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Complete AI Engineering Pod, Operational in 72 Hours

Stop Prototyping. Start Shipping.
Hire a Complete AI Pod in 72 Hours.

A pre-assembled AI engineering pod with a Delivery Head, AI Architect, senior LLM engineers, MLOps, and QA. One contract, one delivery cadence, production-grade AI shipped to staging every Friday. No more POC purgatory. No more AI experiments that never reach your users.

Deploy Your AI Pod See What's Inside the Pod

7-Day Risk-Free Trial

Zero commitment start

Production AI from Week One

Not POCs. Deployed systems.

Senior-Only Engineers

5–10+ years across every role

Trusted by CTOs & Founders Worldwide

Trusted by CTOs & Founders Worldwide

Trusted by CTOs & Founders Worldwide

Trusted by CTOs & Founders Worldwide

Trusted by CTOs & Founders Worldwide

10+ Years in Business

500+ Projects Delivered

200+ Global Clients

4.9/5 Client Satisfaction

Stop Paying for AI Experiments. Start Paying for AI That Ships.

Eighty percent of enterprise applications shipped in Q1 2026 embed at least one AI agent. Yet only 1% of companies have successfully scaled AI beyond pilot phases. The gap is not ambition. It is not budget. It is the team. Here is the specific breakdown.

Senior AI engineers are expensive, hard to hire, and often take months to find

Most AI teams can build prototypes but struggle with production systems like RAG pipelines, inference infrastructure, and multi agent workflows

Ungoverned AI generated code creates architectural debt that is costly to fix later

With individual hiring, you manage coordination, sprint health, and delivery risk yourself

One contract for a complete AI pod with a Delivery Head, AI Architect, senior LLM engineers, MLOps, and QA

Every AI assisted output is reviewed by senior engineers before deployment, ensuring speed without compromising architecture

Production AI features ship every Friday, including RAG systems, agent workflows, and inference APIs

Devlyn gives access to pre vetted production AI engineers in days, not months

What's Inside a Devlyn AI Pod

Every Devlyn AI pod is published before you sign anything. No vague "team of rockstars." Here is exactly who is in the pod, what they own, and why each role is non-negotiable.

Owns delivery, velocity, and communication.

Delivery Head

Most AI vendors drift into contractor mode after onboarding.The Delivery Head prevents execution chaos and keeps the pod operating like an embedded product team.

  • Sprint planning & retrospectives
  • Weekly Friday demos
  • Velocity tracking & delivery metrics
  • Blocker escalation before delays happen
  • Coordination with your engineering leadership
  • Keeps execution aligned with roadmap
Owns technical integrity

AI Architect

AI systems fail when architecture decisions are improvised. The AI Architect ensures every technical decision scales beyond prototype stage and maintains long-term system reliability.

  • RAG pipeline architecture
  • Agent orchestration design
  • Vector database selection
  • LLM routing strategy
  • Inference cost optimization
  • Architecture review before merge
  • 5–10+ years production AI experience
Architecture-matched specialists

Senior LLM Engineers

These engineers build and optimize the core AI workflows inside your system. Assigned based on your architecture requirements — not whoever happens to be available.

  • LangChain & LlamaIndex implementation
  • OpenAI & Anthropic integrations
  • CrewAI & LangGraph agents
  • RAG implementation across FAISS, Pinecone & Weaviate
  • AI workflow optimization
  • 5–8 years production software experience
  • Deep LLM specialization
Production reliability owner

MLOps Engineer

Without MLOps, AI systems stay demo-quality. This role ensures deployment reliability, monitoring, scalability, and operational stability in production.

  • ML CI/CD pipelines
  • Model serving infrastructure
  • Monitoring & observability
  • Drift detection
  • Cost-performance optimization
  • FastAPI & BentoML deployment workflows
  • LangSmith & Weights & Biases monitoring
AI-specific quality assurance

QA Engineer

Traditional QA misses AI-specific failures. This engineer validates retrieval quality, agent behavior, and edge-case reasoning before anything reaches staging.

  • Automated AI behavior testing
  • RAG retrieval validation
  • Agent decision-path testing
  • Edge-case evaluation
  • Pre-staging quality gates
  • Hallucination & reasoning checks
  • AI workflow reliability testing

5–6 people per pod. Optional DevOps support added for infrastructure-heavy AI deployments.

Why Engineering Leaders Choose the AI Pod Model

There is a reason Toptal, DevTeam.Space, and every other major AI talent platform still sells individual engineers. Building a pod product that delivers production AI is harder. You have to vet a team, not just a person. You have to commit to architectural outcomes, not just availability. Most vendors cannot do it. Devlyn is built for exactly this.

Why Engineering Leaders Choose the AI Pod Model
Production AI, Not POC AI

Production AI, Not POC AI

The Devlyn AI pod ships production ready RAG pipelines, LLM features, and agent workflows built for real world scale, latency, monitoring, and reliability.
Governance First Delivery

Governance First Delivery

Every AI assisted commit is reviewed by the AI Architect before deployment, preventing architectural debt and maintaining production quality from day one.
Senior-Only Across Every Role

Senior-Only Across Every Role

Every role in the pod brings 5 to 10+ years of experience across AI engineering, MLOps, and production systems. The focus is not just working AI, but scalable and reliable AI.
Weekly Demos. Shipped to Staging.

Weekly Demos. Shipped to Staging.

Every Friday, the pod presents live AI systems running in staging or production, including RAG endpoints, inference APIs, and agent workflows your team can test immediately.
One Contract. Zero Coordination Overhead.

One Contract. Zero Coordination Overhead.

One pod contract means one onboarding process and one delivery relationship, while the Delivery Head manages coordination, sprint execution, and delivery accountability.
India-Based. Production Proven.

India-Based. Production Proven.

Devlyn AI engineers operate from Bengaluru and Hyderabad, giving you access to production AI talent at significantly lower cost than equivalent US hiring, without compromising delivery quality.

AI Pod Options - You Stay in Control

Every engagement starts with a defined pod configuration. Start at the scale your roadmap requires. Add capacity at 30 days notice when your AI roadmap grows.

Starter AI Pod

A focused 4–5 person pod for companies shipping their first production AI feature or adding LLM capabilities to an existing product.

Ideal For

Series A companies adding AI to a working product; SaaS platforms building RAG search, AI copilots, or recommendation systems

  • Delivery Head + AI Architect + 2 Senior LLM Engineers + QA

  • Full-time 640–800 hrs/month combined

  • Human-governed delivery, weekly demos, sprint retrospectives

Deploy Starter AI Pod
Growth AI Pod

A complete 5–6 person pod for companies running parallel AI workstreams, LLM features, agentic automation, and MLOps infrastructure simultaneously.

Ideal For

Series B companies building AI-native product capabilities. platforms deploying multi-agent systems or production RAG at scale

  • Delivery Head + AI Architect + 2 Senior LLM Engineers + MLOps + QA

  • Full-time 800–1,000 hrs/month combined

  • Human-governed delivery, architecture reviews, weekly demos, cost-performance reporting

Deploy Growth AI Pod
Enterprise AI Pod

A scaled pod for complex, multi-track AI product engineering, multiple agent systems, custom LLM fine-tuning, and enterprise-grade MLOps infrastructure.

Ideal For

Enterprises building internal AI platforms; regulated industries deploying AI with compliance requirements.

  • 2 Delivery Heads + 2 AI Architects + 3–4 Senior Engineers + MLOps team + QA

  • Full delivery governance, SLA-driven KPIs, architecture review committee

  • Compliance-aware delivery model, dedicated AI security review

Build Your Enterprise AI Pod

AI Skills and Technical Expertise

The pod's technical capability covers every layer of a production AI system. No surface-level experimentation. Production-depth across the full modern AI engineering stack.

LangChain

LangChain

Multi-model routing

Multi-model routing

LlamaIndex

LlamaIndex

OpenAI & Anthropic APIs

OpenAI & Anthropic APIs

Mistral, Groq & open-source LLMs

Mistral, Groq & open-source LLMs

LLM fine-tuning & RLHF

LLM fine-tuning & RLHF

Prompt engineering

Prompt engineering

AutoGen & CrewAI

AutoGen & CrewAI

LangGraph orchestration

LangGraph orchestration

Human-in-the-loop systems

Human-in-the-loop systems

Prompt routing & task decomposition

Prompt routing & task decomposition

Agent reliability engineering

Agent reliability engineering

FAISS, Pinecone & Weaviate

FAISS, Pinecone & Weaviate

Qdrant & ChromaDB

Qdrant & ChromaDB

Pgvector

Pgvector

Pipeline optimization

Pipeline optimization

Accuracy tuning

Accuracy tuning

Context window scaling

Context window scaling

Hybrid search

Hybrid search

PyTorch & TensorFlow

PyTorch & TensorFlow

Hugging Face & PEFT

Hugging Face & PEFT

Scikit-learn workflows

Scikit-learn workflows

ONNX model portability

ONNX model portability

Model validation & bias testing

Model validation & bias testing

Feature engineering pipelines

Feature engineering pipelines

MLflow & Kubeflow

MLflow & Kubeflow

SageMaker & Vertex AI

SageMaker & Vertex AI

BentoML & FastAPI

BentoML & FastAPI

Docker & Kubernetes

Docker & Kubernetes

CI/CD pipelines

CI/CD pipelines

Drift detection & retraining

Drift detection & retraining

LangSmith evaluation

LangSmith evaluation

Weight & Biase tracking

Weight & Biase tracking

Datadog observability

Datadog observability

Helicone monitoring

Helicone monitoring

Custom RAG eval

Custom RAG eval

Cost-performance optimization

Cost-performance optimization

How to Launch Your AI Pod - In 5 Simple Steps

From first conversation to production AI in your sprint tools, the process is built around one principle: no surprises.

1.

AI Pod Strategy Session

Tell us your AI roadmap, delivery goals, and current engineering gaps. We define the right pod structure, architecture direction, and delivery targets for your product.

2.

Pod Assembly and Architecture Alignment

We match your needs with pre vetted senior AI engineers. The AI Architect reviews your stack, codebase, and infrastructure before assembling 2 to 3 aligned pod options.

3.

Pod Interview and Selection

Meet the Delivery Head and AI Architect directly, with optional interviews for LLM engineers. You are selecting a team with established workflows and delivery standards already in place.

4.

Onboarding in 72 Hours

Your pod joins your Jira, GitHub, Slack, and CI/CD workflows within 72 hours. Sprint planning starts immediately, with the first Friday demo scheduled in week one.

5.

Ship, Govern, and Scale

Working AI ships to staging every Friday with AI Architect review on every PR. Track delivery, quality, and LLM cost metrics while scaling the pod up or down as needed.

Proven Track Record Across Industries

Our AI pods have shipped production LLM features, agentic workflows, and RAG systems for startups, scale-ups, and enterprises. The same senior-only, human-governed model that ships for Series A SaaS works for regulated healthcare platforms and high-volume fintech AI systems.

500+

Engineering Projects Delivered
Production AI systems, LLM features, RAG pipelines, and agent workflows from MVPs to enterprise scale platforms.

30+

Countries Served
AI pods trusted by engineering teams in the US, UK, Australia, Germany, Singapore, and the Middle East deploying production AI into their products.

99%

Client Retention Rate
Clients stay because the AI Architect owns the architecture and the Delivery Head owns the outcomes. Weekly demos make performance visible before problems compound.

65–80%

Cost Advantage vs US Market
Senior AI engineers from Bengaluru and Hyderabad at 65 to 80% lower cost than US hiring, without compromising quality or governance.

Industries Where Our AI Pods Ship

SaaS and AI-Native Products

Fintech and Financial AI Systems

Healthcare and Clinical AI

E-Commerce and Personalization Platforms

Legal Tech and Document Intelligence

EdTech and Adaptive Learning Systems

Logistics and Predictive Operations

Media and Content Intelligence

Who Should Hire an AI Pod?

Whether you are a Series A startup or a scaling enterprise, a pre-assembled AI pod is within reach if your product roadmap demands it.

CTOs Whose AI Roadmap Has Stalled in POC

Your team can build AI demos but struggles to productionize them. Devlyn AI pods focus on scalable inference systems, reliable RAG pipelines, governed delivery, and production ready AI shipped every Friday.

Scaling SaaS Companies Adding AI Features

Your roadmap includes copilots, RAG search, or agent workflows, but your team lacks deep AI architecture and MLOps expertise. Devlyn AI pods integrate into your existing engineering team and accelerate delivery without disrupting your product.

Founders Who Need AI Execution Without Managing AI Teams

You have the AI vision but not the internal AI leadership or time to manage contractors. The Delivery Head manages execution while the AI Architect drives technical direction and system design.

Enterprises Replacing Failing AI POC Teams

Move beyond AI prototypes that never reach production. Devlyn AI pods are built for production first delivery, governed AI development, and accountable execution inside real environments.

Ready to Deploy Your AI Pod?

Your AI roadmap does not need another POC. It needs a production team. A complete, pre-assembled, human-governed AI engineering pod is operational in 72 hours. One contract, one delivery lead, working production AI every Friday.

Deploy Your AI Pod Now Book an AI Pod Strategy Call
hello@devlyn.ai
+91 9974839428

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Frequently Asked Questions

Staff augmentation places individual AI engineers under your management. You own sprint planning, architecture decisions, code review, and delivery accountability. A Devlyn AI pod delivers a complete, pre-assembled team where the Delivery Head owns sprint health and weekly demos, the AI Architect owns technical direction and governance, and every engineer works to established architectural standards from day one. The pod owns the outcome. You direct product priorities.

Every pod includes a Delivery Head, AI Architect, 2 senior LLM engineers, MLOps engineer, and a QA engineer. Growth pods add DevOps support. Enterprise pods scale with multiple architects and dedicated security review. Every role carries a 5–10+ year minimum. The pod composition is published on this page before you speak to anyone on our team.

72 hours from selection to active sprint participation. The AI Architect runs a technical discovery session on your existing stack on day one. The Delivery Head schedules the first sprint planning and Friday demo before the week ends. The 72-hour timeline reflects our pre-vetted, pre-assembled pool, not a scramble to staff after contract signing.

Proof-of-concept AI system is optimized to demonstrate capability. A production AI system is optimized to run reliably, scale under load, fail gracefully, stay within inference cost budgets, and be monitored for drift and degradation over time. The Devlyn AI pod designs for production constraints from the first sprint: retrieval accuracy at scale, agent failure modes, LLM cost routing, model monitoring, and CI/CD for ML systems. POC work looks impressive in demos. Production work runs in your product.

Full production-depth coverage: LangChain, LlamaIndex, OpenAI API, Anthropic Claude, Mistral, and open-source LLMs for LLM engineering. AutoGen, CrewAI, and LangGraph for agentic systems. FAISS, Pinecone, Weaviate, Qdrant, and pgvector for RAG and vector infrastructure. PyTorch, Hugging Face, and PEFT for model engineering. MLflow, SageMaker, Vertex AI, BentoML, and FastAPI for MLOps and deployment. LangSmith, Helicone, and Weights and Biases for observability.

Every AI-assisted or AI-generated output in the pod goes through the AI Architect's review before it merges into your codebase. This is not a manual second pass on every line, it is an architectural review that catches pattern violations, production safety gaps, and cost-inefficient design before they compound. The result is faster throughput from AI tooling without the architectural fragmentation that ungoverned AI code introduces. Your codebase stays clean. Your architecture stays coherent.

Pod size adjusts at 30 days notice in either direction. Adding a specialist, a fine-tuning engineer for a custom model, a second MLOps engineer for infrastructure scaling, is handled through the same pre-vetted pool with architectural continuity maintained by the AI Architect. Reducing headcount follows the same 30-day cycle. No penalty fees. No replacement friction. Your pod adapts to your roadmap.

If the pod's delivery falls short of the agreed sprint targets, the Delivery Head owns the diagnosis and correction plan, presented in the following Friday retrospective. If the issue is with a specific engineer, we replace them within 5 business days at no cost. If the pod configuration itself is the issue, we reconfigure and present alternatives within the same timeframe. The 7-day risk-free trial means if the pod is not the right fit in the first week, you do not pay. After that, every Friday demo is the accountability mechanism, delivery problems surface weekly, not quarterly.