Build / Applied AI & voice
AI, voice and intelligent automation built as production systems
We use models and providers as components—not as the architecture. The work includes data, orchestration, evaluation, permissions, fallbacks, cost controls and the surrounding product experience.
Intended outcomes
What this engagement is designed to achieve.
- A bounded AI use case with task-level acceptance criteria
- Measured latency, quality, reliability and unit economics
- Provider-independent boundaries where they matter
- Human escalation and operational control for uncertain behavior
Engineering capabilities
Capabilities applied where the system needs them.
We select technology based on the product, team and operating constraints. The list reflects established areas of delivery, not a mandatory stack.
- LLM applications
- Agents & tools
- RAG & semantic search
- Streaming STT/TTS
- Real-time voice
- Translation
- Model orchestration
- Evaluations & guardrails
When to bring us in
When this service is a good fit.
- Adding AI or semantic search to an existing product
- Building a real-time voice, speech or translation experience
- Automating a multi-step workflow with agents and tools
- Replacing a brittle chatbot or AI prototype
What you receive
Concrete deliverables and a clear path forward.
- AI feasibility and workflow design
- Voice, STT, TTS and real-time orchestration
- RAG, search, agents and tool integration
- Evaluation, tracing, guardrails and fallback
- Model routing, caching and cost optimization
Questions
Common questions.
Are you tied to one AI provider?+
No. We choose hosted, open or hybrid models from the product constraints. OpenAI, Anthropic and other APIs are implementation options—not the service being sold.
How do you keep an AI project from becoming a demo?+
We define representative tasks, failure modes, evaluation data, human escalation and unit economics before expanding the build.
Direct access to the delivery team