aisewa.ai

Data infrastructure

Get your company data into a shape AI can use

We pull data out of your CRM, ERP, shared drives and spreadsheets into one governed store that models and agents can query, with the original access rules intact.

It is usually the slowest part of an AI project, and the part that decides whether the rest works.

Talk about your dataHow we handle security

The starting point

Most of it is already written down, in too many places

Customer historyCRM software
Invoices, costs and stockERP systems
How the work actually gets doneDocuments, spreadsheets, internal apps

What we connect

Systems we pull data from

CRM
ERP and accounting
SQL and NoSQL databases
Shared drives and wikis
Cloud storage
Websites
Mobile apps
Analytics tools
Helpdesk
Email
Third-party APIs
Internal apps
Spreadsheets

What we build

One store with access rules that AI can query

We consolidate databases, CRM and ERP platforms, websites, documents, APIs, analytics tools and internal apps, and keep the copy in sync as the source systems change.

01A central data store
02Pipelines that keep it in sync
03Document and file ingestion
04Vector search
05Metadata and classification
06Cleaning and transformation
07Access control
08Real-time and scheduled sync
09Feeds for BI dashboards
10Retrieval APIs for models and agents

Where it sits

The data layer in the AI Sewa stack

Retrieval, models, agents and automated workflows all read from this layer, so a mistake here shows up in everything above it.

  1. L01

    Your applications

    CRM, ERP, HRMS, website, databases, APIs

  2. L02

    Integration

    Connectors, credentials, events, rate limits

  3. L03

    Data and knowledge

    Pipelines, vector search, metadata, access rules

  4. L04

    Models

    Hosted or private LLMs, fine-tunes, retrieval, evaluation

  5. L05

    Agents

    Narrow roles, tool permissions, hand-offs

  6. L06

    Workflows

    Steps, approvals, retries, audit log

  7. L07

    People

    Staff, customers and managers, in the tools they already use

Security

Access control goes in at the start

If someone can't see a record in the source system, the AI shouldn't show it to them either. We design permissions, logging and data governance alongside the pipelines.

Role-based access
Encryption in transit and at rest
Private model hosting
Isolated environments
SSO and scoped API keys
Activity logs
API rate limits and validation
Approval checkpoints
Per-model access policies
Data retention rules
Audit trails
Separate dev, staging and production

The right setup depends on your security, regulatory and operational requirements. Running everything inside your own environment is possible, and it costs more than a managed cloud setup.

Talk about secure deployment

Data foundation

Start with the data.

Connect sourcesClean and structureSet access rulesMake it searchable
Talk about your dataExplore AI models