Natural-language query agents
Agents that answer real questions against a live database — confirming customer queries, pulling records, and responding accurately instead of guessing.
AI, LLM & NLP Solutions
AI agents that query your real data, not a demo database
Eonovate builds production AI agents and natural-language query systems — sales assistants, co-pilots, and domain-specific AI — powered by leading LLMs and connected to your live business data.
The problem
Most "AI features" are demos, not production systems
It's easy to wire up a chatbot that sounds smart in a sales call. It's harder to build one that actually knows your inventory, your customers, your pricing, and can be trusted to answer correctly — in production, against live data, every day.
What we build
Agents built for a specific job, not a general demo
Sales agents & co-pilots
Task-specific agents — a sales AI that qualifies and engages leads, a co-pilot that assists your team through real workflows, built around what your business actually needs done.
Domain-specific AI assistants
Industry-trained assistants built for specialized terminology and workflows — like a veterinary AI assistant built for a livestock operation's specific needs.
Voice assistants & smart analytics
Voice-driven interaction and natural-language analytics on top of the same agent infrastructure, so your team can ask questions instead of building reports.
Featured project
AI Suite: a multi-agent system built for a livestock business
Our AI Suite deployment combined a Co-Pilot Agent, a Sales AI, a domain-specific Vet AI, Smart Analytics, a Voice Assistant, and media tools — all answering real queries against a live database, built on Claude, Llama 3.3, and Groq. It's a working example of what "AI-powered" means in production, not in a pitch deck.
A multi-agent suite — Co-Pilot, Sales AI, Vet AI, Smart Analytics, Voice Assistant — built on Claude, Llama 3.3, and Groq, confirming real customer queries against a live database.
3
LLMs in production: Claude, Llama 3.3, Groq
6
Agents in one suite
Related specialty
Enterprise AI Search & Knowledge Assistants (RAG)
A deeper specialization under the same umbrella: private AI search assistants that answer staff questions from your own documents, with citations, running entirely in your own environment if required. If your team needs to query policies, manuals, and internal knowledge rather than live transactional data, this is the deep dive.
A reference platform that found every expected document, matched every citation, and leaked nothing across permission tests — in about 60 milliseconds per response.
100%
Citation accuracy, demo dataset
0
Permission leaks across security tests
FAQ
Common questions about AI & LLM agent builds
Which AI models do you work with?
We build on leading LLMs rather than locking you into one — our AI Suite work has shipped with Claude, Llama 3.3, and Groq (for low-latency inference), choosing whichever model fits the task, cost, and response-time needs best.
Can the AI agent actually access and query our own data?
Yes — that's the core of what we build. Rather than a standalone chatbot, our agents connect to a live database so they can answer real questions about your actual business data, confirm customer queries, and take action, not just talk about it.
Is this just a chatbot, or something more?
More. A generic chatbot answers generic questions. What we build are task-specific agents — a sales agent that qualifies leads, a co-pilot that assists staff with real workflows, a domain-specific assistant (like a veterinary AI for a livestock business) — each connected to live data and built for a specific job.
Can you build an AI assistant specific to our industry?
Yes. One of our AI Suite deployments included a domain-specific "Vet AI" built for a livestock business alongside a general sales agent and co-pilot — the same approach works for any industry with specialized terminology or workflows.
Do you build voice assistants as well as text-based agents?
Yes, voice assistant capability has been part of our AI Suite deployments alongside text-based agents, smart analytics, and natural-language query tools — the right interface depends on how your team or customers actually want to interact with it.