Forward-deployed AI engineering.Systems your team can own.
AI agents. Build. Evaluate. Run. We embed with your team to build agents, integrations and the systems behind them. We evaluate new and existing agents with the Harness Method and stay on monthly to run and improve them after launch. Architecture, implementation and verification run through to a documented handoff.
AI-native engineering · La Jolla, California
Daniel Voigt
Founder & CEO
Daniel Voigt is the founder of TeqEngine. His work spans architecture and implementation for large enterprises and venture-backed startups, from agent platforms to the infrastructure behind large consumer applications.
He served as CTO of AZZLY, a regulated EHR and revenue-cycle platform, through its acquisition by an a16z-backed company. Daniel and the TeqEngine software-development team built the core EHR/RCM system, and he led technical diligence for the acquisition. His post-acquisition work included an enterprise data layer supporting more than 100 reports and analytics used by hospitals and healthcare organizations.
As lead engineer and architect, he built the backend, infrastructure and messaging system behind a consumer app that reached #1 on the iOS App Store, repeatedly ranked in the top three on iOS and Android, and handled more than 100 million messages in a single day.
Across engagements, TeqEngine has built AI-native platforms for well-funded startups with substantial active user bases, spanning web and iOS applications, backend systems and cloud infrastructure. Recent work includes agent platform engineering across backend AI, APIs and infrastructure, with 50+ MCP tools built and support for three model providers: Anthropic, OpenAI and Google. The platform has been used by large enterprises and a unicorn startup, and has operated at high volume.
We build with agentic engineering: bounded tasks, explicit verification and design review. The result is software your team can understand, operate and keep changing, with the option to keep us on to run it.
Background
Areas of expertise
Engineering approach
How we deliver
From a defined problem to working software your team can own.
AI-native engineering from architecture through release.
We design the architecture, implement the system and verify the result against agreed requirements. Decisions, evidence and open risks stay visible throughout the work.
A working system your team can own.
Project guidance, tests, runbooks and a demonstrated handoff make the next change possible without a private history of how the system was built.
How we work
Four things that decide every engagement
AI should solve a real business problem.
If the workflow does not matter when it is done manually, automating it with an agent will not make it matter.
Launch readiness is a feature, not a phase.
Evals, permissions, observability and rollback are designed at the start. Retrofitting them before launch is how launches slip.
Rigor and speed are not a tradeoff.
Working software every two weeks, with the gates in place. The discipline is what makes the speed repeatable.
Recommend what the evidence supports.
Including a different model, the platform you already own, or no agent at all. We don't resell models or platforms.
Start with a conversation.
Tell us what you are building. We will start with what exists today and what has to be true before it reaches your users.