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Further is a 51–200 person consulting firm built on a modern data and AI stack: Snowflake + Databricks + GCP, with heavy investment in LLM tooling (LangChain, LangGraph, LlamaIndex, Pinecone, Weaviate, RAG). Active hiring skews heavily toward data (14 open roles) and engineering (10), with senior-level positions dominating — indicating they're scaling delivery capacity and internal platform capability rather than adding junior labor. Projects span RAG systems, feature stores, and production ML workloads, while pain points cluster around prototype-to-production gaps and data maturity, suggesting they're building internal tools to solve client problems they face repeatedly.
Tech Stack 53 technologies
Core StackGitHub Copilot Cursor Snowflake Databricks Salesforce Dynamics 365 JavaScript Jira Python FastAPI RAG Pinecone Weaviate PostgreSQL LangChain Go TypeScript Svelte Adobe Analytics Adobe Customer Journey Analytics Google Analytics 4 GCP Adobe Launch Git Adobe Experience Platform HTML CSS Adobe Target LangGraph LlamaIndex+20 more
What Further Is Building
◆Challenges
- Improving data usage maturity
- Bridging gap between prototypes and production
- Ensuring high availability, security, cost-effectiveness
- Increasing conversion rates
- Security concerns with tagging libraries
- Increasing revenue
- Turn raw data into the right decisions
- Reducing administrative overhead
- Improving employee engagement
- Profile stitching issues
▲Active Projects
- Scalable data pipelines
- Experimentation backlog development
- Llmops workflows
- Deployment of web applications on google cloud platform using cloud run, cloud sql, vertex ai
- Cloud-based commercial ai products
- Complex web applications using go, typescript (svelte), and python (fastapi)
- Rag systems
- Client conversion optimization
- Feature store architecture for ml workloads
- Structured backlog of evidence-based hypotheses
Hiring Activity
35 roles · 35 in 30d
Department
Seniority
Company intelligence
