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.