Jetty AI offers an outsourced AI engineering service that designs, builds, and deploys production‑grade AI features for SaaS and enterprise customers in 2‑4 weeks. Leveraging a global network of senior engineers, the firm integrates multiple LLM providers (GPT‑4, Claude, Gemini, custom models) with retrieval‑augmented generation, vector search, and CI/CD pipelines on cloud platforms, delivering cost reductions of 60‑70% versus hiring U.S. talent.
Funding
Funding not disclosed
Founders
Product
Problem
Companies that need AI capabilities often spend $500K‑$1.2M + per year on senior engineers, consulting firms, or prolonged internal projects, yet the time to ship a production‑ready feature stretches from six months to a year. The high cost and slow delivery prevent SaaS products, growth‑stage startups, and large enterprises from gaining a competitive AI moat. Additionally, many organizations lack a vetted talent pool that can deliver end‑to‑end AI solutions at scale.
Solution
Jetty AI operates as an outsourced AI engineering team that designs, builds, and deploys production‑grade AI products in 2‑4 weeks. By leveraging a network of elite engineers across the US, UK, and India, the firm reduces development costs by 60‑70 % compared with hiring senior U.S. talent. The service covers the full product lifecycle—from problem definition and rapid prototyping to cloud‑native deployment, monitoring, and continuous improvement. Jetty AI’s stack is model‑agnostic, integrating OpenAI, Anthropic, Google Gemini, and fine‑tuned custom models through LangChain/LlamaIndex orchestration. Data‑centric components such as RAG, vector search (Pinecone/Weaviate), and PostgreSQL + pgvector enable enterprise‑grade knowledge management. All infrastructure is containerized (Docker/K8s), CI/CD‑automated (GitHub Actions), and hosted on AWS, GCP, or Azure with edge delivery via Vercel/Netlify. Security and compliance are baked in with OAuth 2.0/JWT, row‑level security, SOC 2 controls, and end‑to‑end encryption. Clients receive a web dashboard for monitoring, analytics, and API access, allowing rapid iteration without additional engineering overhead.
Target Audience
Primary customers are mid‑market SaaS companies (ARR $5‑50 M), Series A‑C growth startups, and enterprise organizations with revenue >$100 M that require internal AI tools, copilot features, or workflow automation. The service is also suitable for regulated sectors (healthcare, finance, defense) needing on‑prem or air‑gapped deployments.
Features
- Multi‑model LLM layer supporting GPT‑4, Claude, Gemini, and custom fine‑tuned models via LangChain orchestration
- Retrieval‑augmented generation (RAG) pipeline with Pinecone/Weaviate vector stores and hybrid PostgreSQL + pgvector search
- AI agents and automation workflows built on ReAct loops, exposing 23 built‑in tools (email, browser, shell, vision, voice) and 61 operations
- Production‑ready CI/CD pipeline (GitHub Actions) with Docker/Kubernetes containerization and automated scaling on AWS/GCP/Azure
- Enterprise security suite: OAuth 2.0/JWT, row‑level security, data‑at‑rest encryption, SOC 2 compliance, and audit logging
- Observability stack integrating Datadog, Prometheus, Grafana, and LangSmith for LLM performance tracing
- API gateway and SDKs (Python, Node.js, TypeScript) for seamless integration with existing SaaS back‑ends
- Dedicated AI innovation center option that embeds a full‑time AI R&D team within the client’s organization