RAG & Enterprise Search Development
Turn internal documents and systems into a secure, reliable AI search experience - with grounded answers and citations.
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 trusted enterprise knowledge with a modern LLM interface. Instead of “guessing,” it retrieves the most relevant internal content - policies, tickets, specs, contracts, and wiki pages - and uses it as evidence to answer questions. The outcome is faster, more consistent decisions without losing control (hallucinations) over what the AI is allowed to say.
Enterprises choose RAG because it reduces risk and increases reliability. Responses are grounded in approved sources and can include citations and direct links back to the original documents, making answers verifiable and easy to audit. Access control can be enforced end-to-end (RBAC/ABAC), so every user only sees content they are authorized to view.
RAG is also measurable and improvable over time. You can track retrieval precision/recall, citation coverage, answer accuracy, latency, and cost per query, then iterate based on real usage data. In practice, this makes RAG the most pragmatic path to “enterprise GPT”: useful, governed, and production-ready.
Use cases
Real-world workflows where RAG delivers faster answers, fewer errors, and measurable time savings.
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
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
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.
And many more workflows where trustworthy answers are critical.
RAG becomes a single reliable access point to organizational knowledge, reducing manual search, avoiding guesswork, and enabling teams to make faster, confident decisions.
Data sources and integrations
Data sources and integrations means we connect the AI to the tools where your information already lives - docs, tickets, chats, databases, and internal systems. This lets the assistant find the right content across all sources and answer with links to the originals, while keeping the same access permissions and security rules.
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 in two main steps:
1. First, it converts the user’s query into an embedding and searches a vector database to retrieve the most relevant document snippets.
2. Then, the AI uses these retrieved snippets as context to generate a response.
This grounding helps the model produce more accurate and up-to-date answers instead of relying only on its general training data.
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
Model monitoring and response quality guardrails
How delivery process with us looks like
We start by listening - mapping your workflow, data sources, and success metrics in a short discovery. Then we build a focused PoC to prove value fast, using your real documents and real permissions. Once it works, we harden it for production: security controls, monitoring, and measurable quality. Finally, we keep improving it with feedback and updates - so the solution stays accurate as your business and knowledge base evolve.
Discovery (1-2 weeks)
We start by aligning on the real business problem rather than jumping straight into implementation. During discovery, we define scope, identify the data sources the system will rely on, and agree on measurable success criteria. We also review potential risks early to avoid architectural dead-ends later.
PoC (2-6 weeks)
Next, we validate feasibility on real data. We connect one or two priority sources, implement retrieval with citations, and measure baseline response quality. The goal of the PoC is not polish, but evidence — proving the system produces reliable and explainable answers.
Pilot
After validation, the solution is introduced to a limited group of users. We collect feedback, apply access controls, and tune performance based on real usage patterns. This stage ensures the system works not only technically, but operationally inside the organization.
Production
Finally, the system becomes a governed internal capability. We establish monitoring, operational playbooks, and clear ownership rules while continuously improving quality and reliability. At this stage, the AI is treated as infrastructure — observable, maintainable, and accountable.
Need expert guidance to make AI answer from your data — accurately and securely?
From AI prototype to business capability
Most teams can launch a demo. Very few make it dependable enough for daily decisions.
Our job is to turn AI search into something employees trust — not experiment with.
We design the system around your real workflows, data ownership, and risk tolerance. That means structuring knowledge, defining answer boundaries, and ensuring every response is grounded in approved sources instead of model assumptions.
After launch, the work doesn’t stop. We continuously monitor usage, improve answer quality, and evolve the system as your documentation changes — so the AI remains accurate months later, not only on day one.
faq
Let's discuss how we can help bring your ideas to life!
Got an idea but no one to implement it fast? Contact us and we'll get back to you within 24 hours.
