AI Development Company That Ships to Production
Most AI development companies build demos. We build AI systems that run in production every day, for our own products and for our clients.
FLYTEBIT is an AI development company that builds custom AI agents, generative AI applications, and enterprise AI infrastructure. We run three AI products in production (DOCKR, PASSR, TESTR) and bring that engineering experience to every client engagement.
What sets us apart
Most companies that call themselves an "AI development company" are staff augmentation firms that rebranded. They sell you developers by the hour. The AI part is a label they slapped on, not something they can actually do.
We build and run AI products. DOCKR watches every commit on your repository and updates documentation automatically. PASSR reviews every pull request across eight quality dimensions. TESTR reads your code via AST and generates executable tests in 11+ languages. These are production systems serving real users, not slide decks.
That changes how we build for clients. We know what it takes to run an AI system in production because we do it every day. Model drift, latency budgets, fallback strategies, cost optimization at scale, governance and access control, monitoring and alerting. We deal with these issues in our own products, not in a workshop.
Custom AI development capabilities
We build across the full AI stack: model selection and fine-tuning, agent orchestration, RAG pipelines, API integration, and the infrastructure to run it all in production.
Goal-driven AI agents that use tools, call APIs, and execute multi-step workflows autonomously. Built with LangGraph, crewAI, or custom orchestration depending on your requirements.
Production generative AI apps built on OpenAI, Anthropic, and open-source models. RAG pipelines, document processing, content generation, and chat interfaces with guardrails and output validation.
The infrastructure layer that makes AI systems run reliably in production: monitoring, model drift detection, cost optimization, fallback strategies, and CI/CD pipelines for model updates.
Feasibility first, then build
We start with a feasibility study, not a tool demo. The study maps your current state, finds where AI creates value, and defines a production roadmap before anyone writes code.
The feasibility study runs 2-4 weeks. We audit your data, your workflows, your team structure, and your existing infrastructure. The output is a scoped build plan with timelines, costs, and measurable success criteria. If the study shows AI will not help, we tell you.
The build phase runs 8-16 weeks for most engagements. We ship in increments. You see working software every two weeks.
AI products we run in production
We build and run our own AI products, not just client systems. This is the difference between an AI development company that talks about AI and one that ships it.
Watches every commit. Analyses what changed and updates documentation automatically. Architecture diagrams, API references, module summaries. Always current, committed on every push.
Reviews every PR across eight quality dimensions. Security, availability, performance, scalability, architecture, code quality, testing, maintainability. Every finding includes an impact assessment and a ready-to-apply fix.
Reads every function via AST, discovers what should be tested, and generates executable test code across 11+ languages with auto-generated mocks. Runs through CI/CD on every commit.
Why teams choose us as their AI development company
The AI development market is crowded with firms that rebranded from web development or staff augmentation into AI overnight. They can talk about AI all day. Shipping it is a different story.
We have been building AI systems since before the generative AI boom. Our products process real code and handle real production traffic for real users. That experience shows up in every client engagement.
We ship production, not pilots
Every client engagement ends with a system running in production, not a demo that dies after the presentation. We define success as deployed software with measurable outcomes.
We build our own AI products
DOCKR, PASSR, and TESTR run in production every day. We deal with model drift, latency, cost optimization, and monitoring so you do not have to learn these lessons on your budget.
Feasibility before build
We start with a structured audit. If AI will not solve your problem, we tell you before you spend money on a build.
Full-stack AI capability
Model selection, agent orchestration, RAG pipelines, infrastructure, monitoring, CI/CD. One team covers the entire AI stack. No handoffs between strategy consultants and implementation teams.
FAQ
What does an AI development company do?
An AI development company designs, builds, and deploys AI systems that run in production. This includes custom AI agents, generative AI applications, RAG systems, and AI infrastructure that integrates with your existing stack. The key distinction from a consulting firm is that a development company writes the code and ships the system. A consultant hands you a strategy document.
How is FLYTEBIT different from other AI development companies?
FLYTEBIT runs its own AI products in production (DOCKR, PASSR, TESTR). Most AI development companies sell services but have no shipped products. We build AI systems every day for our own platform, then bring that engineering experience to client engagements. Every engagement starts with a feasibility study. No tool demos.
How much does custom AI development cost?
A feasibility study starts from $2K. Full custom AI development engagements start from $8K onwards. The final cost depends on the scope and engagement model. A feasibility study takes 2-4 weeks. A production AI agent build takes 8-16 weeks, and enterprise AI infrastructure projects can run 3-6 months. The feasibility study scopes the build accurately before any commitment.
What AI technologies does FLYTEBIT work with?
We work with OpenAI, Anthropic, Google, and open-source LLMs (Llama, Mistral). For agent frameworks, we use LangChain, LangGraph, crewAI, and custom orchestration. For infrastructure, we deploy on AWS, GCP, and Azure. We choose the stack based on your constraints.
Can FLYTEBIT build AI agents for my specific industry?
Yes. We have built AI agents for financial services, healthcare, e-commerce, SaaS, and manufacturing. The approach is the same regardless of industry: map the workflow, identify where AI creates value, build a focused pilot, prove it in production, then scale. Industry expertise matters less than engineering discipline.
Do you build AI systems from scratch or integrate existing AI tools?
Both. Some engagements require custom model fine-tuning and agent orchestration built from scratch. Others integrate existing AI tools (Copilot, ChatGPT Enterprise, Claude) into your workflow with custom wrappers and monitoring. The feasibility study determines which approach fits your situation.
Talk to Us About Your AI Project
Tell us what you are trying to build. We will tell you whether AI is the right approach, what it would take, and what it would cost. The first call is 30 minutes. No slides.