Forward Deployed AI Engineers Embedded in Your Repository
Turn foundational LLMs into production-ready software features. We deploy forward-deployed AI engineers in under 48 hours to architect custom RAG pipelines, configure vector stores, and build agentic workflows.
Core AI Implementation Capabilities
Production-grade implementations focusing on reliability, latency reduction, and deterministic guardrails.
Custom RAG Retrieval
Chunking strategies, hybrid search (BM25 lexical + dense vector embeddings), contextual re-ranking, and dynamic context windows to prevent hallucination.
Vector Infrastructure
Configuring scalable indexing on pgvector, Qdrant, or Pinecone with automated embedding pipelines and metadata filtering schemas.
Agentic Workflows
Multi-step reasoning loops, structured JSON outputs, function calling, tool execution pipelines, and deterministic fallback paths.
Latency & Cost Optimization
Semantic caching, token usage management, model cascading (routing simple tasks to lightweight models), and throughput benchmarking.
Sprint-Based AI Delivery
How our AI engineers integrate with your product roadmap in rapid agile cycles.
Data & Pipeline Audit
Assessing existing documentation, schemas, and API requirements for retrieval accuracy.
Proof of Concept
Building an initial working pipeline in a test harness with objective evaluation benchmarks.
Production Integration
Implementing API endpoints, error handling, rate limiting, and private VPC connections.
Continuous Evaluation
Tracking accuracy metrics, token consumption, and regression testing across model updates.
Architectural Considerations Before Implementation
Not every feature requires complex agentic loops or custom fine-tuning. We help clients evaluate whether simple prompt engineering, RAG, or specialized model routing offers the highest return on engineering effort.
When RAG is Preferred:
When responses require real-time proprietary data, frequent knowledge base updates, or strict source citations for verification.
When Fine-Tuning is Necessary:
When teaching a model a specialized tone, domain-specific syntax, or highly constrained output formats where context injection is inefficient.
Forward Deployed AI Engineering FAQ
What does a Forward Deployed AI Engineer do?
A Forward Deployed AI Engineer embeds directly into client technical teams to bridge the gap between theoretical AI models and production software. They implement data ingestion pipelines, vector databases, prompt orchestration layers, model evaluation frameworks, and API integrations within the client's existing codebase.
Which vector databases and AI frameworks do your engineers support?
Our engineers build with vector storage systems including pgvector, Qdrant, Pinecone, and Chroma, and work across orchestration frameworks including LangChain, LlamaIndex, LiteLLM, and native OpenAI/Anthropic/Google GenAI APIs.
How is client data privacy maintained during AI implementation?
Engineers deploy architectures within the client's private cloud VPCs or enterprise-compliant model endpoints, ensuring that proprietary training data and enterprise context remain strictly within the client's security perimeter.
Deploy AI Engineers in 48 Hours
Describe your AI project scope, data structures, and tech stack. A deployment architect will coordinate your squad kickoff.
- Kickoff Velocity Onboarding within 48 Hours
- Sprint Flexibility Weekly Sprints, Direct Git Commits