A chatbot responds to messages. An agentic AI system pursues a goal — it can break a task into steps, call tools and APIs, check its own work, and adapt when something doesn’t go as planned, often without a human in the loop for every step. Chatbots are one possible interface to an agentic system, not the same thing.

RPA automates repetitive, rule-based tasks with fixed logic — it breaks when the input format changes. AI-driven automation uses language models to handle variability, ambiguity, and judgment calls that RPA cannot. Many of our clients use both: RPA for stable, structured tasks and AI for the messy, judgment-heavy ones.

A focused pilot on a single use case typically takes 6–10 weeks from kickoff to a working proof of value. Moving from pilot to full production scale — with monitoring, governance, and integration into existing systems — typically adds another 4–8 weeks depending on complexity.

We build vendor-agnostic architectures wherever possible, and have delivered projects on OpenAI, Azure OpenAI, and Anthropic Claude. We help you choose based on your data residency requirements, existing cloud commitments, and cost profile — not vendor preference.

We design architectures that keep your sensitive data within your control — using retrieval-augmented generation (RAG) over your own data stores rather than fine-tuning models on confidential information, and using enterprise agreements with model providers that exclude your data from training. Governance and guardrails are part of every engagement, not an afterthought.