Generative AI Development That Ships to Production
Most GenAI development companies build demos. We build generative AI systems that run in production every day, for our own products and for our clients.
FLYTEBIT builds custom LLM applications, RAG pipelines, AI agents, and generative 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 a "generative 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.
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.
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, governance and access control, monitoring and alerting. We deal with these issues in our own products.
What are generative AI development services?
Generative AI development services cover the design and deployment of systems that use large language models to generate text, code, images, or structured output. The work spans seven areas:
GenAI consulting & strategy
Feasibility studies, technology selection, and architecture design. Determines whether AI solves your problem before you spend build budget.
Custom LLM applications
Chat interfaces, copilots, content generation tools, and document processing pipelines built on OpenAI, Anthropic, or open-source models.
RAG pipelines
Retrieval-augmented generation systems that connect LLMs to your data. Knowledge engines, document Q&A, and enterprise search.
AI agent development
Goal-driven agents that use tools, call APIs, and execute multi-step workflows autonomously. Built with LangGraph, crewAI, or custom orchestration.
Model fine-tuning
Custom models trained on your data using Llama, Mistral, or proprietary foundations. Useful when API-based models hit cost or accuracy ceilings.
GenAI integration
Embedding LLM features into existing products and workflows. Custom wrappers, monitoring, and guardrails around ChatGPT Enterprise, Copilot, or Claude.
Ongoing operations
Monitoring, drift detection, cost optimization, and governance for systems running in production. The work that starts after deployment.
The engagement timeline
We audit your data, workflows, and infrastructure to define what to build.
We build a working prototype on a slice of your data to prove the approach.
We build the full system, integrate it with your stack, and ship it.
We monitor, tune, and maintain the system after launch.
These timelines are general. Actual durations change based on the scope of work, data readiness, and integration complexity. The feasibility study gives you the real numbers for your specific case.
How to engage a GenAI development partner
Common myths and what nobody tells you
Development is a one-time expense. API calls are forever. A system that costs $15K to build can cost $3K per month in API calls. Budget for the ongoing cost from day one. The build is the small number. The monthly bill is the one that compounds.
The LLM provider updates the model. Your agent's outputs shift. Your CI pipeline stays green because nothing in your code changed. This is the core operations problem for GenAI systems. You need evaluation suites that catch drift before users do.
If you build tightly to one provider's API, switching costs are high. Abstract the model layer early so you can swap providers without rewriting your application. The feasibility study should address this on day one, not after you are locked in.
Deployment is the starting line. The day after you deploy, five things start decaying: model behavior, token costs, tool APIs, governance rules, and credentials. None of them trigger an error. They are slow failures that look like success until someone reads the bill.
Teams spend weeks choosing between GPT-4 and Claude. They spend zero time building evaluation suites. The model choice is reversible. The evaluation infrastructure is what catches drift before your users do. Build it first, then pick a model.
A great model on bad data produces confident nonsense. RAG systems retrieve from your knowledge base. If that base has stale docs, contradictory entries, or broken chunks, the model generates polished wrong answers. Clean your data before you build, not after users complain.
Start with the problem, bring data, and define success criteria before the build starts. Ship in increments so you see working software every two weeks. Budget for operations, because the build is the small number and the ongoing cost is the big one. And read our operations guide before you deploy anything.
Custom generative AI development capabilities
We build across the full generative AI stack: LLM applications, RAG pipelines, AI agents, model fine-tuning, and the infrastructure to run it all in production.
Production LLM applications built on OpenAI, Anthropic, and open-source models. Chat interfaces, content generation, document processing, and copilots with output guardrails and streaming responses.
Retrieval-augmented generation systems that connect LLMs to your data. Vector databases, semantic search, document Q&A, and enterprise knowledge bases with hybrid search and citation tracking.
Goal-driven AI agents that use tools, call APIs, and execute multi-step workflows autonomously. Built with LangGraph, crewAI, or custom orchestration. Human-in-the-loop governance built in.
How much does generative AI development cost?
Scoped audit of your data, workflows, and infrastructure. You get a build plan with timelines, costs, and success criteria.
Custom GenAI system built, integrated, and shipped. Final cost depends on scope. The feasibility study defines the real number.
Architecture reviews, technology selection, and ongoing advisory. Best for open-ended engagements and second opinions.
Best for advisory work, architecture reviews, and open-ended engagements. The risk is cost growing without a fixed endpoint.
Works well for defined deliverables with clear scope. Scope changes trigger change orders, so the feasibility study matters here.
Covers ongoing operations, monitoring, and iteration. Watch for retainers without defined deliverables, which become open-ended hourly in disguise.
You pay per deliverable. The feasibility study defines the milestones before the build starts, so both sides know what each payment covers.
Three cost drivers explain the pricing. LLM API costs are token-based and scale with usage, so a system serving 10,000 users costs 10x what a system serving 1,000 users costs in API calls. Infrastructure for vector databases, compute, and hosting adds a monthly floor that does not go away. GenAI engineering expertise is scarce, and engineers who have shipped production LLM systems command premium rates. Industry standard projects range from $10K to $500K+. A feasibility study scopes the actual cost for your specific use case before you commit.
Start with a $2K feasibility study to scope accurately before committing to a build. Use open-source frameworks like CrewAI, AutoGen, and LangGraph to reduce build cost. Phase the delivery: prototype first, then production, then scale. Consider build vs buy: some use cases are served by existing products like DOCKR, PASSR, or TESTR. Building in-house from scratch climbs past $100K in engineering salaries alone. Read our build vs buy guide for the full comparison.
Engagement costs can be structured as milestone-based billing where you pay per deliverable, phased delivery where cost spreads across project phases, or retainer models for ongoing operations. Industry retainer rates range from $5K to $50K per month. The feasibility study defines the right structure for your budget and timeline.
We deliver globally. Remote engagement is the default. Pricing does not change based on geography. The feasibility study and build phases work the same regardless of location.
Signs you need generative AI development services
Not every problem needs GenAI. These signs suggest it is worth a feasibility study.
Your team spends hours on summarizing, categorizing, drafting, or extracting from documents. A GenAI system handles this in minutes and frees your team for higher-value work.
Your customers or prospects are asking for AI-powered search, Q&A, copilots, or automation. If your competitors are shipping these and you are not, you are losing deals.
Your company has years of documents, tickets, wikis, or manuals that nobody can search effectively. RAG pipelines turn that archive into a working knowledge engine for your team and customers.
Multi-step workflows require human handoffs at every stage. Agents can execute these workflows autonomously with guardrails, cutting cycle times from days to minutes.
Your competitors are launching AI features while your team is still evaluating frameworks. A development partner helps you close the gap without hiring a full AI team.
Support tickets, review queues, or operational handoffs are growing faster than headcount. GenAI can absorb the repetitive portion and let your team focus on exceptions.
The day after you deploy an AI agent, five things start decaying at the same time. None of them trigger an error. They are slow, silent failures that look like success until someone reads the bill, the audit log, or the customer complaint.
The LLM provider updates the model. Your agent's outputs shift. Your CI pipeline stays green.
Token usage trends up as the agent finds longer reasoning paths or retries more often. The $380 support conversation did not fail. It just kept going.
An API the agent calls changes its response schema or adds a rate limit. The agent starts failing silently because it cannot parse the new format.
The governance rules were written for the agent's original scope. Since then, the team added two new tools and expanded data access. The policy envelope was not updated.
API keys expire. Service accounts accumulate permissions. Offboarded team members' credentials stay active. Nobody reviews them until something breaks.
Based on what you are seeing, your next step is either a scheduled consultation or an emergency response. Here is how to tell which one.
- You are planning a new GenAI system
- Your existing system needs evaluation or optimization
- You want a feasibility study before committing to a build
- Your AI system is actively losing money
- Quality degradation is visible to users
- Governance violations are occurring
If an agent is actively causing damage, hit the kill switch first, then revoke tool permissions, terminate active sessions, and page the incident commander. Document what happened once the bleeding stops. The first 5 minutes determine whether the incident is contained or compounding. Read our operations guide for the full incident response framework.
For active incidents, we offer emergency response. Same-day availability depends on current capacity. The first step is a call to describe the situation. We will tell you whether it needs immediate action or scheduled work. For non-emergency engagement, the first step is a feasibility call to scope the problem.
Feasibility first, then build
We start with a feasibility study. The study maps your current state, finds where generative 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. This is the difference between a GenAI 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, and module summaries. Always current, committed on every push.
Reviews every PR across eight quality dimensions: security, performance, scalability, architecture, code quality, testing, and maintainability. Every finding includes an impact assessment and a 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.
FAQ
What are generative AI development services?
Generative AI development services cover the design, build, and deployment of systems that use large language models to generate text, code, images, or structured output. This includes LLM applications, RAG pipelines, AI agents, model fine-tuning, and integration of generative AI features into existing products. A development company writes the code and ships the system. A consultant hands you a strategy document.
How much does generative AI development cost?
A feasibility study starts from $2K. Full generative AI development engagements start from $8K onwards. The final cost depends on the scope and engagement model. Industry standards for fixed-price AI projects range from $10K to $500K+. Hourly rates range from $50 to $300+. Retainer models range from $5K to $50K per month. The feasibility study scopes the build accurately before any commitment.
What is the cost per hour for generative AI development?
Industry hourly rates range from $50 to $300+ depending on seniority and region. Advisory work and architecture reviews at FLYTEBIT bill $150 to $300 an hour. For project engagements, our feasibility assessments start from $2K and custom builds start from $8K. Building in-house from scratch easily climbs past $100K in engineering salaries alone.
How long does a generative AI project take?
A feasibility study takes 2-4 weeks. A focused GenAI feature like a RAG Q&A or document summarizer takes 4-8 weeks. A production AI agent build takes 8-16 weeks. Full enterprise GenAI infrastructure projects run 3-6 months. The feasibility study scopes the timeline accurately before any commitment.
What does generative AI development include?
Generative AI development includes feasibility studies, custom LLM application development, RAG pipeline development, AI agent development, model fine-tuning and adaptation, integration of GenAI features into existing products, and ongoing operations including monitoring, drift detection, and cost optimization. The full stack from model selection through production infrastructure.
Why is generative AI development so expensive?
Three cost drivers explain the pricing. LLM API costs are token-based and scale with usage. Infrastructure for vector databases, compute, and hosting adds a monthly floor. GenAI engineering expertise is scarce and commands premium rates. Industry standard projects range from $10K to $500K+. A feasibility study scopes the actual cost for your specific use case before you commit.
How can I save on generative AI development?
Start with a $2K feasibility study to scope accurately before committing to a build. Use open-source frameworks like CrewAI, AutoGen, and LangGraph to reduce build cost. Phase the delivery: prototype first, then production, then scale. Consider build vs buy: some use cases are served by existing products. Building in-house from scratch climbs past $100K in engineering salaries alone.
Do you offer payment plans or financing?
Yes. Engagement costs can be structured as milestone-based billing where you pay per deliverable, phased delivery where cost spreads across project phases, or retainer models for ongoing operations. Industry retainer rates range from $5K to $50K per month. The feasibility study defines the right structure for your budget and timeline.
What are the signs I need generative AI development services?
Five signs indicate you need professional GenAI development. Your model outputs are drifting without code changes. Token costs are trending up. Tool APIs are breaking your agent silently. Governance rules have gone stale as your team added tools and data access. Credentials are expiring or accumulating permissions. None of these trigger an error in your CI pipeline. They are slow failures that look like success until someone reads the bill.
When should I call a generative AI development professional?
Call a professional when your AI system is actively losing money, when quality degradation is visible to users, when your team lacks the expertise to diagnose drift or cost creep, or when you need to ship a GenAI feature and do not have in-house LLM engineering capacity. For active incidents like runaway spend or data exposure, request emergency response. For planning and scoping, schedule a consultation.
Do you offer emergency or same-day response?
For active incidents like runaway spend, data exposure, or unauthorized agent actions, we offer emergency response. Same-day availability depends on current capacity. For non-emergency engagement, the first step is a feasibility call to scope the problem and define the response plan. Book a call and describe the situation. We will tell you whether it needs immediate action or scheduled work.
What should I prepare before my first consultation?
Bring a summary of the business problem you are trying to solve, your current tech stack, what data you have available and where it lives, a rough budget range, and any timeline constraints. If you have an existing AI system that is failing, bring the symptoms: what changed, when it started, and what the impact is. The first call is 30 minutes. No slides.
Is generative AI development worth it?
Generative AI development is worth it when the problem cannot be solved with traditional software, when the workflow involves unstructured data like text or documents, when the volume of work justifies the ongoing API and infrastructure costs, and when your team can maintain the system after launch. The feasibility study answers this question for your specific case before you spend build budget.
What generative AI technologies does FLYTEBIT work with?
We work with OpenAI, Anthropic, Google, and open-source LLMs including Llama and Mistral. For agent frameworks, we use LangChain, LangGraph, crewAI, and custom orchestration. For RAG infrastructure, we deploy vector databases like Pinecone, Weaviate, and pgvector. For hosting, we deploy on AWS, GCP, and Azure. We choose the stack based on your constraints, not our preferences.
Do you work globally or only locally?
We deliver globally. Remote engagement is the default. We have built AI systems for clients across North America, the UK, Europe, and India. The feasibility study and build phases work the same regardless of location. Pricing does not change based on geography.
Talk to Us About Your GenAI Project
Tell us what you are trying to build. We will tell you whether generative AI is the right approach, what it would take, and what it would cost. The first call is 30 minutes. No slides.