AI Engineering Capability

AI & Agentic Systems Engineering

Move beyond simple API wrappers and toy demos. We deploy specialized Forward Deployed AI Engineers to build resilient retrieval pipelines, multi-agent execution systems, and private LLM inference architectures directly in your secure cloud environment.

Configure AI Pod Pilot to Production
Retrieval Architecture Hybrid Dense+BM25
Deployment Boundary Private VPC / Airgap
Agent State Topology Deterministic Graphs
Output Validation Pydantic / Guardrails

The Engineering Reality of Enterprise AI

Building generative AI applications that deliver enterprise value requires solving complex systems engineering problems: semantic chunking, multi-modal ingestion, high-dimensional vector search indexing, context window optimization, deterministic tool execution, and continuous evaluation against hallucination.

DRG's Forward Deployed AI Engineers work alongside your product managers and software developers to build custom, production-hardened AI software that runs within your latency, security, and budgetary boundaries.

Core Technology Stack

Vector Search & Storage: pgvector (PostgreSQL), Qdrant, Milvus, Chroma.
Agentic Frameworks: LangGraph, LlamaIndex, AutoGen, CrewAI.
Inference & Serving: OpenAI, Anthropic Claude, Meta Llama 3, Mistral, Ollama, vLLM, HuggingFace TGI.

Core AI Engineering Practices

Advanced RAG Architectures

Hybrid BM25 + dense semantic vector search with Reciprocal Rank Fusion (RRF), cross-encoder reranking (Cohere / BGE), parent-document retrievers, and AST parsing for codebases.

Deterministic Multi-Agent Workflows

Cyclic graph architectures (LangGraph) for task decomposition, tool routing with schema validation, and human-in-the-loop checkpoints that eliminate rogue execution loops.

Private & Air-Gapped Model Serving

Containerized open-weight model serving (vLLM / Ollama) on Kubernetes for sensitive enterprise data requiring zero third-party API exposure.

Continuous Telemetry & Evaluation

Automated regression evaluation frameworks (Ragas, TruLens) monitoring context precision, recall, faithfulness, latency, token spend, and drift over time.

How FDE AI Engineers Integrate

Unlike traditional ML consulting firms that deliver theoretical Jupyter notebooks, DRG Forward Deployed AI Engineers write production code:

  • Data Pipeline Engineering: Implementing asynchronous document parsers, chunking strategies, and vector batching with Kafka, Redis, and Celery.
  • Robust API Gateways: Wrapping model execution in strict OpenAPI schemas with rate limiting, fallback fallthroughs, and streaming SSE endpoints.
  • Guardrails & Sanitization: Integrating strict input/output filter layers (NeMo Guardrails, Guardrails AI) to eliminate prompt injection and sensitive PII leakage.

Frequently Asked Questions

What vector databases and AI frameworks do your FDE teams support?
We build production integrations with pgvector, Qdrant, Milvus, Chroma, and Pinecone, utilizing orchestration frameworks such as LangGraph, LlamaIndex, and AutoGen, alongside models from OpenAI, Anthropic, and open-source models hosted via Ollama or vLLM.
How do you ensure data security and compliance with enterprise AI models?
All deployments are engineered to support enterprise governance requirements, utilizing private VPC boundaries, role-based access control (RBAC) at the embedding level, and strict input/output validation guardrails.
Can your engineers build AI agents that take autonomous actions?
Yes. We design stateful agentic systems with deterministic tool calling, structured JSON output enforcement, sandboxed execution boundaries, and human-in-the-loop review checkpoints.

Build Enterprise-Ready AI with Forward Deployed Engineers

Explore our interactive pod builder to configure your AI engineering team, or discuss your technical architecture with our senior engineering partners.

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