Applied AI
Private LLM platform
Model serving on your own accelerated hardware, with routing, quotas, caching and observability, so the data never leaves.
8–12 weeksTypical duration
The problem this solves
You want to use language models on data that cannot go to a third party, and every proposal so far has been an API key.
What you receive
Artefacts you can hold, and that you can accept or refuse — never a list of activities.
- Model serving on your hardware, with the model selection justified against your latency and quality bar
- A routing layer presenting one interface across local models and external providers
- Per-team quotas, caching and rate limiting, with usage attributed
- Full request observability, including token accounting and cost per team
Also in applied ai
Retrieval-augmented knowledge systems
A retrieval layer over your corpus, with ingestion, chunking and evaluation treated as engineering rather than as configuration.
Agentic workflow engineering
Multi-step agents with tools, bounded autonomy, human checkpoints and a full audit trail of what the agent did and why.
Voice agents
Speech recognition, a reasoning layer and speech synthesis assembled into an assistant that answers inside a human conversational turn.