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.
Hire Generative AI Developers
We've shipped 20+ AI systems to production for clients in the US, UK, and Ireland, LLM integrations handling real user traffic, RAG pipelines querying enterprise knowledge bases, and multi-agent workflows that run reliably at scale.
Start in days, no lengthy hiring cycles or model-selection paralysis.
Engineers with GPT-4o, Claude 3.5, Gemini, and open-source LLM production experience.
Evaluation metrics and weekly reporting from day one, AI performance is visible, not assumed.
Recognition
Sound familiar?
Struggling to find generative AI engineers who have actually shipped LLM systems beyond the demo stage?
Burned by AI pilots where the prompts worked in isolation but collapsed under real production load?
In short
RaftLabs provides generative AI developers who have shipped production LLM systems, RAG pipelines, multi-agent workflows, and AI evaluation frameworks, for clients in the US, UK, and Australia. Engagements run part-time, full-time, or as a dedicated team. Developers start within 1-2 weeks at USD 2,400/month.
Generative AI developers who have shipped LLM systems to production
Hiring a generative AI developer who can write a demo is easy. Finding one who has designed a system that holds up under real production traffic, accurate retrieval, controlled costs, measurable output quality, is a different problem.
We've been building with LLMs since the earliest API access. In that time, we've shipped RAG systems for enterprise document search, multi-agent workflows for operations automation, and AI writing tools used daily by clients in the US, UK, Ireland, and Australia. These aren't proof-of-concept prototypes, they're live products with real users and measurable performance targets.
If you need an engineer who has debugged retrieval accuracy at scale, cut token costs by 60% through model tiering, and designed evaluation pipelines that catch regressions before they hit users, that's the work we do.
Who We Work With
We work with founders and engineering teams building generative AI products and features.
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.
Why Hire Generative AI Developers from Us?
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.
RaftLabs vs. in-house vs. freelance
| RaftLabs | In-House | Freelance | |
|---|---|---|---|
| Time to hire top Generative AI developers | 1 day to 2 weeks | 4 to 6 weeks | 1 to 12 weeks |
| Project initiation time | 1 day to 2 weeks | 2 to 10 weeks | 1 to 10 weeks |
| Risk of project failure | Exceptionally low with a 98% success rate | Low | Very High |
| Developers supported by project management | Yes, dedicated PM and Agile processes | Varies | No |
| Exclusive development team | Yes, dedicated team guaranteed | Yes | No |
| Assurance of work quality | Yes, with quality assurance processes | Yes | Varies |
| Advanced development tools and workspace | Yes, enterprise-grade tools | Yes | Varies |
How to Get Started
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. Fill out the contact form and we'll respond within one business day.
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. Concrete numbers you can plan around.
Project Kickoff
Engineers onboard in days. Evaluation baseline set in week one. First AI features in production within two weeks.
Pricing
Dedicated senior engineers from $6,000/month (minimum pod $12,000/month).
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.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
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.
Part-time engagements start at USD 2,400/month (80 hours). Full-time engagements start at USD 4,800/month (160 hours). Dedicated AI teams start at USD 15,000/month. Project-based work starts at USD 10,000 for an AI feature build with evaluation and production deployment.
Contact us for a tailored quote based on your specific requirements.
Most engagements start within 1-2 weeks of signing. We have engineers available on short notice for urgent projects. The discovery call and proposal typically happen within 2-3 business days of your initial contact.
Yes. We sign NDAs as standard practice for all client engagements. Contact us to get the process started.
We use Slack for day-to-day communication and Asana for project tracking. You will have direct access to your developer and a dedicated project coordinator throughout the engagement. Daily standups or weekly syncs are available depending on your preference.
RAG (retrieval-augmented generation) is the right choice when your AI needs to answer questions from a document corpus that changes frequently, product manuals, contracts, internal knowledge bases. It costs less to maintain because you update the data store, not the model. Fine-tuning is the right choice when you need a model to produce output in a very specific format, style, or domain vocabulary that general models don't do consistently. Most production systems start with RAG; fine-tuning is added later when output consistency requirements tighten.
Every engagement includes an evaluation framework from week one, so quality issues surface with data rather than gut feel. If retrieval accuracy, output format adherence, or task completion rates aren't hitting targets, we have a systematic process: review the evaluation traces, adjust chunking strategy or prompt design, re-run evals, and iterate. You can see exactly where the gaps are and what changed between versions. Quality problems that are invisible are the hard ones, our evaluation-first approach means they're always visible.