LLM Development
LLM selection and deployment
We select, configure and serve the right language model for your use case — hosted API or self-hosted, with data staying where you need it.
The problem
The default is to pick the most famous model and start calling its API. That works until you look at the bill after three months of real traffic, or until your legal team asks where your customer data is going, or until you notice that a smaller open-weights model would have done the same job for a fraction of the cost. Model choice is a real decision with real consequences for cost, latency, data residency and what you can build on top. Most teams make it in ten minutes because nobody has laid out the actual trade-offs.
You know exactly what you are running, what it costs, and where your data goes.
Built for: Teams who need to make a deliberate model choice rather than defaulting to whatever API is easiest to sign up for.
What we deliver
Model benchmarking on your task. We run the candidates — closed API and open-weights — against your actual inputs rather than general benchmarks. Quality on your task is what matters, not position on a leaderboard.
Cost modelling at your scale. We project cost per thousand requests at your expected volume for each option, including the infrastructure cost of self-hosting when it is relevant.
Latency and fallback design. For user-facing features, latency matters. We design the serving architecture so slow model calls do not block your interface, and set fallbacks for when a model endpoint is unavailable.
Data residency when it matters. If your data cannot leave India or cannot enter a US data centre, we structure the model serving accordingly — open-weights self-hosted on Indian cloud infrastructure, or a hosted API with the right data processing agreement.
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Not sure which of these fits? See the whole generative ai practice, or read what we build for your industry.
Tell us what’s slow.
Describe the job eating your team’s day. We’ll tell you straight whether an agent is the right fix — and if it isn’t, we’ll say so.
Frequently asked questions
- Should we always use the biggest, most capable model?
- Rarely. Bigger models cost more per call, are slower, and for many well-defined tasks — classification, extraction, short drafting — a smaller model does the job equally well. We start from your task requirements, not from model prestige.
- What does self-hosting a model actually involve?
- A GPU instance on a cloud provider you already use, a serving framework, monitoring, and someone to handle updates. It makes sense when your call volume is high enough that the infrastructure cost beats the API cost, or when your data cannot leave your own environment. We tell you when self-hosting does not make sense financially.
- Can you help us switch models if a better one becomes available?
- Yes. We build the model layer with the interface abstracted so swapping the underlying model is a contained change. You are not locked to the first model we pick.