RAG Systems

AI grounded in your knowledge, with answers you can trust

Retrieval-augmented generation connects AI to your own data, so answers are accurate, current, and traceable to a source, instead of confidently made up. We engineer RAG for the retrieval quality that production actually requires.

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OverviewAI · RAG Systems

Most AI accuracy problems are really retrieval problems

When an AI gives a wrong answer, the instinct is to blame the model. More often, the real problem is that the model never had the right information in front of it. Retrieval-augmented generation fixes that: instead of relying on what a model learned in training, RAG retrieves the relevant passages from your actual knowledge at query time and grounds the answer in them. Done well, it produces responses that are accurate, current, and traceable to a source, and it stays current automatically as your content changes, with no retraining.

But RAG is deceptively hard to do well. Naive implementations retrieve the wrong passages, miss context, or chunk documents badly. The answer is only as good as what was retrieved. The work is in the retrieval: how content is parsed, chunked, embedded, indexed, ranked, and re-ranked, and how the system handles the queries where simple similarity search falls short. Villaex engineers RAG for retrieval quality, which is what separates a system that's reliably accurate from one that's confidently wrong.

PipelineAI

From input to outcome

What an AI engagement actually moves through, stage by stage, and what has to be true at each boundary before the next stage can run.

Source data

Documents, events and system records are collected, cleaned and labelled.

In
Your systems
Out
Prepared corpus

Model & retrieval

Models are tuned and grounded in that corpus so answers trace to a source.

In
Prepared corpus
Out
Grounded output

Evaluation & guardrails

Automated evals, safety checks and human review where stakes demand it.

In
Grounded output
Out
Verified output

Production system

Served behind an API with monitoring, cost ceilings and a rollback path.

In
Verified output
Out
Decisions in product

Feedback edge

Production outcomes re-enter the evaluation set and the next round of tuning.

Selected Work

Proof, in production

Real systems, shipped and running: the interface, the data model, and the workflows they replaced.

AI Document Intelligence · Insurance

We cut underwriting review by 89% and made it audit-ready

A Series B insurance platform was capped by how fast underwriters could read unstructured documents to pull a handful of decision-critical fields. We built a document-intelligence pipeline with OCR, a fine-tuned extraction model, and a RAG layer that grounds every field in its source passage. A human-in-the-loop review UI lets underwriters confirm in seconds.

  • Fine-tuned extraction grounded to source passages for full auditability
  • Confidence scoring that routes only genuine edge cases to a human
  • Every decision logged to continuously improve the model
aptiva.app/submissions/SUB-40912
ASubmissionsSUB-40912liability_cert.pdf
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Northbridge Mutual

Certificate of Liability Insurance

FORM CG-2010
REV 09/2025

Policy number

NBM-PL-4471-88A1

Named insured

Delgado Fabrication LLC2

Effective / expiry

01 Mar 2026 to 28 Feb 2027

Annual premium

$12,480.00 USD3

This certificate is issued as a matter of information only and confers no rights upon the certificate holder. It does not affirmatively or negatively amend, extend or alter the coverage afforded by the policies listed herein.

Should any of the above described policies be cancelled before the stated expiration date, the issuing insurer will endeavour to mail thirty (30) days written notice to the certificate holder named to the left, but failure to do so shall impose no obligation or liability of any kind upon the insurer, its agents or its representatives.

Authorized representative

Date

Coverage afforded by the policies described herein is subject to all the terms, exclusions and conditions of such policies. Limits shown may have been reduced by paid claims.

Extraction5 fields · 1 flagged
1Policy number

NBM-PL-4471-88A

99.4%page 2
2Named insured

Delgado Fabrication LLC

98.1%page 2
3Annual premium

$12,480.00

97.6%page 2
4Effective date

01 Mar 2026

99.0%page 2
5Risk tierReview

B · Standard

86.2%page 5
AI Skills Marketplace · Developer Tools

A catalogue of installable AI skills and prompts, searchable by task

Genesis AI is a marketplace for reusable AI skills and prompt packs. Teams browse by category, install a skill into their assistant, and publish their own. The catalogue is searchable, versioned and curated, so the useful prompts stop living in private notes.

  • Searchable catalogue of skills and prompt packs by category
  • One-click install into the team's assistant
  • Publishing flow with versioning and moderation
app.genesis-ai.dev/skills
Genesis AI skills and prompts catalogue

What we build and deliver

The retrieval engineering that makes RAG reliable.

Ingestion & Parsing

Robust parsing of documents, PDFs, and data into clean, retrievable content.

Chunking & Embedding

Smart chunking and embedding strategies that preserve context and meaning.

Retrieval & Re-ranking

Vector, keyword, and hybrid retrieval tuned to surface the right passages, then re-ranking and filtering to push the most relevant of them to the model.

Grounding & Citations

Answers constrained to retrieved content, with citations users can verify.

Evaluation

Retrieval and answer evaluation that catches accuracy regressions before users do.

Use cases

Where it creates value

RAG wherever accurate answers from your knowledge matter.

Support

Grounded support AI

Assistants that answer from your help content. Every response is accurate to what that content says, and traceable back to it.

Enterprise

Knowledge search

Conversational search across internal documents, wikis, and data.

Legal & Compliance

Document Q&A

Accurate answers pulled from contracts, policies, and regulations. Each one carries its citation.

Sales

Sales enablement

Instant, grounded answers from product and pricing knowledge for reps.

Healthcare

Clinical knowledge

Grounded answers from clinical guidelines and protocols. Every answer carries its sources. Human oversight stays part of the flow.

Product

In-product help

Embedded assistants grounded in your docs that help users self-serve.

Business outcomes

The return on the work

What well-engineered RAG returns.

90%+
Accuracy you can checkGrounded answers that users can trust, with the source on every one so they can verify it themselves.
Current
AutomaticallyKnowledge that stays up to date as content changes, no retraining.
Instant
Knowledge accessAnswers from across your knowledge in seconds, any hour.
FAQ

Questions we get before we start

Still unresolved? A 30-minute conversation with an engineer usually settles it faster than another page of copy.

Retrieval-augmented generation grounds an AI's answers in your real data by retrieving relevant content at query time and basing the response on it. It matters because it makes answers accurate, current, and traceable to a source, solving the hallucination and staleness problems that plague AI relying on training data alone.

Fine-tuning adjusts a model's weights on your data, changing how it behaves. RAG leaves the model alone and feeds it relevant information at query time. RAG is usually the better choice for factual accuracy and currency: it's easier to update, cites sources, and doesn't require retraining when your content changes. Sometimes the two are combined.

Because the answer is only as good as what was retrieved, and retrieval is hard. Bad chunking, weak embeddings, or simple similarity search surface the wrong passages, and the model answers from wrong context. We engineer the full retrieval pipeline: parsing, chunking, indexing, hybrid search, and re-ranking. That's what makes RAG reliable.

Yes, and we build it to. Every answer can be traced to the retrieved passages it came from, so users can verify it. Citations are core to trust, especially in high-stakes domains.

Yes. Because RAG retrieves from your live content, its knowledge updates as your content does: no retraining required to stay current.

Documents, PDFs, wikis, databases, support content, and more. We handle parsing and ingestion of varied, messy real-world content into a clean, retrievable knowledge base.

We build evaluation into the system, measuring both retrieval quality (did it find the right content) and answer quality (is the response correct and grounded), so accuracy is monitored and regressions are caught before users see them.

Adjacent capabilities

See also

Generative AI

Custom generative AI: copilots, content generation, summarization, and reasoning systems on OpenAI, Anthropic, and open models, tuned to your domain and guarded for production.

Conversational AI

Conversational AI and chatbots that actually resolve: grounded in your knowledge, integrated with your systems, and engineered to deflect tickets and convert visitors.

Data Engineering

The data foundation behind great decisions and reliable AI: pipelines, warehouses, real-time streaming, and analytics that turn scattered data into a single source of truth.

Now taking new projects

Ground your AI in truth.

Point us at your knowledge. We'll build a RAG system that answers accurately, cites its sources, and stays current.

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