Custom-tuned AI, end to end.
A general-purpose model is the starting point, not the answer. We tune open-weight models to fit your business, and build on Anthropic's Claude where frontier reasoning matters — backed by full-stack product engineering.
LLM Fine-Tuning & Local Deployment
Our core service: we adapt open-weight models — Gemma, gpt-oss, Llama, and more — to your domain, and deliver them where your data lives.
Simulate in VMs
Candidate models — Gemma, gpt-oss, Llama, and more — run side by side in isolated VM environments on your real tasks and data.
Benchmark report
You receive a per-model report: task quality, latency, cost per thousand requests, and GPU footprint — evidence you keep, whatever you decide.
Model fine-tuning
Once the model is chosen, we adapt it to your domain and deliver it where your data lives.
Private serving
Quantized, optimized models served in your VPC or fully on-premises with vLLM or Ollama. Your data and your model weights never leave your infrastructure.
Anthropic-Based AI Development
We build on Claude, Anthropic's frontier model family, as our primary development platform.
Agentic systems & assistants
- Claude-powered agents and copilots
- Tool use and MCP integrations
- Multi-step workflow automation
- Human-in-the-loop design
Knowledge & RAG systems
- Retrieval-augmented generation
- Document understanding pipelines
- Enterprise search assistants
- Citation and grounding controls
Evaluation & reliability
- Task-specific evaluation suites
- Prompt and model regression testing
- Guardrails and safety policies
- Cost and latency optimization
Full-Stack Product Engineering
The application layer that turns a model into a product.
Frontend
- React & Next.js
- Responsive UI/UX
- Design systems
- Performance optimization
Backend & infrastructure
- Node.js, PHP & Python services
- API design and integration
- MySQL and data architecture
- Cloud deployment and operations
How we engage
Start small, prove value, then scale.
Pilot
A fixed-scope, fixed-price pilot that ships a working system against agreed success metrics — typically in 2–6 weeks.
Build
Full implementation with evaluation gates, security review, and staged rollout.
Operate
Ongoing monitoring, model updates, and continuous improvement as your usage grows.