Service
AI Chatbots and Voice Agents
RAG-powered chatbots and voice agents that answer from your actual data. Not generic bots - production systems with evals, guardrails, and measurable outcomes.
Book a free strategy callWhat you get
RAG pipeline setup
Your AI answers from your docs, tickets, CRM, and knowledge base - not from generic training data. Hybrid search (semantic + keyword) for accurate retrieval.
LLM integration and prompt engineering
Model selected and tuned for your use case. Prompts versioned, tested, and reviewed like code.
Voice agent build (if applicable)
Inbound call handling using Retell AI or Twilio. Natural conversation flows with escalation to humans for edge cases.
Eval suite
Golden set of 50-100 real queries with expected answers. Accuracy, retrieval quality, and latency measured before and after every change.
Guardrails and content filters
Outputs stay on-brand and safe. Your AI does not go off-script under edge-case inputs.
Integration and deployment
Connected to your existing tools (Slack, email, CRM, helpdesk). Deployed to production with monitoring.
Who it is for
SaaS teams with support or sales workflows
Your support agents are handling the same questions repeatedly. Your sales team is slow to follow up. You want AI that uses your actual data to handle these workflows reliably.
- High-volume support with repetitive queries
- Sales teams needing AI-assisted outreach
- Products that need an embedded AI assistant
Enterprise teams adding AI to existing systems
You have existing infrastructure and compliance requirements. You need an AI layer that integrates cleanly, passes security review, and comes with audit trails.
- Enterprise SaaS with compliance requirements
- Teams replacing legacy chatbots with LLM-powered systems
- Multi-channel deployments (web, Slack, voice)
How it works
Data audit and RAG design (Week 1)
We map your data sources, define retrieval architecture, and build the first prototype. You see an accuracy baseline before we proceed.
Build and eval (Week 2-5)
RAG pipeline, LLM integration, and eval suite built in parallel. Every release is gated on accuracy thresholds.
Integration and guardrails (Week 5-7)
Connected to your tools. Guardrails added. Tested against edge cases and adversarial inputs.
Production deploy and handoff (Week 7-8)
Deployed to your infrastructure with monitoring. Your team trained on how to maintain and improve it.
Related work
Ready to ship your AI chatbot or voice agent?
Book a free 30-minute strategy call. We will map your data sources, define your eval criteria, and give you a clear delivery plan. No obligation.