RAG Development Services
From data ingestion and vector search to evaluation and observability, Morton Technologies delivers end-to-end RAG pipelines. Our solutions focus on accuracy, latency, and safety—with clear citations and guardrails for enterprise use.
- Security-first: Encryption, role-based access, and auditability.
- Production reliability: Smart caching, fallbacks, and monitoring.
- Evaluation: Golden sets, offline/online metrics, and human feedback.
- Measurable ROI: Deflection, conversion, TTR, and analyst hours saved.
What We Build
Practical RAG solutions your teams can run and trust.
- Enterprise search & knowledge assistants with citations
- Agentic workflows for support, ops, and compliance
- Domain-specific retrieval over docs, tickets, logs, and DBs
- Secure connectors & pipelines for ingestion
- Evaluation harnesses, dashboards, and alerts
- Performance tuning for speed and quality
Industries We Serve
RAG adapts to your data and workflows. Explore use-case guides for your domain:
- Healthcare — clinical support, research, and patient education
- Manufacturing — predictive maintenance and quality
- Legal — research, discovery, contracts, and compliance
- Sales & Marketing — insights, personalization, and analysis
- CRM — lead scoring, retention, and service automation
- What is RAG? — primers & concepts
How We Deliver
1) Discover
Map data sources, security requirements, and success metrics. Define answer formats, citations, and guardrails.
2) Implement
Build ingestion, embeddings, retrieval, and generation. Integrate with your stack and CI/CD.
3) Measure
Run evaluations, tune prompts/indexes, and monitor latency/quality. Iterate toward KPIs.
Frequently Asked Questions
RAG grounds answers in your approved sources at query time, returning citations and reducing hallucinations—ideal for compliant, high-stakes use cases.
Yes—documents, tickets, logs, databases, and APIs. We design ingestion pipelines and schemas to keep retrieval fast and precise.
We build golden test sets and track retrieval/answer metrics (precision, coverage, groundedness), plus human reviews, then iterate until KPIs are met.