Data Readiness and Architecture

We get your data ready for AI: inventoried, cleaned, modeled, access-controlled and fed by reliable pipelines.

Request a project review

Describe the goal, current systems and constraints. We will review the scope and identify the next evaluation step.

WE MULTIPLY WHAT'S IMPORTANT

Your Metrics

Your Capacity

Your Revenue

Your Time

Your Performance

Any Model

On Your Schedule

Your Hardware or Ours

You Own the Code

Built to Be Measured

How results are measured

Business case

Value definition

Expected value, implementation cost, and the measurement method are agreed before development begins.

Delivery plan

Delivery approach

Milestones are set around data readiness, integrations, security review, and the production environment.

Operating baseline

Current cost, cycle time, quality, capacity, or risk is established before improvement is measured.

Verified outcome

Results are reported against approved source data, a defined system boundary, and an agreed measurement period.

ALLTIPLY prepares the data that AI systems depend on. We inventory your sources, measure quality, model business entities and definitions in a semantic layer, set access rules, and build the pipelines that keep data current.

The work is scoped to the AI use cases you plan to run, so effort goes where a model or agent will actually use the data. For examples, see the operations-platform observations in the production-gap field report, where the platform is built and live data connections are planned, and the conversation-intelligence architecture report, where prototype capabilities and synthetic-data demonstrations are labeled separately from specified work.

Challenges That Hold You Back

Nobody has a full list of where key data lives, who owns it or how current it is, so every AI project starts with the same discovery.

The same customer, product or metric is defined differently in each system, and models inherit the confusion.

Sensitive data has no clear access rules, so security blocks AI projects or teams copy data into places it should not go.

Measurable Outcomes That Drive Real Results

A clear inventory of your data

Sources, owners, freshness, quality issues and sensitivity are documented for the data each AI use case needs, so gaps are known before the build starts.

Shared definitions in one layer

Customers, products, work and metrics are defined once in a semantic layer and linked across systems, so models, agents and dashboards read the same meaning.

Governed, reliable pipelines

Pipelines deliver current data with quality checks and alerts, and role-based access rules follow the data into every AI system that uses it.

Read the implementation evidence

These firsthand reports distinguish production use from proof environments and prototypes.

Production voice platform: operating choices and handoff. A field report from a platform operating in production.
Enterprise retrieval: intent-aware proof. A proof environment; production outcomes are not claimed.
Conversation intelligence: aggregation architecture. A prototype demonstrated with synthetic data.

Explore our work and evidence status or read the research library directly.

Assess your data readiness

Describe the goal, current systems and constraints. We will review the scope and identify the next evaluation step.

Request a project review

Steps to Getting Started

Four phases from a data inventory to governed pipelines feeding AI systems.

Inventory and assess

Baseline set

We map sources, owners, freshness, quality and sensitivity for the data your planned AI use cases need, and rank the gaps that block them.

Model and define

Model approved

We design the data model and semantic layer, agree business definitions with data owners and set access rules for sensitive fields.

Build pipelines

Pipelines running

We build and test ingestion and modeling pipelines with quality checks, lineage and alerts, on your existing data platform where possible.

Hand over and govern

In production

Data flows to AI systems with monitoring, documentation and clear ownership, so your team can maintain and extend it.

Data readiness FAQ

Tell us about your project. Share the goal, current systems and constraints so we can review the scope and identify the next evaluation step.

Request a project review

Do we need perfect data before starting AI work?

No. You need the right data for the use case, at a known quality level. We assess what each planned use case needs, fix what blocks it, and document the remaining gaps rather than waiting for a full cleanup.

What is a semantic layer?

A shared set of business definitions, such as customer, order, active account or margin, mapped to the underlying tables. Models, agents and dashboards use the same meaning without each one rebuilding the logic.

How do you handle sensitive data?

We classify sensitive fields, define role-based access rules, and apply masking or exclusion where needed, so AI systems only see data the user or process is allowed to see. Your security team approves the rules.

Do we need a new data platform?

Usually not. We work with your existing warehouse, lake or databases and add structure, pipelines and controls on top. If the current platform cannot support the use cases, we show the tradeoffs before any change.

How does this connect to the AI build itself?

Directly. Data readiness is scoped to named use cases and hands over clean sources, definitions and pipelines that the model, assistant or automation build uses from the start.