Generative AI that works in production, not just prototypes.
We build RAG systems over private data, fine-tune where it truly improves outcomes, and implement agent workflows with evaluation, guardrails, and monitoring.
RAG (Retrieval-Augmented Generation)
What it's for: Internal knowledge assistants, support copilots, policy Q&A, document intelligence, enterprise search.
What you get:
- Ingestion pipelines
- Chunking strategy
- Embeddings
- Vector database
- Access control
- Citations/source links
- Evaluation harness
- Monitoring
Fine-tuning & customization
What it's for: Specialized tone/format, domain patterns, structured outputs, higher consistency.
What you get:
- Dataset prep
- Safety filters
- Benchmarking
- Validation
- Deployment guidance
Agents & automation
What it's for: Workflows that take action (ticket triage, document processing, approvals, CRM updates).
What you get:
- Tool calling
- Approval steps
- Audit logs
- Role-based permissions
- Integration into business systems
Trust & Safety
Frequently asked questions
Most teams should start with RAG + evaluation. Fine-tuning is recommended only when it materially improves quality or consistency, and we'll help you evaluate that.
Yes. Our RAG implementations can return source links/snippets to reduce risk and improve trust. This is especially important for compliance-sensitive use cases.
We mitigate hallucinations through careful retrieval design, evaluation harnesses, confidence thresholds, and fallback mechanisms. No AI system is perfect, but we build for accountability.
Want to validate GenAI with measurable quality?
What happens next