Assistants Grounded in Your Data, Not Just Trained on the World's
Build customer-facing and internal AI assistants connected to your own knowledge, systems, and conversation history with guardrails, human handoff, and the analytics that show what is actually working.
System 05 - what we ship
What Are AI Chatbots & Assistants?
A chatbot built on a general-purpose model answers general-purpose questions. An assistant grounded in your data, your policies, and your live systems answers the questions your customers and employees actually have. The difference is retrieval, connection, and context to your knowledge base, your CRM, your product database, and the conversation that happened yesterday. That is what we build.
- Best for
- Customer support
- Best for
- Employee self-service
- Best for
- Patient and candidate engagement
Customer support teams handling high volumes of repeatable queries
Where the same questions come in every day and the answers exist but finding and delivering them consistently requires more human time than the work justifies.
Internal teams where employees spend time searching for information that already exists
Policy documents, product specifications, process guides, and institutional knowledge that teams cannot find quickly enough to use effectively.
Patient, candidate, or customer engagement workflows
Where consistent, accurate, timely communication at scale matters and the volume makes manual delivery impractical.
Companies that have tried generic chatbot platforms and found the quality or configurability insufficient
Platform chatbots are built for generic cases. Useful assistants are built around your specific queries, your specific knowledge, and your specific escalation paths.
- 01
Use Case Definition
We define the specific queries the assistant needs to handle, the systems it needs to access, and the conditions under which it should hand off to a human rather than attempt an answer.
- 02
Knowledge and Integration Design
RAG pipeline for document-grounded responses. API connections to live systems for real-time data, such as inventory, account status, and calendar availability. Conversation history design for multi-turn interactions that don't lose context between messages.
- 03
Channel and Interface Build
Web widget, WhatsApp, Slack, or custom interface, whichever channel your users already use. Multi-channel from one underlying system where the query volume and user base justify it.
- 04
Guardrail and Handoff Design
What the assistant should decline. How it surfaces uncertainty rather than answering confidently from insufficient context. How the handoff to a human carries the conversation history, the user's information, and a summary of what the assistant already attempted.
- 05
Analytics and Improvement
Conversation analytics that show what users are asking, where the assistant is failing, and what knowledge gaps to address in the next iteration. Improvement is data-driven, not impression-driven.
- Customer support and service desk automation
- HR and employee self-service portals
- Patient communication and pre-appointment guidance
- Candidate engagement during recruitment workflows
- Product and policy information access for sales teams
- Internal IT helpdesk and first-response triage
RAG-first, not prompt-first
Useful assistants ground their responses in your actual documents and systems. We build the retrieval layer first and evaluate citation accuracy before users see any output. Hallucination is a retrieval problem before it is a model problem.
Multi-channel production experience
WhatsApp, web widget, and Slack integrations are production work this team has done repeatedly, not configurations we are proposing to figure out during the build.
Conversation history and memory by design
Assistants that lose context between turns frustrate users and break workflows that span multiple messages. We design conversation state into the architecture from the start, not as a feature requested after launch.
Handoff that carries context
The escalation to a human agent needs to work. That means passing the conversation history, the user's account data, and a summary of what the assistant already tried, so the human picks up where the assistant stopped, not from the beginning.
Next step · 06
MCP Servers & AI Integrations
Model Context Protocol servers that expose your internal systems (CRM, CMDB, ERP, databases) as secure, permissioned tools any AI client can use. Build the integration once. Every assistant and agent that follows can use it without a new integration project.