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AI + Data

Enterprise Knowledge AIA Single Brain for the Company.

Enterprise Knowledge AI for the company's collective documents — the ingestion across the data sources, the per-user access control, the retrieval, the generation, the eval suite and the deployment on the infrastructure the security review requires, owned by the company.

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10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served

Overview

Enterprise Knowledge AI is RAG at company scale

Enterprise Knowledge AI is RAG at company scale: the documents come from every system the company runs (Drive, SharePoint, Confluence, the wiki, the helpdesk, the CRM), the access control is per-user, the retrieval is hybrid, the eval suite is held-out, and the deployment is on the infrastructure the security review requires.

We build Enterprise Knowledge AI with the ingestion, the access control, the retrieval, the generation, the eval suite and the deployment as part of the architecture from sprint one. The output is a knowledge AI the company owns, deployed on the infrastructure the security review requires, with the eval that proves it answers correctly.

This is the wrong engagement if the data is genuinely siloed (one team only) or if the security review is not part of the plan. We will say so on the call.

  • A Single Brain — One AI that knows the company's documents, with the per-user access control the security review needs.
  • Cited Answers — Every answer cites the source document, with the link the user can verify.
  • On Your Infra — On your VPC, on your hardware, with the deployment model the security review requires.
  • Audit-Logged — Every query, every answer, every citation logged for the audit the security review needs.

What we deliver

Everything included in our enterprise knowledge ai

Multi-Source Ingestion

Drive, SharePoint, Confluence, the wiki, the helpdesk, the CRM — every system the company runs.

Per-User Access Control

Per-user access control, with the documents filtered to the user the answer is for.

Hybrid Retrieval

Vector + keyword + metadata, with the reranking the accuracy needs.

Generation & Citations

The generation, the citation, the link to the source document the user can verify.

Evaluation & Guardrails

A held-out test set, the safety filters, the audit log the security review needs.

Deployment on Your Infra

On your VPC, on your hardware, with the deployment model the security review requires.

Our process

A proven process for successful delivery

  1. 01

    Discover

    We audit the data sources, the access control, the questions and the security posture.

  2. 02

    Plan & Design

    We design the ingestion, the access control, the retrieval and the eval suite.

  3. 03

    Develop

    We build in two-week sprints with the team testing as we go.

  4. 04

    Deploy

    We ship to production on your infrastructure, with the eval and the audit log live.

  5. 05

    Optimize & Grow

    We read the accuracy, the eval and the team feedback, and ship the next iteration.

Technology

Built with a stack that stays maintainable

Vector Stores

  • Pinecone
  • Weaviate
  • Qdrant
  • pgvector

Data Sources

  • Google Drive
  • SharePoint
  • Confluence
  • Notion
  • Slack

Models

  • OpenAI
  • Anthropic
  • Google Gemini
  • On-prem open source

Deployment

  • AWS VPC
  • Azure VNet
  • GCP VPC
  • On-premise

What you can expect

Citation Accuracy Target
90%+Citation Accuracy Target
Ingestion
Multi-SourceIngestion
Access Control
Per-UserAccess Control
Infrastructure
On YourInfrastructure

Industries we serve

Delivered for teams in your sector

View All Industries
HealthcarePatient platforms, clinic software and compliant AI.Finance & FinTechLending, payments, KYC and risk automation.ManufacturingERP, quality vision and predictive maintenance.Professional ServicesPractice management and document intelligence.

FAQs

Questions we get asked

Something not covered here? Ask us directly.

RAG is the technique. Enterprise Knowledge AI is the product. The product is the multi-source ingestion, the per-user access control, the eval suite and the deployment on the infrastructure the security review requires. The technique is one component of the product.

We integrate with the systems the company actually runs — Drive, SharePoint, Confluence, Notion, the wiki, the helpdesk, the CRM. Each integration is a typed connector with the auth, the rate limits and the monitoring the connector needs. The ingestion is incremental, not a one-off export.

Per-user access control is enforced at the retrieval layer, not the prompt layer. The documents are filtered to the user the answer is for, the retrieval only sees the documents the user can see, and the generation only cites the documents the user is allowed to read. The access control is part of the architecture, not a wrapper.

Yes. The vector store, the embeddings and the model can all run on your infrastructure — your VPC, your hardware, your data centre. The deployment model is part of the discovery, and the security review is part of the engagement. The system is yours, on your infrastructure, with the eval that proves it.

Related services

More in AI + Data

RAG DevelopmentAI Search & Semantic SearchPredictive AnalyticsRecommendation EnginesComputer VisionDocument Intelligence & OCR

Business software

ERP, CRM, HRMS & custom software we build

ERP DevelopmentCRM DevelopmentHRMS DevelopmentCustom Software DevelopmentDesktop App Development

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