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AI & Intelligence
LLM application development
We design and build applications on large language models - assistants, copilots, extraction and automation - with the model choice, evaluation and cost control that keep them dependable after launch.
- Part of AI & Intelligence
- NDA on request
When it fits
Is this right for you?
LLM applications are the right fit when work involves reading, writing or reasoning over text: drafting, summarising, extracting fields from documents, classifying requests or helping people find answers. We pick the smallest model that meets the quality bar, so costs stay predictable.
What’s included
Model selection
Hosted and open-weight models compared on your tasks for quality, latency, cost and privacy.
Prompt and output design
Structured outputs, validation and retries so downstream systems can trust the results.
Fine-tuning when it pays
Adapting open models to your domain or format when prompting alone isn't enough.
Self-hosting options
Open models served in your own cloud when data must stay in your environment.
Evaluation and red-teaming
Test sets, automated scoring and adversarial checks before every release.
Cost and latency control
Caching, batching, routing and model tiers to keep spend predictable.
How we build it
Four steps, no surprises.
- 01
Frame the task
Inputs, outputs, quality bar and constraints agreed with your team.
- 02
Compare models
Candidates scored on a test set built from your real examples.
- 03
Build the product
The LLM wrapped in a reliable application with your data and systems.
- 04
Operate and tune
Monitoring, feedback and periodic re-evaluation as models improve.
Tools we use
The stack behind it.
- LLMsLanguage models
- Hugging FaceOpen models
- vLLMModel serving
- OllamaLocal models
- llama.cppOn-device inference
- PyTorchDeep learning
- PythonLanguage
- Next.jsReact framework
FAQ
Common questions.
Can we run models in our own cloud?
Yes. We can serve open-weight models inside your cloud account so prompts and data never leave your environment.
Will we be locked into one provider?
No. We build behind a thin model layer so providers and models can be swapped as prices and quality change.
How do you control costs?
By routing simple requests to smaller models, caching repeated work, trimming context and tracking cost per request from day one.
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Let’s talk
Ready to talk about LLM application development?
Tell us what you're building. We'll come back with a clear plan, the right team and an honest timeline.