Skip to content
Neuralex Labs

AI engineering service

Make scattered company knowledge answerable — with sources.

We build retrieval-augmented generation systems that search your documents, wikis, tickets, and databases, then answer questions with citations — respecting who is allowed to see what.

Build a knowledge systemSee RAG & Knowledge Systems in practice

The problem

The answer exists somewhere. Finding it takes too long.

Policies sit in shared drives, procedures in wikis, decisions in email threads, and history in tickets. People ask colleagues instead of searching, and new hires take months to become self-sufficient.

What we typically hear

  • Knowledge is spread across many tools with weak search.
  • Experienced staff are interrupted with repeat questions.
  • Outdated documents are found and trusted by mistake.
  • AI tools give confident answers with no sources.
  • Access rules make a single shared index risky.

What we build

What we build

Ingestion pipelines

Connectors that pull content from drives, wikis, help desks, and databases, and keep it in sync.

Chunking and indexing

Content split and embedded in ways that preserve meaning, headings, and tables.

Hybrid retrieval

Keyword and semantic search combined with re-ranking for relevant results.

Cited answers

Responses that link to the exact passages they rely on.

Access control

Document permissions carried into the index so users only retrieve what they can see.

Quality evaluation

Question sets that measure retrieval accuracy and answer faithfulness.

Typical use cases

Where this work pays off

Internal knowledge assistant

01

Employees ask questions about policies and procedures and get cited answers.

Support knowledge base

02

Agents and customers find the right article, past resolution, or product detail.

Document research

03

Analysts query large collections of contracts, reports, or filings.

Agent memory

04

AI employees retrieve the policies and history they need before acting.

Architecture

How a RAG answer is produced

  1. 1

    Question

    A user or agent asks something.

  2. 2

    Permissions

    Filter the index to what the asker may see.

  3. 3

    Retrieve

    Find and re-rank the most relevant passages.

  4. 4

    Answer

    Generate a response grounded in those passages.

  5. 5Human

    Cite

    Show sources so the reader can verify.

Retrieval quality determines answer quality, so most engineering effort goes here.

Technology considerations

Decisions we make with you

Technology choices follow from the workflow, data, and constraints — not the other way around.

Vector stores

pgvector, managed vector databases, or search engines — selected by scale, cost, and existing infrastructure.

Freshness

Incremental sync so updated or deleted documents are reflected quickly.

RAG vs fine-tuning

RAG suits knowledge that changes; fine-tuning suits consistent style or format. Many systems need only RAG.

Structured data

Questions about numbers are answered by querying databases directly rather than searching text.

Delivery

How the engagement runs

  1. 01

    Source audit

    Identify content sources, owners, quality, and permissions.

  2. 02

    Question set

    Collect real questions people ask to define success.

  3. 03

    Build pipeline

    Ingest, index, and tune retrieval.

  4. 04

    Evaluate

    Measure retrieval and answer quality against the question set.

  5. 05

    Launch and maintain

    Roll out with feedback capture and content hygiene routines.

Security & governance

Control is designed in, not added later.

Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.

  • Source-level permissions enforced at retrieval time
  • Citations on every answer
  • Exclusion rules for sensitive repositories
  • Retention and deletion propagated to the index
  • Logging of questions for quality review, with privacy controls
Read our security & governance approach

FAQ

Frequently asked questions

01What is RAG in simple terms?

Retrieval-augmented generation means the AI first searches your own content for relevant passages and then writes an answer based on them, rather than relying only on what the model learned in training.

02Can it handle documents with different access levels?

Yes. We carry permissions from the source systems into the index and filter results per user, so people only get answers from documents they are allowed to read.

03How accurate are the answers?

Accuracy depends on content quality and retrieval tuning. We measure it against a set of real questions before launch and show citations so users can verify any answer.

Related reading

Go deeper on this topic

Related services

Often delivered together

AI

AI Copilots

Neuralex Labs builds AI copilots for internal teams and SaaS products: context-aware assistants grounded in your data, with permissions and citations built in.

AI

AI Employee Systems

Neuralex Labs builds AI employees: role-based, human-supervised AI workers connected to your CRM, inbox, and business systems. Scope your first AI employee.

AI

Generative AI Development

Generative AI development from Neuralex Labs: LLM features and pipelines with structured outputs, evaluations, guardrails, and predictable running costs.

Next step

Bring us the process your team is tired of doing manually.

A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.

Build a knowledge systemSee how we work

What happens next

  1. 1Tell us about one workflow
  2. 2We review it before replying
  3. 3A working session, then a first-build proposal