404 Dispatch
Transport company CRM: drivers, cargo, accounting—and an AI assistant helping dispatchers make decisions.
Service
AI Agents
MetaByte develops AI agents—programs based on language models connected to your data that perform business tasks: handling inquiries, preparing documents, answering clients, and escalating complex issues to humans.
- Models
- Claude · GPT · open-source models on your own server
- Data
- are used exclusively for your purposes and do not feed into a general model
- Timelines
- First functional agent delivered in as little as 3 weeks
How does an AI agent differ from a chatbot?
Chatbots respond by following preset scripts, while AI agents comprehend free-form requests, access your data, and perform actions—such as creating records, preparing documents, or assigning tasks.
The difference becomes clear when a user says something outside the script. A chatbot replies, 'I don't understand, please select a menu item.' An AI agent analyses the request: it finds a contract by number, notices it's expired, and suggests renewal—because it has data access and permission to act.
This highlights the main limitation: an agent is only as effective as the quality of the data it connects to. If client information is scattered across multiple tables and emails, the first step is data organization—not the model—and we address this upfront, not midway through the project.
What tasks can AI agents truly solve?
AI agents pay off on tasks involving large volumes of repetitive text and verifiable outcomes: handling inquiries, preparing documents, first-line support, and extracting data from emails and invoices.
Poor candidates are the opposite: one-off tasks, tasks without verifiable results, or where errors are costly and uncheckable. We advise against these—failed AI deployments end up costing more than none, causing long-lasting organizational resistance to automation.
- Incoming request processing: classification, prioritization, routing to responsible staff
- Document preparation from CRM data using templates
- First-line support based on your knowledge base, escalating to human agents as needed
- Extracting data from emails, invoices, and delivery notes into structured formats
- Operator assistance: suggesting solutions based on history of similar cases
What happens to our data?
Client data is used solely to respond via their agent: MetaByte connects to models over APIs with training disabled, or if strict requirements exist, deploys an open model on your server, ensuring data never leaves your infrastructure.
Data requirements are our first question, as they dictate the overall architecture. Commercial APIs offer better quality and lower entry costs. Open models hosted on your hardware provide full control and predictable volume costs but require GPU servers and deliver somewhat lower quality.
A hybrid approach is often best: sensitive fields are anonymized before sending, and the model works with text stripped of names and numbers.
How do we handle model errors?
Language models are always fallible, so MetaByte designs agents to minimize the cost of errors: sources are shown, irreversible decisions aren’t made autonomously, and uncertain cases are escalated to humans.
Engineering is more important than the model choice. Responses are based on retrieved documents, not the model’s memory, with source links for quick verification. Actions that cannot be undone—payments, client communications, deletions—require human confirmation. Agent confidence is measured; low confidence triggers escalation rather than guessing.
We also record baseline metrics before deployment. Without knowing the volume of requests currently processed manually, discussions about impact become subjective.
What You Get
- A functional agent integrated into your process—not a separate sandbox
- Integration with your data sources: CRM, databases, documents, email
- Dashboard logging requests, responses, and cases escalated to humans
- Measurement of metrics before and after deployment
- Employee guides and escalation protocols
- 30 days of free corrections following launch
Tech Stack Claude API · OpenAI API · RAG · PostgreSQL + pgvector · Node.js · Python
Verified Work Samples
Links open directly—this is the verification. We do not display or describe NDA-protected work.
Frequently Asked Questions
What does AI agent deployment cost?
Costs include development and monthly fees for model usage. MetaByte calculates both upfront during evaluation to prevent surprises in operational expenses.
The second part is often overlooked. An agent handling a thousand queries per month versus one processing a hundred thousand emails incurs vastly different operational costs, and the choice of model impacts this difference more than anything else. We calculate the cost per query on actual volumes before starting work.
Will the agent replace an employee?
In practice, the agent takes over repetitive tasks from employees rather than replacing them: people remain responsible for decision-making, exceptions, and communication where errors are costly.
Can the model be trained on our documents?
For most tasks, additional model training isn’t necessary and can be counterproductive — MetaByte connects documents through knowledge base search (RAG), so the agent responds based on the most current version, not a static snapshot from training.
Additional training makes sense when you need to enforce a specific style or response format. Transferring knowledge this way is costly: any document updates require retraining, whereas knowledge base search updates immediately when you replace a file.
How quickly can we see the first result?
A first working agent for a single, focused task is delivered within three to four weeks by design: a narrow task provides measurable results sooner than a full-scale rollout and enables an initial demo.
Tell us about your task
We'll analyze and get back to you with a scope of work, timeline, and cost estimate. Analysis is free and we always respond. Within the business day..
