AI Integration & Infrastructure

AI Integration & Infrastructure

Connect AI capabilities to your existing systems — legacy, cloud, or hybrid — without disruption.

LLM integration is the engineering work of embedding large language model APIs and AI microservices into existing enterprise systems — ERP, CRM, legacy databases, and cloud platforms — so AI capabilities become part of daily workflows rather than separate tools. Remolda designs AI API integration architectures that handle authentication, rate limiting, data residency, and fallback logic so your production systems stay reliable. Organizations we integrate go from proof-of-concept to production AI features in 6–10 weeks, with zero downtime to existing operations.

AI Operations (AIOps) & Monitoring

Remolda builds AIOps practices that keep your AI systems reliable, performant, and aligned with business objectives — monitoring model performance, managing data drift, orchestrating updates, and ensuring that deployed AI continues to deliver value after the initial implementation.

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AI API Design & Integration

Design and implementation of AI APIs that connect enterprise systems, expose model capabilities securely, and integrate new AI services into existing technology landscapes without requiring wholesale system replacement.

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Cloud AI Infrastructure & Migration

Structured migration of AI workloads to cloud environments with the right architecture for performance, security, and compliance — including Canadian data residency requirements.

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AI-Ready Data Pipelines

Design and implementation of reliable, governed data pipelines that consistently deliver clean, well-structured data to AI systems — because model quality is only ever as good as the data that feeds it.

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Legacy System AI Integration

Connect modern AI capabilities to existing legacy systems without full replacement. Extract value from decades of organizational data while maintaining operational continuity.

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Frequently asked questions

What's the difference between RPA and AI workflow automation?
RPA (Robotic Process Automation) automates UI-driven tasks by mimicking user clicks and keystrokes in legacy applications. AI workflow automation replaces those interactions with API-first agents that can also handle judgement steps (extracting data from unstructured documents, classifying customer intent, routing exceptions). RPA is brittle to UI changes and is being progressively replaced or augmented by AI agents — most modern automation projects combine both.
Which business processes are good candidates for AI automation?
Good AI automation candidates have four properties: high volume (1000+ executions per month), repetitive structure with bounded variation, an existing digital input (document, form, ticket, message), and a clear definition of correct output. Examples include invoice processing, claim triage, contract review, customer support routing, and compliance screening. Processes that lack any of these properties are better left manual or handled with case-by-case AI assistance.
How long does an AI automation project take from kickoff to production?
A typical AI automation project goes to production in 8–14 weeks: 2 weeks of process audit and acceptance criteria definition, 4–6 weeks of build and integration, 2–4 weeks of pilot with full output review, 1–2 weeks of cutover. We do not run multi-quarter automation projects — if scope requires that timeline, we split into independently shippable waves instead.
What is intelligent document processing (IDP), and how does it work?
Intelligent document processing (IDP) is the automated extraction of structured data from unstructured documents — invoices, contracts, clinical notes, claims forms — using OCR, NLP, and large language models. The modern IDP pattern uses an LLM with structured output schemas, supplemented by retrieval against reference documents, replacing the rule-based templates that older OCR/IDP tools relied on. This handles document variation that template-based systems break on.
How do you handle errors and edge cases in AI automation?
We design every AI automation around an explicit confidence threshold: outputs above the threshold flow through autonomously, outputs below the threshold route to a human reviewer with the AI's reasoning attached. The reviewer's decisions feed back into the system as labeled training data. Acceptable autonomous-flow rates depend on the workflow — invoice processing typically reaches 80–90%, regulated workflows like medical coding stay closer to 50–70% with the rest staying under human supervision.
Do AI automation systems require ongoing maintenance?
Yes — every production AI automation needs continuous monitoring of three signals: drift in input distribution (are documents changing?), drift in model behavior (is the LLM provider releasing new versions?), and drift in business definitions (have downstream consumers changed what they expect?). We bake monitoring into every deployment and budget 10–20% of initial implementation cost annually for retuning. Automations that are deployed and abandoned silently degrade over 12–24 months.

Industries Served

Financial Services & InsuranceHealthcare & Life SciencesLegal & Professional Services

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