Generative AI and RAG Systems

Domain retrieval, safe responses and production serving for your documents and data.

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

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Search across files, databases, and APIs with robust parsing, chunking, and embeddings tailored to your domain.

Guardrails and safety

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Policies for PII handling, access control, and enforcement. We require citations and decline answers without sufficient sources.

Prompt and tool orchestration

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Orchestration to execute structured actions, workflows, and function calls based on user intent.

Document automation

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Automated generation of drafts such as KIDs, prospectuses, summaries, and citations with high accuracy.

Low latency serving

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Production-ready serving infrastructure with caching, tracing, usage analytics, and established SLOs.

Evaluation & Metrics

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rigorous testing with tricky evaluation sets, measuring precision at top-k, hallucination rates, and citation coverage.

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Architecture at a glance

Ingest

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Connectors, parsing, and normalization of diverse data sources.

Index

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Embeddings, metadata extraction, filters, and freshness windows to keep data current.

Retrieve

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Hybrid search and reranking algorithms with strict score thresholds.

Generate

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Optimized prompts, templates, and function calls to guide model output.

Observe

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Feedback loops, red flags, metrics, and traces for continuous improvement.

When to use RAG

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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

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Drafts and summaries from a controlled repository. Stats: ~60% time reduction for first drafts, p95 latency ~1.2s.

RFP and Tender Responses

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Responses based on references and internal policies. Stats: Drafting time reduced from 2 days to 3 hours with high accuracy.

Support and Compliance

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Answers with mandatory citations from procedures and registries. Stats: ~70% fewer incorrect answers after adding reranking.

Research Assistant

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Combines files, databases, and APIs with paragraph-level sources to provide comprehensive answers.

Process

1

Discovery (1 week)

We define the scope, identify data sources, establish guardrails, and agree on evaluation metrics.

2

PoC (4 weeks)

Time to first PoC with measurable uplift over baseline. We prove the retrieval quality and response accuracy.

3

Build (6-10 weeks)

Full implementation including ingestion pipelines, index setup, prompt engineering, and UI integration.

4

Launch and Monitor

Production deployment with continuous monitoring of hallucination rates, latency, and user feedback.

Get your Blockchain or AI roadmap in 24 hours

One 30-minute call with our engineer can save you weeks of uncertainty.

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European UnionEuropean Funds

NEXT ENTERPRISES LIMITED LIABILITY COMPANY

is implementing the project „Audit of smart contracts using artificial intelligence”

Project co-financed by the EU:
3 090 156,39 PLN

Generative AI and RAG Systems - Retrieval, Guardrails, Document Automation | Nextrope