Devectra / AI Systems

Intelligence built into how your organization actually runs.

We connect AI to your organization's real data, workflows and knowledge — rather than bolting a generic chatbot onto a website. Retrieval-augmented generation, knowledge systems, and AI embedded directly into the tools your team already uses.

The difference

Not a chatbot widget. A system grounded in what you actually know.

Generic chatbot

Devectra RAG

Answers from general internet knowledge

Answers grounded in your actual documents and data

No connection to your real records

Connected to controlled organizational knowledge

Same experience for every visitor

Scoped to what your organization actually needs

Bolted onto a website as a widget

Built into the workflow it's meant to support

How it works

Your organization's data, made retrievable and reasoned over.

Documents, databases and policies flow through a retrieval layer before the model ever sees them — so answers stay grounded in what's actually true for your organization.

Organization Data

Documents

Database

Policies

Knowledge

Retrieval Layer

Intelligence

Search

Answers

Automation

Applications

Where this actually gets used.

Not a chatbot bolted onto a homepage — AI grounded in the documents, data and workflows your organization already runs on.

01

Internal knowledge search

Ask questions across your documents, policies and records instead of searching through folders and shared drives.

02

Document Q&A and support

Answer staff, customer or student questions grounded in your actual materials — not generic web knowledge.

03

Workflow automation

AI embedded directly into an existing operational process, triggered by real events, not a separate tool bolted on the side.

04

AI-augmented dashboards

Natural-language queries over your operational data, layered onto the dashboards your team already checks daily.

05

Institutional knowledge systems

Search and reason across policies, curricula, records and correspondence — built for organizations that run on documentation.

06

Custom AI applications

AI as a core part of the product you're building, engineered in from the start rather than added as an afterthought.

Capabilities

Built on real engineering, not a plugin.

AI services connect directly into the application and data layers of a real system — the same infrastructure that runs everything else we build.

  • Retrieval-augmented generation over your documents and data
  • Knowledge systems that search and reason across information
  • AI integrated directly into existing workflows
  • Vector retrieval and controlled information access
  • Applications with AI as a core capability, not an add-on

User

Interface

Application

API / Services

Data

Cloud Infrastructure

AI Services

AI services connect into the application & data layers

How we scope it

From data review to a working system.

01

Data & access review

We map what data actually exists, where it lives, and who should be able to reach it.

02

Pilot

A scoped, working pilot against real organizational data — not a generic demo.

03

Integration

Built into the workflow or application your team already uses, not a separate tool to check.

04

Evaluation & iteration

Ongoing evaluation against real queries from real use, with room to improve after launch.

Pricing

AI Integration.

RAG and intelligent system development.

  • Retrieval-augmented generation over your data
  • Vector store and controlled knowledge access
  • Integration into an existing workflow or application
  • Evaluation against real organizational queries
  • Documentation for internal handoff

$9,000 +

₹7,45,000+

Request proposal →

Indicative starting price. Final scope confirmed after a discovery call.

Common questions

Before you reach out.

Retrieval-augmented generation: the AI looks up relevant information from your actual documents and data before answering, instead of relying only on what a general model already 'knows'.

No — retrieval systems we build keep your data in your own controlled store; it isn't sent off to train third-party models.

Yes. A generic chatbot answers from general knowledge. What we build is grounded in your organization's actual documents and workflows, which is a different (and more useful) engineering problem.

A scoped pilot typically takes a few weeks once we understand your data and access requirements. Full integration timelines depend on how many systems it needs to connect to.

Access to the documents, data or systems the AI needs to reason over, and a clear idea of the questions or workflow it should support. We help narrow this down during discovery if it isn't clear yet.

Have organizational data that should be doing more for you?

Start a project ↗