What we can help with

Agents that understand requests, use tools, work with company data, and execute real workflows — for support, research, document processing, operations and lead qualification.

• Agent architecture and implementation
• Tool and API integrations
• Human-in-the-loop approvals
• Multi-agent and subagent workflows
• Scheduled and event-driven automation
• Agent tracing and evaluation

Evaluate and deploy open models such as Qwen and Gemma on local GPUs or cloud infrastructure when privacy, control, or cost matters.

• Open-model feasibility studies
• Local and private AI deployments
• llama.cpp, vLLM, and Ollama infrastructure
• GPU inference optimization
• Quantization and model packaging
• Hybrid local + cloud model architectures

Turn an AI prototype into a production-ready system, with the backend infrastructure to operate it reliably at scale.

• Multi-provider LLM integrations
• Model routing and fallbacks
• Structured outputs
• Embeddings and semantic search
• OCR, vision, and document processing
• Authentication and rate limiting
• Observability and tracing
• Cost and latency optimization

For classification, routing, moderation, intent detection and extraction, smaller specialised models are often faster and significantly cheaper.

• Dataset preparation
• LoRA / SFT fine-tuning
• Classifier training
• ModernBERT and embedding models
• Evaluation pipelines
• ONNX and INT8 optimization
• CPU-friendly inference

A focused POC on your real workflow and data to find what actually works before a larger investment — working software, measurable results.

• Cloud vs open models
• Prompting vs fine-tuning
• Agent vs deterministic workflow
• Model quality and reliability
• Infrastructure requirements
• Latency and operating costs

For teams still figuring out where AI fits. Leave with a clear technical direction instead of a generic AI strategy deck.

• Map the workflow
• Identify where AI provides value
• Select the right models and architecture
• Define a practical implementation plan