What's the difference between an AI assistant and an agent?

An assistant answers questions and helps users — chat-style. An agent takes actions on behalf of the user — booking, fetching, submitting, orchestrating. Most useful systems are a mix of both, with clear boundaries on what the agent is allowed to do without human approval.

How do you stop the assistant from hallucinating?

Three layers: RAG over your authoritative content so answers are grounded in real documents; system-prompt guardrails that refuse out-of-scope questions; and confidence-based human handoff when the model isn't sure.

Which LLM do you use?

Whatever fits the use-case. Anthropic Claude or OpenAI GPT for production assistants where quality matters; open-weights models (Llama, Mistral) when data sovereignty or cost matters more. We benchmark before committing.

Can the assistant work on private data without sending it to OpenAI?

Yes. We can run on Azure OpenAI (with private endpoints), AWS Bedrock, or self-hosted open-weights models. We architect for data sovereignty when compliance demands it.

Service

AI assistants & agents

Custom AI built on top of your data — RAG, intent classification, multi-turn flows, with grounding and guardrails so it doesn’t hallucinate. Deployed to your cloud, source code in your repo.

What we build

Six kinds of AI system we ship

RAG over your docs

Internal knowledge-base assistants that answer from your own content — not hallucinated. Built with vector search and citation links.

Customer-facing assistants

Web-embedded chat or support widget. Grounded in your help docs, with confidence-based human handoff.

Action agents

Agents that take actions on behalf of users — bookings, lookups, form submissions — with explicit tool-call boundaries and approval gates.

Document extraction

Structured data extraction from PDFs, invoices, contracts, scanned forms. Output goes straight into your database or workflow.

Intent routing

Classify inbound messages, emails, or tickets and route to the right team or sub-bot. Replaces brittle keyword rules with semantic understanding.

WhatsApp + voice agents

Same brain across channels. WhatsApp Business API, web chat, or voice (Twilio + speech models) sharing one knowledge base.

How we keep it grounded

Three guardrails against hallucination

The difference between a demo and a production system.

1. Retrieval before generation

We pull relevant chunks from your authoritative content (docs, knowledge base, product catalog) and feed only those into the LLM. The model answers from real text, with citations.

2. Scope guardrails

System prompts and pre-checks refuse out-of-scope questions cleanly. The bot says “I don’t know” when it doesn’t — not a confident fabrication.

3. Confidence-based handoff

When the model’s answer confidence drops below a threshold, the conversation routes to a human with full context. Better to escalate than to hallucinate.

Stack

Models & infrastructure

Anthropic Claude (Sonnet, Opus, Haiku)
OpenAI GPT-4o, GPT-4.1, o-series
Open-weights: Llama, Mistral (when data sovereignty matters)
Vector DBs: pgvector, Pinecone, Qdrant, Weaviate
Azure OpenAI / AWS Bedrock for private endpoints
LangChain or direct SDK — whichever fits the complexity
Observability: LangSmith, Helicone, or custom traces
Eval harness for regression-testing prompt changes