Hire generative AI developers who go beyond wiring an app to a foundation model and calling it done. Code B engineers bring the production judgment to know when generative AI actually solves a problem, the engineering discipline to take a system from working demo to reliable deployment, and the communication clarity to integrate into your existing workflows without a prolonged ramp-up.
- Senior-Vetted Engineers Matched to Your Model Stack, Data Environment, and Production Requirements
- Hands-On Experience Across Fine-Tuning, RAG Pipelines, and Agentic Workflows in Live Systems
- Consistent Delivery Across Time Zones with Structured Overlap and Clear Accountability
Generative AI Engineering Support for Businesses Across Global Markets
We support businesses across the US, UK, Europe, and other global markets that require specialized Generative AI engineering expertise to accelerate AI adoption without expanding internal engineering overhead.
Our Generative AI developers contribute as an extension of existing product and engineering teams, supporting AI-powered product development, model integrations, and intelligent workflows through a distributed AI development approach for long-term AI initiatives across global delivery environments.
- 8+ years of software engineering experience supporting global businesses.
- 300+ software projects delivered across global product environments.
- 4.9 Clutch rating across software engineering engagements.
How We Onboard Generative AI Developers
From developer alignment to active sprint execution, our onboarding process is structured to help generative AI developers integrate into existing engineering workflows with minimal disruption across ongoing product delivery environments.
Project Discovery
Share your product environment, model requirements, data constraints, and delivery expectations.
Developer Alignment
Generative AI developers are aligned based on your model stack, use case, and technical requirements.
Workflow Integration
Developers integrate into sprint planning, communication channels, code repositories, and release coordination workflows.
Active Contribution
Engineers begin contributing across model development, integration work, and ongoing engineering operations.
Generative AI Development Case Studies
LLM-Powered Sales Coaching at Enterprise Scale
Mple needed to move beyond scripted sales training into something that could actually reason about a conversation, run role-play simulations, evaluate rep performance in natural language, and generate personalized coaching feedback for enterprise teams across pharma, banking, and FMCG.
We built the Generative AI infrastructure required to support LLM-powered role-play simulation, natural-language conversation analysis, and automated feedback generation, backed by the data infrastructure needed to support ongoing coaching loops across enterprise training cohorts.
Best for product teams that need models grounded in proprietary data, building LLM data retrieval pipelines and vector search architecture that connect generative outputs to your own systems and content.
Ideal for businesses building multi-step, tool-using agents that plan and execute tasks across existing systems, from internal workflow automation to customer-facing task completion.
For teams integrating LLM APIs, structuring prompts as production logic, and embedding AI copilots or assistants directly into an existing product's architecture and release cycles.
Production Experience
Generative AI projects often move beyond proofs-of-concept into production environments where performance, integrations, and business requirements continuously evolve. Businesses benefit from developers who understand building AI systems that are production-ready rather than delivering isolated AI implementations.
Technical Ownership
Dedicated Generative AI developers contribute across product requirements, architecture discussions, and implementation decisions throughout the development lifecycle. Technical ownership remains with the engineering team, actively contributing to product delivery and ongoing enhancements.
Product Continuity
Generative AI capabilities require continuous improvements as models, products, and business requirements mature. Long-term engineering continuity allows businesses to scale AI initiatives without repeatedly onboarding new development teams or rebuilding institutional knowledge.
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Build autonomous AI agents capable of reasoning, task execution, tool calling, and orchestrating multi-step workflows across enterprise systems and digital products.
Develop AI-powered copilots that provide contextual assistance, improve productivity, and enhance user experiences across customer-facing and internal applications
Build Generative AI knowledge systems using Retrieval-Augmented Generation (RAG) to retrieve, organize, and generate contextual information from proprietary business data and documents.
Develop AI-enabled product features such as content generation, semantic search, recommendations, and conversational experiences across web, mobile, and SaaS platforms.
Build intelligent document processing solutions for summarization, information extraction, classification, and document understanding across structured and unstructured business content.
Develop multimodal AI applications that combine text, voice, image, and document understanding to deliver richer and more intuitive user experiences.
Product Development Ownership
Dedicated developers contribute across AI-powered product development, feature implementation, and Generative AI enhancements aligned with business objectives.
Embedded Team Collaboration
Generative AI developers work alongside existing product and engineering teams across different software delivery models, while aligning with established workflows, sprint cycles, and delivery processes.
Continuous Delivery Support
Maintain development momentum across Generative AI initiatives, product releases, and evolving engineering requirements throughout the product lifecycle.
Accelerated AI Implementation
Dedicated engineering capacity enables businesses to introduce Generative AI capabilities across products and platforms without disrupting existing development operations.
Generative AI Development Across Industry Verticals
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Generative AI Development Across Business Stages
Generative AI adoption requirements evolve based on business maturity, internal engineering capabilities, product complexity, and long-term AI implementation priorities.
Early-Stage AI Initiatives
Businesses exploring Generative AI capabilities often require engineering support for validating AI opportunities, introducing intelligent product features, and establishing scalable foundations for future AI adoption.
Scaling AI Products
Growing product companies typically expand Generative AI capabilities across existing software products, business workflows, and intelligent automation initiatives as engineering and product requirements evolve.
Enterprise AI Transformation
Enterprise organizations implement Generative AI capabilities across business-critical systems, internal operations, and long-term digital transformation initiatives that require dedicated engineering expertise and structured execution.
They have strong expertise in the latest technologies and provide excellent guidance in using them effectively.
CODE B launches the products quickly, and their solutions have excellent architecture and are scalable.
CODE B is proactive in coming up with solutions.
Aside from getting the job done, they’re able to provide their expertise and share their opinion.
They’re a very bright team that requires minimal levels of communication or time investment to be very effective.
Their constant communication was a key aspect of the success.
They completed the project within the timeline we gave them, and they did it within budget.
Had a great experience working with the team and in times of crisis, CODE B team was always there to support us.
The way that they have supported us by giving us one of their developers to work directly with our development team.
Our overall experience has been very positive.
They are friendly and reliable.
The ability to deliver on time impressed us the most.
They’re excellent at what they do and come up with solutions for various problems.
CODE B will work overtime to resolve issues, which is a difficult trait to find.
Code B’s communicative.
I’ve had a great experience working with CODE B
The main positive point of working with CODE B team is their analyzing skills.
They are receptive and try to adjust to meet our requirements.
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