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Dispatch Commander

Dispatch spreadsheets replaced by a route solver

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  • Securing AI agents: permissions, prompt injection and audit trails

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  • What an AI model really costs in production

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  • Getting retail AI ready for peak season

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Build

AI agents, built into your business.

Agents, knowledge assistants and the product around them, wired into your tools.

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DocsCRMEmailDatabaseAgentDone
  • MipMap
  • easyvisas
  • EventGo
  • Ace Acumen Heights
  • Suon Academy
  • Kpon
  • Ski China
  • Canada International Golf Academy

Agents that finish the job.

An agent plans the steps, uses your tools and checks its own work.

  1. Understand

    Reads the request, your documents and the context.

  2. Plan

    Breaks the goal into steps.

  3. Act

    Uses your tools: CRM, ERP, email, databases.

  4. Check

    Validates the result, and hands off to a person when it should.

Know what you need? We build it.

  • Lines of code on a dark screen

    Agentic systems

    Agents that plan, use your tools and finish multi-step work.

    • Several agents with clear roles, working one task together
    • Agents that call your APIs, run code and take actions
    • Multi-step work planned and carried through to the end
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  • Person working across several monitors

    Knowledge assistants

    Answers grounded in your own documents, for customers and staff.

    • Answers grounded in your documents, with sources
    • Search that finds the right page, even when the words differ
    • Answers that stay current as your documents change
    Book an AI audit
  • Network switch with patch cables plugged in

    Systems integration

    AI wired into your CRM, ERP and the systems you already run.

    • AI that reads and acts on the data in your CRM and ERP
    • Workflows that start on their own when an event or a record changes
    • Older systems opened up so AI can work with them
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  • Developers working side by side at a long desk

    The product around the model

    The apps, APIs and data a model needs to work as a product.

    • Apps your users and staff actually use
    • APIs and data your agents can rely on
    • Logs and evals that show how the model behaves in production
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  • Code open in an editor on a laptop

    Add AI to the product you already have

    We connect the systems you already run to AI, one workflow at a time.

    • AI features added inside the product your customers already use
    • Older systems opened up to models through clean APIs
    • Existing data cleaned and connected so the model can use it
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  • OpenAI
  • Anthropic
  • Google Gemini
  • Mistral
  • Hugging Face
  • Ollama
  • Replicate
  • LangChain
  • PyTorch
  • TensorFlow
  • Keras
  • Haystack
  • Milvus
  • Activeloop

How we work with you.

Two-week sprints

A demo at the end of each one.

Two developers working at their screens at night

Every step agreed upfront.

  1. Strategy

    Most projects stall before they start.

    Find where AI pays off.

  2. Build

    Built for production.

    Agents wired into your workflows.

  3. Run

    Models change every few months.

    Keep it working as models change.

Developer working on a laptop in a dimly lit office

Built, then run by the same team.

  • Senior-led from start to finish.

    The people you meet build it.

  • We run what we build.

    easyvisas is live, and we operate it.

  • Fixed price, scoped upfront.

    Scope and price agreed before we start.

  • Reliable by design.

    Guardrails and monitoring ship with it.

What we've shipped

All case studies

easyvisas – fully AI-powered immigration processing

Agentic AI backend with RAG over immigration policy and 12,000+ historical cases — automated document intake through decision support.

Read the case study

Questions

What is AI agent development and how is it different from standard software development?
An agent plans the steps, uses your tools and checks the result. It hands off to a person when it should.
What is RAG and when should my business use it?
RAG connects a language model to your own documents, databases and wikis. Answers come from your data, with sources. Use it for support assistants, internal knowledge tools and document Q&A.
Which LLM providers and models does Meterra work with?
We build with OpenAI and Anthropic Claude. Open-source models such as Llama and Mistral fit when data has to stay on your servers or cost matters. We pick the model for your latency, cost and quality needs.
How do you ensure AI outputs are reliable and safe in production?
Every output is checked before it is used. Guardrails keep answers in the expected format, and high-stakes decisions go to a person. A regression suite runs before any prompt or model change, and tracing and cost alerts watch it in production.
Do you work with our existing codebase, or do you build from scratch?
Both. We regularly inherit legacy codebases for modernization — migrating from monoliths to modular services, updating outdated dependencies, or adding new capabilities to existing products. When starting from scratch we establish a clean, well-documented foundation that your in-house team can own long-term.
How does Meterra handle project communication and delivery?
We operate in two-week sprints with a demo at the end of each. You get a private Slack channel, a shared GitHub repository (or your repo), and weekly written status updates. We flag blockers early and never disappear between meetings.