AI systems / production load
Most teams can demo an AI feature. Keeping one fast and correct at production traffic is a different job. That is the one we do.
A studio of engineers, not account managers. You talk directly to the people writing the code.
Taves is new. Our engineering approach is shaped by production systems our lead engineer runs elsewhere, at 200 rps sustained and p99 under 800ms. what that means in practice
ingest
validate · authenticate · rate-limit
route
queue · batch · apply backpressure
retrieve
vector + keyword search
infer
timeout + retry budgets
serve
stream · cache · fall back
16 modules, deployed and failed independently.
Work
Taves is new, so instead of asking you to take our word for it, here is working software with source and demos you can inspect yourself.
Bidirectional RPC across nine languages over raw TCP with MessagePack framing. No schemas, no .proto files, no codegen — export a function and call it from anywhere.
Open-source AI resume generator. A GitHub profile, LinkedIn and a job description go in; a tailored LaTeX PDF comes out. Self-hostable, with a free ATS parseability checker.
Resume analysis against job descriptions. Dual PDF extraction paths for messy documents, similarity scoring, and an LLM fallback for the cases scoring alone gets wrong.
Built and maintained by our lead engineer. Client work stays private, but we can walk you through it on a call.
Capabilities
AI systems are the core of what we do. The rest is what gets built around them.
16 modules
each deploys, scales and fails
independently of the rest
Have something different in mind? We'd love to hear it.
Let's build it togetherHow we think about scale
Our lead engineer designs and operates systems like this in his day-to-day work elsewhere. That experience is what he brings to Taves projects, and it is the reason we talk about latency budgets and failure modes instead of prompt templates.
Measured figures from the production system described here. It is not a Taves-built product, and the client and product behind it stay confidential.
request path
An AI feature that works in a demo falls over the moment real traffic arrives. Model calls are slow and occasionally fail, load arrives in bursts rather than evenly, and one blocked stage stalls everything behind it.
Split the work into modules that deploy and fail independently. Queue and batch at the boundaries so bursts flatten instead of cascading. Put explicit timeout and retry budgets around every model call, with fallbacks for when the budget runs out.
That system sustains 200 requests per second with p99 latency under 800ms. Bursts to 500 concurrent degrade gracefully rather than dropping the service, and error budgets stayed within 10% through mixed-load testing.
Taves has not yet shipped a system at this scale under its own name. What we bring is the engineering judgement above, applied to your problem.
Services
Everything it takes to get a product from idea to production. For AI at scale, see the architecture breakdown.
Fast websites and web apps, built to be maintained rather than rebuilt next year.
iOS and Android apps, native or cross-platform depending on what the project actually needs.
Interfaces tested on real users before they ship, not after complaints come in.
Smaller AI work for teams who need a feature shipped, not a pipeline operated. Same engineers, lighter scope.
APIs and databases that hold up when the traffic actually shows up.
A second opinion when you need one, and upkeep after launch so things keep working.
Contact
Tell us what you're building. We usually reply within a day.
Describe your project in a couple of sentences. Rough ideas are welcome; most projects start that way.
