• AI features working in demos but not in production for real users?

  • LLM costs growing faster than revenue because the integration wasn't designed for scale?

Hire Generative AI Developers

Generative AI engineers who build production LLM systems -- from GPT-4 integration and RAG pipelines to multi-agent workflows -- for teams that need AI features that work in the real world, not just demos.

  • AI engineers with GPT-4, Claude, Gemini, and open-source LLM experience

  • RAG systems, agentic workflows, fine-tuning, and AI evaluation frameworks

  • 20+ AI systems built. Start in days. Fixed cost or retainer.

Who We Work With

We work with founders and engineering teams building generative AI products and features.

AI-First Startups

Building a product where generative AI is the core capability. We design the full AI architecture -- model selection, prompt engineering, RAG system, evaluation framework, and production infrastructure -- so you ship with confidence.

Enterprises Adding AI to Existing Products

Adding AI writing, search, document processing, or automation to existing enterprise software. We integrate LLMs into your existing systems without disrupting what's already working.

Agencies Building AI Products for Clients

We provide generative AI engineering resource for agencies delivering AI products to clients -- embedded in your delivery process, following your standards.

Teams Recovering from Failed AI Projects

AI pilot that didn't deliver. Prompts that work in isolation but fail at scale. We diagnose what went wrong and rebuild the system correctly -- with evaluation metrics so you know it's working.

Our Generative AI Development Services

LLM Integration and API Development

Production-grade integration of OpenAI GPT-4o, Anthropic Claude, Google Gemini, or open-source models (Llama, Mistral) into your product backend. Streaming, rate limiting, cost management, fallback strategies, and evaluation built in from the start.

RAG Pipeline Development

Retrieval-augmented generation systems for document Q&A, knowledge base assistants, and enterprise search. Chunking strategy, embedding model selection, vector database setup (Pinecone, Weaviate, pgvector), retrieval pipeline, and re-ranking. RAG systems that answer accurately, not confidently wrong.

AI Agent Development

Multi-step AI agents that use tools, retrieve information, and take actions. Designed with clear task decomposition, tool definitions, retry logic, and failure handling. Agentic workflows that complete tasks reliably, not just sometimes.

AI Product Development

Full AI-first products from discovery to production -- AI writing assistants, document intelligence platforms, code generation tools, research assistants, and domain-specific expert systems. We've shipped 20+ AI systems across industries.

LLM Evaluation Framework

Automated evaluation pipelines that measure LLM output quality -- accuracy, consistency, format adherence, and task completion rate. Regression testing when prompts or models change. Production monitoring for quality drift. You can't operate AI in production without measuring it.

Model Fine-Tuning

Fine-tuning for tasks where general models don't produce consistent enough output -- domain-specific extraction, style-constrained generation, or classification tasks. Training data preparation, fine-tune job execution, evaluation against base model, and production deployment.

Hire generative AI engineers who build AI that works in production

20+ AI systems built. LLM integration, RAG, agents, and evaluation. Fixed cost or retainer.

What Sets Our Generative AI Developers Apart

Production AI Experience

We've built AI systems that handle real production traffic -- 20+ AI products shipped across healthcare, fintech, operations, and content. We know what breaks at scale and how to prevent it.

Multi-Model Expertise

OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini, Meta Llama, Mistral -- we work across the major providers and recommend based on your requirements, not familiarity with one API.

Evaluation-First Approach

We measure before we optimise. Every AI system we build includes an evaluation framework so you can prove it works and catch regressions when things change.

Regular Reporting

Evaluation scores, cost per request, accuracy metrics, and error rates -- at the cadence you choose. AI performance is visible, not assumed.

Cost Architecture

LLM costs are a product cost. We design cost-efficient integrations -- model tiering, caching, token budgets -- so costs are predictable and don't surprise you at the end of the month.

Full-Stack Delivery

AI integration is one layer of an AI product. Our engineers handle the backend API, database, authentication, and deployment -- the complete product, not just the AI component.

Comparative Analysis of RaftLabs, In-House & Freelancers

RaftLabsIn-HouseFreelance
Time to hire generative AI developers
Project initiation time
Risk of project failure
Engineers supported by project management
Exclusive development team
Assurance of work quality
Advanced development tools and workspace

Generative AI Developer Costs -- Monthly

Hire Resource (Part-Time)

For a specific AI feature, LLM integration, or RAG system build.

  • 10 work days per month (80 hours)
  • Dedicated project coordinator
  • Senior team member support when required

Starts at USD 2400

Hire Resource (Full-Time)

For sustained AI product development or a full AI-powered system build.

  • 20 work days per month (160 hours)
  • Dedicated project coordinator
  • Full senior team support included

Starts at USD 4800

Dedicated AI Team

A full AI engineering team for complex AI-first products or enterprise AI deployments.

  • 20 work days per month (160 hours) per resource
  • Dedicated project manager
  • AI, backend, and frontend resources available

Starts at USD 15000

Generative AI Project Costs -- Project Basis

AI Feature Build

A single generative AI feature integrated into your product with evaluation framework and production monitoring.

  • LLM integration, prompt design, and evaluation
  • Backend integration and UI
  • 8--12 week delivery

USD 10,000 -- 30,000

Full AI Product

A complete AI-first product with multiple features, RAG system, agents, evaluation pipeline, and production deployment.

  • Multiple AI features with shared knowledge base
  • Agent workflows and evaluation monitoring
  • 14--24 week delivery

USD 30,000 -- 100,000

Enterprise AI System

Enterprise AI deployments with fine-tuning, on-premise or private cloud hosting, compliance requirements, or multi-system integration.

  • Custom model fine-tuning or private deployment
  • Compliance and data residency requirements
  • Custom scoping required

Get Custom Quote

Our AI and Backend Stack

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Get Started Today

Contact Us

Tell us the use case -- what you want the AI to do, what data it works with, and what system it needs to integrate with.

Discovery Call

A 30-minute call to understand the task, the data, and what reliable production performance means for your use case. We'll tell you what's feasible and what the right architecture is.

Get a Proposal

A clear proposal with scope, timeline, model recommendations, and fixed or retainer cost.

Project Kickoff

Engineers onboard in days. Evaluation baseline set in week one. First AI features in production within two weeks.

Hire generative AI developers who build AI products that work

20+ AI systems built. Engineers available in days. Fixed cost or monthly retainer. Full source code ownership.

Frequently Asked Questions

Model selection depends on your task requirements, performance expectations, data privacy constraints, and budget. GPT-4o is the strongest general-purpose model but the most expensive. Claude 3.5 Sonnet is competitive for reasoning and document tasks. GPT-4o-mini and Claude Haiku handle routine classification and extraction at a fraction of the cost. Open-source models (Llama 3, Mistral) are the right choice when data cannot leave your infrastructure. We recommend the right model after understanding your specific use case -- not by defaulting to the most familiar one.

A focused AI feature -- an LLM integration for a specific task, with evaluation and production deployment -- typically takes 8--12 weeks. The time is spent on: prompt design and iteration (2--3 weeks), retrieval system if RAG is required (2--3 weeks), backend integration (2--3 weeks), evaluation framework setup (1--2 weeks), and production deployment (1 week). Projects that try to skip evaluation typically take longer overall because they debug production failures without measurement.

Data privacy options range from API-level controls (OpenAI Enterprise and Anthropic don't train on API data by default) to private deployment of open-source models on your own infrastructure. For regulated industries, we deploy open-source models (Llama 3, Mistral) on your AWS, GCP, or Azure environment -- no data leaves your infrastructure. We design the data handling architecture based on your specific privacy requirements and regulatory obligations, not a generic approach.

Yes. Most generative AI projects involve integrating with existing databases, APIs, CRMs, ERPs, or document management systems. We design the integration architecture that connects the LLM to your data sources -- reading from your databases, calling your APIs, and writing structured results back to your systems. The AI capability sits within your existing data architecture, not as a separate system.