Beacon AI is an aviation technology company focused on enhancing flight safety and operational efficiency for commercial and private aircraft operators. The company develops an AI Assistant that…
AI AssistantAviationFleet MonitoringFlight EfficiencyFlight SafetyOperational EfficiencyPilot AssistanceRoute Optimizationbeaconai.co
Beacon AI
Beacon AI is an aviation technology company focused on enhancing flight safety and operational efficiency for commercial and private aircraft operators. The company develops an AI Assistant that…
AI AssistantAviationFleet MonitoringFlight EfficiencyFlight SafetyOperational EfficiencyPilot AssistanceRoute Optimizationbeaconai.co
HQSan Carlos, US
Team Size28
Open Jobs25
Total Funding$20M
Latest Fundraise2 years ago
TL;DR
Core product: AI pilot assistant and fleet operations platform
Founded: 2021
Headquarters: San Carlos / Silicon Valley, California
Total funding: ≈ $20M (includes $15M Series A)
Key customers / partners: Commercial, charter, corporate and defense fleets; U.S. Air Force collaborations
Company Overview
Problem Domain
Aviation safety and fleet operations efficiency for commercial, private, charter, corporate and government/defense operators.
Founded
2021
Industry
Software Development
Tech Stack
Cloudflare
Cloudflare JS
Font Awesome
Global Site Tag
Google Analytics
Google Font API
Google Tag Manager
HSTS
SSL by Default
reCAPTCHA
Funding Track Record
Series A- 2024-08-21
$15,000,000
Grant- 2024-02
$1,300,000
Recorded as a U.S. Air Force award
Pre-seed / early support- 2021-07
Investor Signal
“Backed by venture investors including Costanoa Ventures, Scout Ventures, JetBlue Ventures and individual/angel participants (e.g., Sam Altman)”
Data and AnalyticsDeepTechHealthInformation TechnologySoftware
$31M
You will ship LLM-powered product features end-to-end. That means designing retrieval and tool-calling flows, writing the services that run them, building evals and guardrails, and watching cost, latency, and quality in production
You’ll partner with the ML/infra teammates on embeddings, indexing, and model hosting, and with the product teammates on user experience and outcomes. We move fast, and we care about reliability in a safety-critical domain
We’re hiring across levels. Senior engineers own features and services. Staff engineers own systems, standards, and cross-team technical direction
Design and implement retrieval-augmented generation and tool-calling flows using frameworks like LangChain or equivalent primitives, where simpler is better
Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks
Add function calling to integrate with internal tools, search, routing, and data services
Own the service layer
Ship APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff
Add caching, request shaping, prompt templates, and context packing to control latency and cost
Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints as needed
Retrieval and data prep
Collaborate with infrastructure teammates to develop chunking, embeddings, and indexing capabilities for documents, time series, and multimedia
Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone
Keep knowledge bases fresh with data syncs from S3, Aurora, DynamoDB, and external sources
Evaluation and quality
Create offline evals and golden sets for prompts, retrievers, and tools
Stand up online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request
Run A/B tests and prompt/version rollouts with guardrails and canaries
Safety, privacy, and compliance
Implement content and policy checks, PII detection and redaction, access controls, and auditing
Design human-in-the-loop paths for sensitive actions
Handle aviation data with care and follow internal security standards
Operate what you build
Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation
Debug tricky failures across retrieval, prompts, tools, and providers
Example problems you might tackle in month one
Transform an internal knowledge base into a low-latency RAG service, complete with explicit schemas and evaluations
Add tool-calling to automate a repetitive cockpit or ops workflow with guardrails and audit trails
Reduce the cost per request through improved chunking, caching, and prompt refactoring, while maintaining task success rates- Strong builder: Comfortable writing production code, tests, and docs. You keep things simple and observable
Shipped LLM apps: You’ve put LLM features in front of users and improved them with data
RAG and tools depth: You understand embeddings, chunking, vector search tradeoffs, and function calling
Clear communicator: You explain tradeoffs and align partners across product, infra, and security
Cost and latency aware: You track p95, hit SLAs, and reduce cost without hurting quality
Quality mindset: You design evals, define success metrics, and iterate based on evidence
Experience with Bedrock, OpenSearch Serverless, pgvector, Pinecone, or Weaviate
Prompt versioning, guardrails, and provider routing in production
Multimodal work with time series or video
Familiarity with GPU inference, Triton, or TensorRT-LLM
Aviation or other safety-critical domain exposure
DevOps basics for CI/CD, IaC, and secure secrets handling
