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. In plain words: it keeps answering even when everyone shows up at once.
A studio of engineers, not account managers. You talk directly to the people writing the code.
3 things you can verify right now
Taves is new, and says so. Our engineering approach is shaped by production systems led elsewhere: 200 rps sustained, p99 under 800 ms.
01 ingest: validate · authenticate · rate-limit 02 route: queue · batch · apply backpressure 03 retrieve: vector + keyword search 04 infer: timeout + retry budgets 05 serve: stream · cache · fall back, 16 modules, deployed and failed independently.
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
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, and one blocked stage stalls everything behind it. We fix that by splitting the work into modules that deploy and fail independently, queueing and batching at the boundaries so bursts flatten instead of cascading, and putting explicit timeout and retry budgets around every model call with fallbacks for when the budget runs out. Run that way, the system sustains 200 requests per second at a p99 under 800ms and survives bursts to 500 concurrent without dropping the service.
In plain words: it keeps answering fast even when everyone shows up at once, and one slow part never takes the whole thing down.
Measured over a 30-day continuous production window under mixed load, with error budgets below 10%. It is not a Taves-built product, and the client and product behind it stay confidential; the engineering approach is what transfers.
— Emaad Akhtar, Taves lead engineer
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.
Someone keeping scope, timeline, and stakeholders aligned so engineering time doesn't get burned on rework.
FAQ
It depends on scope, which is why we quote after a short call rather than off a website form. Rough ranges that we work within come up in that conversation; nobody gets a surprise invoice at the end.
We answer within a day. If the scope is clear, the first milestone — a real artifact you can open, not a deck — lands within a week or two of kickoff.
You do. We build on your repositories and your infrastructure, and the contracts are explicit that everything produced for your project belongs to you.
Because the code is on GitHub and you can read it today, the numbers above come from production systems our lead engineer still operates elsewhere, and we put you on a call with the engineer — no account manager runaround.
Usually, yes. We build with Next.js, React, React Native, Python, Go and PostgreSQL on AWS or your own cloud; if your platform is outside that set, we tell you honestly on the first call how deep we go.
The short version is in the architecture section above — independent modules, queueing and batching, timeout and retry budgets, observability. The long version is what the 30-minute technical call exists for.
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.
