Give your teams and AI systems information they can use
ETT connects and prepares operational data for reporting, retrieval and automation. Start with a specific decision or workflow, then build the data quality, access and context it needs.
What you would be buying
Approved information made usable for one named decision or workflow, with the quality, access and refresh rules to keep it that way.
What you receive
- Source and pipeline map for the data in scope
- Data-quality baseline: completeness, freshness and known gaps
- Agreed pipelines or retrieval layer
- Dashboard, where the use case calls for one
- Ownership and refresh rules for each source
- Validation results against the agreed use case
What you provide
- Agreed sources and the permission model that governs them
- Named data owners who can approve use
- Stated quality expectations for the decision being supported
- The reporting or retrieval use case the work is for
How success is measured
- Data freshness against the agreed refresh target
- Completeness against the fields the use case needs
- Retrieval relevance, scored on a reviewed sample
- Answer evaluation where the data feeds an AI workflow
- Time spent preparing information, before and after
This service makes approved information useful. Establishing what data exists and how it should be handled is Data Discovery and Classification — a different engagement, usually run first.
What can hold it up
- Source system access and any vendor licensing needed to extract data
- Data owners being available to confirm what a field actually means
- A specific decision or workflow to build towards; open-ended data preparation has no measurable end
After it goes live
Pipelines break when source systems change. Refresh rules, ownership and monitoring are part of the handover so that change has a named owner.
Deliverables and measures describe the standard shape of this engagement. Exact scope, duration and commercial terms are agreed and confirmed in writing before work starts.
How we do the work
AI-ready data infrastructure
We build data pipelines that bring together inputs from CRMs, ERPs, CCaaS platforms, voice interactions, chat transcripts, support tickets, spreadsheets and other operational systems, making data cleaner, more connected and easier for AI to use.
Data strategy and automation
We help organizations understand what data they have, where it sits, how it needs to move and what needs to improve before AI can operate reliably.
Business intelligence and dashboards
We create dashboards and reporting layers that help teams see what is happening across customer interactions, operational workflows and performance trends.
Conversational data mining
We help analyze calls, chats, emails and support interactions to identify recurring issues, intent patterns, process gaps and opportunities for automation.
Predictive modeling for operations
Where appropriate, data can be used to forecast operational pressure, demand changes, service risks, SLA issues or customer behavior patterns.
Grounding repositories and data governance
We help centralize knowledge, FAQs, compliance rules and approved examples into traceable sources that can support AI systems, workflows and governance.
Where better data creates value
More reliable AI agents
AI agents perform more effectively when they can access structured, current and relevant business data.
Why this matters
AI systems are only as useful as the data they can work from.
Many enterprises hold valuable information across CRMs, ERPs, contact center platforms, spreadsheets, ticketing systems, voice recordings, chat transcripts and custom tools. The issue is that these sources often sit apart from each other, with different formats, inconsistent fields, missing context and limited visibility.
When data is fragmented or hard to trust, AI systems struggle. Automation becomes less reliable. Insights become harder to act on. Teams spend time checking, cleaning or interpreting information manually.
ETT helps organizations create the data foundations needed for AI to work properly. We connect, clean, structure and contextualise operational data so it can support automation, analytics, customer insight and better decision-making.
Why AI needs grounded data
AI grounding means giving AI systems access to relevant, reliable and contextual business information.
Without grounding, AI may respond confidently but inaccurately. It may miss important context, rely on outdated information or produce answers that are not aligned with how the business actually works.
For enterprise automation this is a major risk. A voice assistant, AI agent or workflow automation system needs more than a model. It needs access to trusted business data, clear rules, current knowledge and the right operational context.
Generic response, limited context, higher risk of error.
Business-specific data, clearer context, more reliable action.
How we build data foundations for AI
Orient
We assess the current data landscape, including systems, sources, quality issues, workflow dependencies and gaps that may limit AI performance.
Prove
We prove the data foundation against a real AI use case, validating that grounding, quality and pipelines deliver trustworthy outputs.
Govern
We define the data governance, quality controls and grounding approach needed for AI to work from trusted, well-managed information.
Scale
We implement the data architecture, pipelines, dashboards and AI-ready sources required to support live use cases at scale.
Compound
We monitor data quality, refine dashboards, update grounding sources and improve the intelligence layer as business needs change.
Often delivered alongside
Timing, cost, access and ownership
How long does this take?
It is bounded by the use case, and we insist on having one. Data preparation without a specific decision to serve has no completion point and no way to tell whether it worked.
What drives the cost?
The number of sources, the state they are in, and how much reconciliation the data needs before it can be trusted. Where the same field means different things in different systems, agreeing what it means is the work.
What access do you need?
Agreed sources under your own permission model, the data owners who can confirm what a field represents, and the reporting or retrieval use case the work is for.
Who owns the pipelines?
Ownership of the solution, its configuration and the documentation is set out in the engagement contract before work starts. Where a third-party platform forms part of the solution, that platform's own licensing terms apply to it, and we identify which components those are rather than leaving the boundary vague.
What happens next?
A conversation about the specific process or decision you have in mind. If there is a workable opportunity we will describe a scoped first engagement; if there is not, we will say so and explain why. Requesting a session does not commit you to anything.
About this service
What does AI-ready data mean?
Data that is clean, structured, relevant and accessible enough for AI systems to use reliably, with the context, quality and governance needed to support automation and decision-making.
What is AI grounding?
Connecting AI systems to trusted business information so their responses and actions are based on relevant organizational data rather than generic model knowledge alone.
Why does data quality affect AI performance?
Poor data can lead to inaccurate outputs, unreliable automation and weaker decision-making. AI systems need trusted, current and contextual data to perform effectively.
What types of data can support AI automation?
Data from CRMs, ERPs, contact center platforms, ticketing systems, spreadsheets, voice recordings, chat transcripts, documents and internal knowledge bases.
How can analytics support AI adoption?
Analytics can show where processes are slowing down, where customer issues repeat, where demand is changing and where automation may create the most value.
Ready to make your data work harder for AI?
Request an AI session to explore whether your data is ready for AI automation, where stronger insight could support better decisions, and what foundations need to be in place before scaling.
Around four minutes. Indicative guidance based on your answers.