Services

Four ways we embed.

Each engagement is scoped to a specific type of AI engineering challenge. We match the right FDE subspecialty to your problem.

FDE-AgentMost requested

What is FDE-Agent?

AI agents that work in production

Duration8–16 weeks

Team1–2 FDEs embedded full-time

The majority of enterprise AI investment in 2025 went to agent systems — and the majority stalled before reaching production. We specialize in taking agent architectures from prototype to production-grade.

What we deliver

→Multi-agent orchestration systems (LangGraph, CrewAI, custom)
→Tool-use pipelines with reliable function calling
→Memory and context management at scale
→Streaming and async agent architectures
→Automated eval harnesses for agent behavior
→Observability and tracing (LangSmith, Helicone, custom)

Common use cases

Customer service automation
Internal knowledge agents
Code generation pipelines
Document processing workflows

FDE-Infrastructure

What does FDE-Infrastructure cover?

The foundation your models need

Duration6–12 weeks

Team1–2 FDEs embedded full-time

Models are only as good as the infrastructure that serves them. We build the data pipelines, embedding systems, and inference infrastructure that makes AI fast, cheap, and reliable.

What we deliver

→RAG pipeline design and implementation
→Vector database setup and optimization (Pinecone, Weaviate, pgvector)
→Embedding pipeline design and batching
→Fine-tuning pipelines (LoRA, QLoRA, full fine-tuning)
→Inference optimization (vLLM, TGI, quantization)
→GPU cluster management and cost optimization

Common use cases

Enterprise search
Semantic document retrieval
Domain-specific model fine-tuning
High-throughput inference APIs

FDE-Eval

How does FDE-Eval work?

Confidence before you ship — and after

Duration4–8 weeks

Team1 FDE embedded full-time

Shipping AI without evals is flying blind. We build the evaluation frameworks, test suites, and monitoring systems that let you deploy with confidence and catch regressions before users do.

What we deliver

→Automated LLM evaluation pipelines
→Custom benchmark design and implementation
→Red-teaming and adversarial testing
→Human-in-the-loop review workflows
→A/B testing frameworks for model changes
→Production monitoring and drift detection

Common use cases

Pre-launch safety testing
Regression detection for model upgrades
Compliance documentation for regulated industries
Continuous quality monitoring

FDE-Sovereign

What is FDE-Sovereign for regulated industries?

AI in regulated and air-gapped environments

Duration12–24 weeks

Team2–3 FDEs embedded, security-cleared where required

Not every AI system can run in the public cloud. We deploy AI in the environments where it has to live — on-prem, air-gapped, FedRAMP, or private cloud — without sacrificing capability.

What we deliver

→On-premises LLM deployment (Llama, Mistral, custom)
→Air-gapped inference infrastructure
→FedRAMP-compliant AI pipelines
→HIPAA-compliant healthcare AI systems
→Private cloud AI architecture (GovCloud, private AWS/GCP/Azure)
→Data residency and sovereignty controls

Common use cases

Government and defense AI systems
Healthcare AI with PHI
Financial services with strict data residency
Critical infrastructure

Not sure which engagement fits?

Tell us what you're trying to build. We'll scope the right engagement in a 30-minute call.

Talk to us