What we deliver
We build production-grade RAG systems that go beyond simple demos. Our focus is on robust parsing, strict guardrails, and measurable accuracy improvements to ensure your AI works reliably with your internal data.
Domain retrieval, safe responses and production serving for your documents and data.
Retrieval over your data
+Search across files, databases, and APIs with robust parsing, chunking, and embeddings tailored to your domain.
Guardrails and safety
+Policies for PII handling, access control, and enforcement. We require citations and decline answers without sufficient sources.
Prompt and tool orchestration
+Orchestration to execute structured actions, workflows, and function calls based on user intent.
Document automation
+Automated generation of drafts such as KIDs, prospectuses, summaries, and citations with high accuracy.
Low latency serving
+Production-ready serving infrastructure with caching, tracing, usage analytics, and established SLOs.
Evaluation & Metrics
+rigorous testing with tricky evaluation sets, measuring precision at top-k, hallucination rates, and citation coverage.
Architecture at a glance
Ingest
+Connectors, parsing, and normalization of diverse data sources.
Index
+Embeddings, metadata extraction, filters, and freshness windows to keep data current.
Retrieve
+Hybrid search and reranking algorithms with strict score thresholds.
Generate
+Optimized prompts, templates, and function calls to guide model output.
Observe
+Feedback loops, red flags, metrics, and traces for continuous improvement.
When to use RAG
+Ideal when your corpus changes frequently, you need transparent citations, or want to reduce hallucination risk without heavy fine-tuning.
Use cases we implement most often
Real-world applications where our RAG systems deliver measurable results.
KID and Prospectus Generation
+Drafts and summaries from a controlled repository. Stats: ~60% time reduction for first drafts, p95 latency ~1.2s.
RFP and Tender Responses
+Responses based on references and internal policies. Stats: Drafting time reduced from 2 days to 3 hours with high accuracy.
Support and Compliance
+Answers with mandatory citations from procedures and registries. Stats: ~70% fewer incorrect answers after adding reranking.
Research Assistant
+Combines files, databases, and APIs with paragraph-level sources to provide comprehensive answers.
Process
Our Case Studies
Generative AI & RAG Systems - Frequently Asked Questions
- What is a RAG system and when do I need one?
- RAG (Retrieval-Augmented Generation) combines a language model with a retrieval layer over your own data. You need it when your data changes frequently (making fine-tuning impractical), when you need responses grounded in specific documents with citations, or when you want to reduce hallucination risk without the cost and complexity of full model training.
- How do you reduce hallucinations in production RAG?
- We use hybrid search (vector + keyword) with reranking and strict score thresholds to ensure only high-confidence chunks are passed to the model. We enforce citation requirements - the model must reference a source chunk for every factual claim. Responses without sufficient grounding are declined rather than guessed.
- What data sources can a RAG system retrieve from?
- We build connectors for document stores (PDFs, Word, SharePoint), structured databases (SQL, APIs), and real-time data feeds. Parsing and chunking strategies are tailored per data type - a legal contract requires different handling than a CSV report. We also handle access control so users only retrieve what they are authorized to see.
- How do you measure RAG system quality?
- We measure retrieval quality (precision at top-k, recall on a held-out set), generation accuracy (answer correctness vs reference), hallucination rate (claims not grounded in retrieved context), and citation coverage (fraction of answers with verified sources). These are tracked in production, not just during development.
