language. The team has managed the project using a structured agile delivery model with clearly defined sprints. The client has praised the team's high level of technical quality.”

Craig Mahaney

CEO

Note: This endorsement is based on publicly available client feedback from external review sources.

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