Chat assistants grounded in your data. Voice agents that handle real phone calls. Sub-500ms response times so the conversation does not feel like talking to a machine.
Start a projectA 1.5-second pause before every response turns a conversation into a chore. We engineer sub-500ms response times with streaming synthesis and real-time interruption handling. The voice agent responds at the speed of a real conversation, not the speed of an API round-trip.
For text assistants, the goal is accuracy. Every response is grounded in your actual data using retrieval with hybrid search and reranking. Every answer cites its source. When the assistant does not know something, it says so instead of making something up.
Assistants that know your product because they are connected to your docs, support articles, and knowledge base. Source citations on every answer. Multi-turn memory so users do not repeat themselves. Context carries between sessions.
Voice agents that handle inbound and outbound calls. Appointment booking, intake, FAQ resolution, and transfers to humans. The agent knows when it is out of its depth and hands off cleanly instead of guessing.
Natural voice in 70+ languages. Voice cloning for brand consistency. Tone adapts to context so a billing dispute does not get the same delivery as a welcome call.
Real-time and batch transcription with speaker identification. Sentiment tracking, action items, and summaries from calls and meetings. Structured data from unstructured conversations.
AI that resolves support tickets end to end. Reads the history, checks the customer data, takes action in your systems, and writes the response. Closes tickets instead of just drafting replies.
Resolution rates, handle time, satisfaction scores, and escalation patterns. See what questions the AI handles well and where it struggles. The data to know what to fix next.
Define the use cases, connect the data sources, set accuracy targets, and decide what always goes to a human. Build test cases from real conversations.
A working assistant or voice agent by end of week one. Connected to your data, handling real queries, logging every interaction.
Improve retrieval accuracy, response quality, and latency. Eval runs on every change. Add handling for the edge cases that show up in production logs.
Production deployment with monitoring, cost tracking, and quality dashboards. Weekly review of conversations the AI handled poorly.
Production chat or voice agent connected to your data, running in your infrastructure, with monitoring.
RAG pipeline connecting your docs, support articles, or product catalog. Indexed, chunked, and retrieval-tested.
Voice selection, latency optimization, and interruption handling. Configured for your use case.
Resolution rates, response accuracy, cost per conversation, and latency. Updated in real time.
Test conversations covering common queries, edge cases, and adversarial inputs. Accuracy baseline tracked.