AI agents, chatbots, LLM apps
Most companies experimenting with AI end up with a demo that never reaches production. Core AI is our engineering practice for closing that gap — we design, build, and operate AI agents and LLM-powered applications that hold up under real traffic, real edge cases, and real security requirements.
We treat an AI agent as a piece of production software, not a prompt. That means proper architecture: a retrieval layer that grounds responses in your actual data, guardrails that define what the agent can and can't do, structured escalation paths for anything outside its confidence threshold, and full audit logging so you can see exactly what it did and why.
Our AI engineers work across the stack — from the LLM orchestration layer down to the vector database and API integrations that connect an agent to your CRM, support desk, or internal tools. We've shipped agents that resolve customer support tickets, internal copilots that search across scattered company knowledge, and document-intelligence systems that extract structured data from unstructured text at scale.
If you already have an AI initiative that stalled in prototype, we can usually tell you why within a first working session — commonly it's missing retrieval grounding, no evaluation harness, or an architecture that can't scale past a demo.
What's included
Custom AI agents & copilots
Purpose-built agents for support, sales, internal operations, or research — designed around your workflows, not a generic template.
LLM-powered applications
Full applications with an LLM at the core: content generation tools, research assistants, decision-support systems.
Chatbots for support & sales
Conversational interfaces that resolve real queries against your live data, with clear escalation to a human when needed.
NLP systems & document intelligence
Extraction, classification, and summarization pipelines for contracts, support tickets, records, and unstructured text at volume.
RAG pipelines & knowledge retrieval
Retrieval-augmented generation built on your own knowledge base, with chunking, embedding, and ranking tuned for accuracy.
AI app testing & evaluation
Evaluation harnesses and regression testing so model or prompt changes don't silently degrade accuracy in production.
Related case study
Frequently asked questions
A focused first agent — one workflow, one integration — typically takes 3 to 6 weeks from scoping to production. Multi-workflow agents with several integrations run 8 to 12 weeks. We scope this precisely in the first working session.
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Ready to talk about core ai?
Tell us what you're trying to solve — we'll tell you exactly how we'd build it.