Products
From first prompt to production serving
AI Studio is where models are explored, prepared and then served under load — one place that covers the experiment and the SLA, with governance in between.
Build
Explore and prepare models in one workspace
Work starts before anything reaches production. AI Studio gives your team a place to chat with a model while keeping session context, train or fine-tune on your own data, and promote a result to deployment when it is ready. The path from a first experiment to a deployed model is continuous, so nothing has to be rebuilt to cross from playground to production.
Model chat with context
Hold a working session with a model that remembers the thread, so exploration is a conversation rather than isolated calls.
Training and fine-tuning
Adapt a base model to your data and task, then keep the artefacts and settings that produced it.
One-step deployment
Promote a prepared model to a served endpoint without re-implementing it for production.
Prompts
Treat prompts as versioned assets
A prompt that works is a piece of intellectual property, not a string pasted into code. The prompt engine lets your team compose, test and version prompts, and the prompt library makes the good ones reusable across teams instead of rediscovered each time. Changes are tracked, so a regression can be traced to the exact revision that caused it.
Prompt engine
Compose and test prompts against real inputs, comparing revisions before one goes live.
Shared library
Publish proven prompts for reuse, so quality spreads instead of being reinvented per team.
Versioning
Every change is recorded, so behaviour can be tied back to the exact prompt revision that produced it.
Extend and register
Connect tools and track what you run
Models rarely act alone. AI Studio supports MCP servers, local and remote, so models can reach the tools and data they need through a standard protocol, and a model registry keeps a record of what exists and which version is in production.
MCP server support
Connect Model Context Protocol servers running locally or remotely, giving models governed access to external tools and context without bespoke integration code.
Model registry
A single record of every model and version, what it was trained on and where it is deployed, so nothing runs in production untracked.
Serve
Low-latency serving that holds under load
Production inference is judged on latency and throughput, not on how a model performed in a notebook. AI Studio serves models with high throughput and low latency, and keeps requests moving when a provider degrades. Routing sends each request to an appropriate model, load balancing spreads traffic, and failover moves work off an endpoint that has stopped answering — without the caller noticing.
Routing
Direct each request to the right model for the task, so cheaper models handle simple work and stronger ones handle the rest.
Load balancing and failover
Spread traffic across endpoints and shift it away from any that degrade, keeping the service responsive.
Caching
Serve repeated requests from cache to cut both latency and spend on work already done.
Govern
Guardrails, observability and cost in one place
Serving models to real users means controlling what goes in and out, seeing what is happening, and keeping the bill predictable. These are not separate products bolted on later — they are part of how AI Studio serves.
Guardrails and abuse control
Filter unsafe inputs and outputs and block misuse, so a served model does not become a liability.
Observability
See latency, errors, token usage and drift per model and per route, so problems are caught before users report them.
Rate limiting and cost control
Cap usage per client and route to cheaper models where quality allows, so spend tracks value instead of surprising you.
The path through the Studio
One continuous lifecycle
- 01
Explore
Chat with candidate models, engineer prompts and connect the tools a task needs.
- 02
Prepare
Train or fine-tune, register the result, and validate it against your own inputs.
- 03
Serve and govern
Deploy behind routing and failover, with guardrails, observability and cost control from the first request.
Questions
Frequently asked
- Do we have to use a specific model provider?
- No. AI Studio serves and routes across providers, so you can mix models and change providers without rewriting the applications and agents that depend on them.
- How does routing decide where a request goes?
- By rules you set on cost, capability and availability. Simple requests can go to a cheaper model, harder ones to a stronger model, and traffic shifts away from any endpoint that degrades.
- What do the guardrails cover?
- Input and output filtering, abuse and misuse controls, and rate limits per client. They apply at serving time, so every request through the Studio is subject to them.
- Can Nabhik AI agents use models served here?
- Yes. Agents call models through AI Studio, so routing, caching, guardrails and cost controls apply to agent traffic exactly as they do to any other client.
Serve a model under your own SLA
Bring a model and a workload and we will stand it up in AI Studio with routing, guardrails and cost controls, and show you the numbers.