AI Agents & Automation

AI Agents & Automation

Autonomous AI systems that execute complex multi-step workflows with minimal human intervention.

AI agents are autonomous software systems that perceive context, plan multi-step actions, and execute complex workflows — including calling APIs, reading documents, and triggering downstream processes — with minimal human oversight. Remolda architects agentic AI and multi-agent systems that replace fragmented manual handoffs across your operations, connecting language models to your real data and real tools. Enterprise teams using our agent platforms automate 12 or more end-to-end workflows per engagement, typically recovering 20+ hours per employee per month.

AI Document Processing & OCR

Remolda deploys intelligent document processing that extracts, classifies, and validates information from any document type — forms, contracts, invoices, reports, applications — at scale and with 95-99% accuracy. We replace manual data entry with AI systems that understand documents, not just read them.

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AI Email Processing & Routing

Automate the triage, classification, drafting, and routing of high-volume inbound email — reducing manual handling time, improving response consistency, and ensuring enquiries reach the right team without delay.

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Multi-Agent AI Orchestration

Design and deploy coordinated systems of specialised AI agents that divide complex workflows across purpose-built components — handling tasks that are too large, variable, or multi-domain for a single AI model to execute reliably.

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RPA + AI Hybrid Automation

Combine robotic process automation with AI reasoning to automate workflows that span legacy systems, unstructured documents, and variable inputs — extending automation into processes that rule-based RPA alone cannot handle.

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AI Workflow Automation

Multi-step AI agents that execute complex business workflows end-to-end — from data ingestion through decision-making to output generation — with minimal human intervention.

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Frequently asked questions

What is an AI agent, and how is it different from a chatbot?
An AI agent is a software system that takes a goal, plans a sequence of steps to reach it, executes those steps using tools (APIs, databases, browsers, code), and adapts when steps fail. A chatbot answers individual messages; an agent completes multi-step tasks autonomously. Modern agents are typically powered by large language models operating through tool-use loops.
When should we use a multi-agent system instead of a single agent?
Use a multi-agent system when one workflow has multiple specialized roles that benefit from separation — for example, a researcher agent that gathers information, an analyst agent that evaluates it, and a writer agent that produces the output. For workflows that fit a single coherent role, one agent is faster, cheaper, and easier to debug. Multi-agent is not a default architecture; it's a specific pattern.
How do you keep AI agents from hallucinating in business workflows?
We keep agents grounded with three layers: retrieval-augmented generation against authoritative sources, mandatory citation of source documents in every output, and structured tool boundaries that constrain what the agent can return. Hallucinations are not a single problem — they are at least four problems (training-data gaps, outdated information, ambiguous prompts, missing retrieval) and each needs its own mitigation.
What does it cost to deploy a custom AI agent in production?
A production-grade custom AI agent typically costs CAD $40,000–150,000 to design, build, and deploy, plus ongoing inference costs of CAD $500–5,000/month depending on volume. Cost drivers are integration complexity (number of systems and APIs), security requirements (on-premise vs cloud, data residency), and the team's existing observability stack. We do not build production agents under CAD $40K — anything cheaper is a prototype, not a production system.
What is the Model Context Protocol (MCP), and should we use it?
Model Context Protocol (MCP) is an open standard for connecting AI assistants to data sources and tools through a JSON-RPC interface. We recommend MCP servers when you have multiple AI clients (Claude Code, Claude Desktop, Cursor, internal apps) that need access to the same data — building one MCP server is cheaper than building per-client integrations. For single-client deployments, plain function-calling is usually simpler.
How do AI agents handle sensitive enterprise data?
Enterprise AI agents at Remolda use a defense-in-depth pattern: data residency controls at the API layer (regional endpoints, no cross-border transfer), token-level redaction of PII before model calls, scoped retrieval that only surfaces documents the requesting user is authorized to read, and audit logging of every model call with input/output capture. Sensitive deployments use private cloud endpoints (AWS Bedrock, Azure OpenAI) rather than public APIs.

Industries Served

Financial Services & InsuranceGovernment & Public SectorHealthcare & Life SciencesLegal & Professional Services

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