AI Consulting Services From Engineers Who Ship
Most AI consultants hand you a strategy document and leave. We hand you a production roadmap built on experience running AI systems every day.
FLYTEBIT provides AI consulting services backed by real production engineering. We run three AI products in production (DOCKR, PASSR, TESTR) and bring that operational experience to every consulting engagement. Strategy grounded in shipping experience.
What sets us apart
The AI consulting market is full of firms that rebranded from management consulting or IT services into AI overnight. They can frame the business case. They cannot discuss model architecture, latency budgets, or what happens when an LLM hallucinates in production.
We run AI products. DOCKR watches every commit 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 pilot projects.
That changes the quality of our consulting. When we recommend an architecture, it is because we run something similar in production. When we flag a risk, it is because we have hit it ourselves. 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.
AI consulting capabilities
We cover the full consulting lifecycle: from initial feasibility assessment through architecture design, technology selection, and implementation roadmap. Every engagement ends with a scoped plan with timelines, costs, and success criteria.
Structured assessment of whether AI can solve your problem. We audit your data, workflows, and infrastructure. The output is a scoped plan with timelines, costs, and measurable success criteria. If AI will not help, we tell you.
Define where AI creates value across your organization. We map your current state, identify high-impact use cases, and build a phased roadmap with dependencies, timelines, and resource requirements. Strategy that connects to implementation.
Choose the right models, frameworks, and infrastructure for your constraints. We compare OpenAI, Anthropic, Google, and open-source LLMs. We evaluate LangChain, LangGraph, crewAI, and custom orchestration. We deploy on AWS, GCP, and Azure. The selection is based on your data, budget, and latency requirements.
Design the system architecture for your AI solution. RAG pipelines, agent orchestration, API integration, monitoring, and fallback strategies. Production-ready designs that account for scale, cost, and reliability. Designs that survive contact with real traffic.
Define governance frameworks for AI in production. Access control, audit trails, output validation, human-in-the-loop checkpoints, and compliance alignment. We build governance into the architecture from the start.
Oversee the build phase to ensure the architecture is implemented correctly. We review code, validate model performance, and catch production issues before they reach your users. Connects strategy to shipping.
Feasibility first, then roadmap
We start with a feasibility study. 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 plan with timelines, costs, and measurable success criteria. If the study shows AI will not help, we tell you.
A full AI strategy engagement runs 4-8 weeks. We prioritize use cases, design the architecture, select the technology stack, and build a phased implementation roadmap. Implementation oversight can run 3-6 months depending on scope.
AI products we run in production
We consult on AI and run AI systems in production every day. This is the difference between an AI consulting firm 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 for AI consulting
The AI consulting market is crowded with firms that rebranded from management consulting or IT services into AI overnight. They can frame the business case. They cannot tell you what happens when a production LLM starts hallucinating at 3am on a Saturday.
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 consulting engagement.
Strategy grounded in production
Every recommendation we make is backed by experience running AI systems in production. We do not recommend architectures we have not built. We do not suggest approaches we have not tested under real load.
We build our own AI products
DOCKR, PASSR, and TESTR run in production every day. We deal with model drift, latency, cost optimization, and governance so you do not have to learn these lessons on your budget.
Feasibility before commitment
We start with a structured audit. If AI will not solve your problem, we tell you before you spend money on a build. If it will, we scope the work with timelines, costs, and success criteria.
Consulting to implementation
We do not hand you a strategy document and disappear. We can take the engagement from feasibility study through architecture design to implementation oversight. One team covers the full lifecycle.
FAQ
What do AI consulting services include?
AI consulting services cover strategy, feasibility assessment, technology selection, architecture design, and implementation roadmaps. The goal is to determine where AI creates measurable value in your organization and define a path to production. A good AI consultant hands you a scoped plan with timelines, costs, and success criteria. Anything less is a waste of your time.
How is FLYTEBIT different from other AI consulting firms?
FLYTEBIT runs its own AI products in production (DOCKR, PASSR, TESTR). Most AI consulting firms sell advisory services but have never shipped an AI product. We deal with model drift, latency budgets, cost optimization, and governance in our own systems every day. That production experience shapes every consulting engagement. Every engagement starts with a feasibility study.
How much do AI consulting services cost?
A feasibility study starts from $2K. Full AI consulting engagements start from $8K onwards. The final cost depends on the scope and engagement model. A feasibility study takes 2-4 weeks. A full AI strategy engagement takes 4-8 weeks, and implementation oversight can run 3-6 months. The feasibility study scopes the work accurately before any commitment.
What is the difference between AI consulting and AI development?
AI consulting focuses on strategy, feasibility, and roadmap definition. AI development writes the code and ships the system. FLYTEBIT does both. Consulting engagements identify where AI creates value and define the architecture. Development engagements build and deploy the system. Most clients start with consulting and move into development once the roadmap is validated.
Do you work with existing AI teams?
Yes. We augment existing AI teams with architecture review, technology selection, and production readiness assessments. We also fill specific gaps like agent orchestration, RAG pipeline design, or infrastructure setup. The engagement model depends on what your team needs and where the gaps are.
What industries do you provide AI consulting for?
We have consulted on AI projects in financial services, healthcare, e-commerce, SaaS, and manufacturing. The approach is the same regardless of industry: map the workflow, identify where AI creates value, define measurable success criteria, and build a production roadmap. Industry knowledge matters less than engineering discipline and a structured feasibility process.
Talk to Us About Your AI Strategy
Tell us what you are trying to achieve. 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.