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    AI Agents Built for Your Product to Accelerate Delivery

    Use Claude, Gemini, and ChatGPT as internal AI agents. Build autonomous AI agents, create your own AI, and run a fully secure AI solution in your stack.

    Book an AI Quick Start Session View Case Studies
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    Why Engineering Teams Need More Than Generic AI Tools

    Your team needs AI agents, not generic prompts. Teams that create your own AI or deploy a personal AI agent inside their codebase see faster delivery and fewer review cycles.

    Discuss Your Project
    Marketing Director

    AI output is generic

    Doesn't understand your architecture or code patterns.

    Developers waste time prompting

    Long, repetitive prompts slow down real work.

    No measurable productivity gain

    Tools feel helpful, but cycle time stays the same.

    Security concerns limit usage

    Code can't leave the company environment — even when using autonomous agents AI or AI autonomous agents.

    Low adoption

    Teams resist tools that don't improve their workflow.

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    Why Companies Choose Teamvoy for AI Engineering Agents
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    Agents built inside your Codebase

    Context-driven behavior from context engineering AI agents ensures accuracy and fewer revisions.

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    Measurable engineering KPIs

    Cycle time, merge velocity, review duration, defect rate.

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    End-to-end delivery

    From repo analysis to deployment, we help you develop an AI that supports your workflow from day one.

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    10+ years of engineering Experience

    Deep experience in architecture and AI integration — essential when building and scaling AI coding agents for software engineering or AI agents for data engineering.

    Why Companies Choose Teamvoy for AI Engineering Agents

    Generic AI lacks context. Use AI agents to create a personal AI agent that writes accurate code, cuts errors, and speeds delivery.

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    Agents built inside your Codebase

    Context-driven behavior from context engineering AI agents ensures accuracy and fewer revisions.

    Measurable engineering KPIs

    Cycle time, merge velocity, review duration, defect rate.

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    End-to-end delivery

    From repo analysis to deployment, we help you develop an AI that supports your workflow from day one.

    10+ years of engineering Experience

    Deep experience in architecture and AI integration — essential when building and scaling AI coding agents for software engineering or AI agents for data engineering.

    What We Offer: Custom AI Agents for Real Engineering Work

    By combining AI coding agents and AI agents for data engineering with context learning, each agent understands your code. Scale from one to multiple autonomous AI agents across teams.

    Custom Engineering Agents

    30–40% Faster Delivery

    Developer Upskilling

    Workflow Integration

    Secure Private Deployment

    Cost Efficiency
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    Custom Engineering Agents

    Agents trained on your repos, docs, APIs, and workflows. These context engineering AI agents produce code and insights that match your style and patterns.

    30–40% Faster Delivery

    Automated breakdowns, coding steps, debugging, and documentation handled by precise autonomous AI agents.

    Developer Upskilling

    Hands-on training that shows engineers how to guide agents, write better prompts, and create your own AI workflows inside the codebase.

    Workflow Integration

    Agents that work inside GitHub, Cursor, Jira, Slack, Confluence, and CI/CD so your AI autonomous agents can support tasks from commit to deployment.

    Secure Private Deployment

    Deploy on-prem, in a VPC, or your private cloud. Your code stays inside your infrastructure even when running multiple autonomous agents AI.

    Cost Efficiency

    Less review time and rework means more features delivered without expanding headcount.

    Custom Engineering Agents

    Agents trained on your repos, docs, APIs, and workflows. These context engineering AI agents produce code and insights that match your style and patterns.

    30–40% Faster Delivery

    Automated breakdowns, coding steps, debugging, and documentation handled by precise autonomous AI agents.

    Developer Upskilling

    Hands-on training that shows engineers how to guide agents, write better prompts, and create your own AI workflows inside the codebase.

    Workflow Integration

    Agents that work inside GitHub, Cursor, Jira, Slack, Confluence, and CI/CD so your AI autonomous agents can support tasks from commit to deployment.

    Secure Private Deployment

    Deploy on-prem, in a VPC, or your private cloud. Your code stays inside your infrastructure even when running multiple autonomous agents AI.

    Cost Efficiency

    Less review time and rework means more features delivered without expanding headcount.

    What Our Clients
    Say

    “The team at Teamvoy is highly competent and proficient in many programming languages. We have had them work on various applications used in our day-to-day business including custom 3D modeling software, payment processing, and website work.”
    CTO Avatar

    Arnon Rosan, Founder, Ex-President and CEO, EverBlock Systems, LLC.

    "Teamvoy has successfully launched the system within the set timeline and integrated all the required tools and features. The collaborative team led regular meetings, delivered on time, and communicated effectively. Their proactive problem-solving approach and commitment to innovation stand out."
    JH

    Director of Marketing & Business Development at Market Access Direct

    Want AI Agents Working for You?

    Drop your email – we’ll audit your setup & show where agents boost results

    How It Works: From Your First AI Agent to Autonomous AI Support

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    01. AI Quick Start Session

    Identify high-ROI workflows and define your first internal AI agent, including optional paths for autonomous AI agents.

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    02. Agent Setup (2–5 Weeks)

    We build and deploy AI autonomous agents trained on your codebase.

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    03. Developer Training

    Hands-on guidance so your team can create your own AI, manage autonomous agents AI, and extend them over time.

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    04. Continuous Support

    Performance tuning, workflow expansion, and deeper integrations with AI agents for data engineering and other internal tools.

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    How It Works: From Your First AI Agent to Autonomous AI Support
    two professionals analyzing artificial intelligence brain visualization on computer monitor in modern office
    01. AI Quick Start Session

    Identify high-ROI workflows and define your first internal AI agent, including optional paths for autonomous AI agents.

    separator black
    02. Agent Setup (2–5 Weeks)

    We build and deploy AI autonomous agents trained on your codebase.

    separator black
    03. Developer Training

    Hands-on guidance so your team can create your own AI, manage autonomous agents AI, and extend them over time.

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    04. Continuous Support

    Performance tuning, workflow expansion, and deeper integrations with AI agents for data engineering and other internal tools.

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    FAQs – Key Questions About Teamvoy’s Services

    What are codebase-aware AI engineering agents?

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    Codebase-aware AI engineering agents are internal AI systems trained directly on your repos, documentation, APIs, and architecture.

    They don’t rely on generic patterns – instead, they learn your coding styles, workflows, naming conventions, and system structure.
    Because of this context, they can break down tasks, generate PR-ready code, write tests, debug issues, update tickets, and support developers with far higher accuracy than standard copilots.

    How are engineering AI agents different from generic AI tools?

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    Generic AI tools work from public training data and isolated prompts. They lack understanding of your architecture, dependencies, patterns, CI rules, and team workflows. Engineering AI agents, by contrast:

    • Run inside your environment
    • Learn from your repos and engineering processes
    • Follow your coding standards
    • Reduce review cycles and rework
    • Integrate with GitHub, Cursor, Jira, Slack, and CI/CD

    Because they operate on real project context, they deliver measurable gains in cycle time, merge velocity, and bug reduction.

    Is my code secure when using these AI agents?

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    Yes. These AI agents run inside your infrastructure – on-prem, VPC, or private cloud. Your code, embeddings, and indexes never leave your environment.
    All access is read-only, token-scoped, and fully auditable.
    No external model training occurs using your code, and no data is sent to public AI providers.
    This setup satisfies strict security requirements for fintech, banking, and enterprise engineering teams.

    How quickly can we deploy our first internal engineering agent?

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    Most teams deploy their first internal agent in 2–5 weeks. The process includes:

    1. A Quick Start Session to identify high-ROI workflows
    2. Repo scanning and context modeling
    3. Agent configuration and deployment inside your environment
    4. Developer training to use, manage, and extend the agent

    Many companies see value during the first week, even before full rollout.
    Start Here

    Book a Quick Start Session to see how AI agents can accelerate your engineering performance and show how autonomous AI agents operate inside your environment.

    Book Session