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> RAG & Enterprise Search Development

RAG & Enterprise Search Development

Turn internal documents and systems into a secure, reliable AI search experience - with grounded answers and citations. 

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Grounded answers with citations (reduce hallucinations) 

Connectors to docs, ticketing, and CRM systems 

Role-based access control (RBAC) and audit logs 

Evaluation and guardrails for safety and quality 

Cloud, hybrid, or fully on-prem deployment 

What RAG is - and why enterprises use it

Retrieval-Augmented Generation (RAG) combines two things: trusted enterprise knowledge and a modern LLM interface. Instead of “guessing,” the system retrieves the most relevant internal documents (policies, tickets, specs, contracts, wiki pages) and uses them as evidence to generate an answer. The result is faster, more consistent decision-making without sacrificing control. 

Enterprises choose RAG because it reduces risk and improves reliability. Answers are grounded in your approved sources and can include citations and links back to the original content, making responses verifiable. Access control can be enforced end-to-end (RBAC/ABAC), so users only see what they are permitted to see. And because the pipeline is measurable, you can track quality with clear metrics - retrieval precision/recall, citation coverage, answer accuracy, latency, and cost per query - then iterate based on real data. In practice, RAG is the most pragmatic path to “enterprise GPT”: useful, auditable, and ready for production. 

Use cases

Internal Knowledge Assistant

Employees ask: “How do I handle X?”, “What’s the process for Y?”, “Where is the latest spec?”

RAG pulls from Confluence/ SharePoint/ Drive/ wiki and answers with citations, respecting Role Base Authentication, reducing search time and speeding onboarding.

Compliance & Legal (Policies, Clauses, Audit Evidence Retrieval)

Teams ask: “Where is the evidence for this control?”, “What does clause Y say?”, “Do we allow X under our policy?” 

RAG retrieves the relevant policy sections, contract clauses, and audit artifacts and returns verifiable, cited answers—reducing audit preparation time and improving retrieval precision (finding the right evidence quickly, with fewer false matches).

HR & People Ops (Policies, Benefits, Onboarding, Role Guides)

Employees ask: “How do I request PTO?”, “What’s our remote policy?”, “Where is the onboarding checklist?” 

RAG retrieves the relevant HR docs and forms and responds with policy-backed answers and direct links—cutting HR ticket volume and speeding up onboarding time.

Data sources and integrations

Docs

  • Confluence
  • SharePoint
  • Google Drive
  • DropBox
  • Notion
  • File shares

Ticketing

  • Jira
  • Zendesk
  • Freshdesk
  • ServiceNow

Communication

  • Slack
  • Microsoft Teams
  • Email (with explicit permissions)

Dev tools

  • Git repositories
  • CI/CD docs
  • Wikis

Databases

  • SQL
  • Data warehouse
  • Internal APIs

how it works

STEP 1

Data Indexing (prepare knowledge) 

  • You start with your Documents (PDFs, wiki pages, manuals, policies, tickets, etc.).
  • They are stored in a Vector DB (a searchable knowledge store that helps find the most relevant parts of your documents by meaning, not just keywords).

STEP 2

Data Retrieval & Generation (answer a question) 

  • A User Query (question) is sent to the Vector DB.
  • The Vector DB finds the Top-K Chunks — the few most relevant snippets from your documents.
  • These snippets are passed to the LLM, which uses them as evidence to write a Response.

In short: RAG answers questions by first retrieving the best matching document snippets, then having the AI generate a response grounded in those snippets, instead of relying only on its general knowledge. 

Security and privacy

RBAC/ABAC support and document-level ACL enforcement

Audit logs for user queries and system actions

PII/PHI handling (masking, redaction, retention policies)

Prompt injection and data exfiltration mitigations

Private deployment options: on-prem / VPC / dedicated tenants

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delivery process

01

Discovery (1-2 weeks)

  • Scope
  • Sources
  • KPIs
  • Risk review

02

PoC (2-6 weeks)

  • Connect 1-2 sources
  • Implement retrieval + citations
  • Baseline evaluation. 

03

Pilot

  • Limited rollout
  • Feedback
  • Access controls
  • Performance tuning

04

Production

  • Monitoring
  • Governance
  • Incident playbooks
  • Continuous improvement

faq

How do you reduce hallucinations in RAG?
How do you enforce document-level access control?
Can we deploy RAG on-prem or in our VPC?
What data sources can you connect to?
How do you measure answer quality and citation accuracy?
How do you protect against prompt injection?
What latency and cost should we expect?
How long does it take to deliver a PoC?
Do we need perfectly clean data to start?
How do you keep the index updated with new documents?
Let’s work together
"
We partner with ambitious teams to solve complex challenges and create meaningful impact. From early ideas to full-scale delivery — we’re here to support every step.

Tell us what you’re working on, and we’ll help you define the best way forward.

Anna Tukhtarova |

CTO & Co-Founder

What's next?
01 Submit the request—takes <1 minute.
02 Receive confirmation (and optional NDA) within 12 hours.
03 Meet our solution architect to discuss goals & success metrics.
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