# ETT (Emerging Transformational Technology)

> ETT builds AI and automation into the work your teams already do, with
> clear ownership, human oversight and a record of every decision the
> system takes. Headquartered in Dallas, Texas, United States, with
> offices in London, United Kingdom and Vancouver, British Columbia, Canada.

This file is a plain-text map of ettgroup.ai. It is generated from the same
data the site renders, so it cannot drift from the pages it describes.

## What ETT is

ETT (Emerging Transformational Technology) is a services firm that designs,
builds and runs AI and automation inside client organisations, mostly in
regulated industries. Work is delivered by ETT's own people against a
baseline agreed before anything is built.

ETT is not a licence reseller. Where a platform is the right answer it is
implemented; where a simpler or cheaper approach would serve the client
better, that is what is said. Analect is ETT's own product, covered in its
own section below.

## Homepage

[ettgroup.ai](https://ettgroup.ai/) makes one claim and then shows it happening: "AI that does the work, and shows its working."

Below that, the page asks which of three situations the reader is in and
answers in that one. All three answers are in the page source whichever is on
screen; the links below each open the page on one of them.

- [My board wants AI, my regulator wants proof](https://ettgroup.ai/#board): reporting the saving to the board and the evidence to the auditor from the same project.
- [Whatever we build, my name is on it](https://ettgroup.ai/#name): what the system may do alone, what needs approval, who gives it, and where the record of each decision lives.
- [We have run the pilots and nothing has shipped](https://ettgroup.ai/#pilots): taking one process live and measuring it, instead of running another proof of concept.

## How an engagement starts

Three ways in, smallest first. Each is sized so that declining the next one
costs nothing.

- 4 minutes, [See where you stand](https://ettgroup.ai/automation-assessment): Answer a few questions about your processes and data. You get indicative guidance straight away.
- One conversation, [Talk it through with us](https://ettgroup.ai/executive-ai-acceleration-session): Bring the process that’s bothering you. We’ll tell you whether it’s worth automating and what it would take. No deck.
- Fixed price, Business Process Workshop: We map the process with your team, size the saving and set the baseline. If the numbers don’t stack up, we’ll tell you to stop.

ETT does not publish day rates or fixed prices for delivery work. Scope,
duration and commercial terms are agreed per engagement. Anyone quoting a
price for ETT work that is not in this file is not quoting ETT.

## Common questions, answered

These are the questions ETT puts on its own homepage and answers there. The
wording below is the wording on the page.

### What if it goes wrong on my watch?

The system only acts inside limits you set. Anything outside them goes to a named person with the full history attached. Every step is recorded, so if something does go wrong you can see what happened and fix the rule.

### We’ve been burned before. Why is this different?

We agree the baseline and the measure before anything is built, and the proof stage ends with a recommendation to scale or stop. If it isn’t going to work, you find out early and cheaply.

### What does this mean for my team?

The work we automate is the work your people complain about: rekeying, chasing, checking. They keep the decisions. We train them to run what we build, so you aren’t dependent on us.

### Who are you?

We have offices in Dallas, London and Vancouver. You deal with the people who do the work.

## How to read the figures on this site

These rules apply to everything below, and to every page they link to.

- Outcome figures published under Case studies were measured with the client, not projected. Each is stated in the unit it was measured in.
- Clients are described by sector rather than named. The engagements are real, scoped, won and delivered. The anonymity is the client's, not a sign the work is illustrative.
- Where an engagement is still in delivery, the page states what it is designed to deliver and publishes no result.
- Where a result is modelled rather than measured, it is labelled as modelled.
- Claims about Analect are limited to the list under "Analect: the claims we publish". Anything beyond that list is not an ETT claim.
- Example workflows on service and industry pages are proposed use cases, not client results.

## Services

- [AI Strategy and Delivery](https://ettgroup.ai/services/ai-strategy-and-delivery): Opportunity assessment, use case prioritisation, roadmap planning and AI delivery leadership.
- [Agentic AI and Automation](https://ettgroup.ai/services/agentic-ai-and-automation): AI agents, workflow automation and orchestration designed around real enterprise processes.
- [Conversational AI and Voice Automation](https://ettgroup.ai/services/conversational-ai-and-voice-automation): Voice agents and conversational AI that connect customer interactions to insight, workflows and action.
- [Data and Analytics for AI](https://ettgroup.ai/services/data-and-analytics-for-ai): Data pipelines, AI grounding, dashboards and analytics that help AI work from trusted business information.
- [Data Discovery and Classification for AI](https://ettgroup.ai/services/data-discovery-and-classification-for-ai): Discovery, classification and structuring of enterprise data so it can be used safely in AI workflows.
- [vCISO for AI](https://ettgroup.ai/services/vciso-for-ai): AI security, governance, compliance and risk leadership for organisations deploying AI at scale.
- [AI Infrastructure as a Service](https://ettgroup.ai/services/ai-infrastructure-as-a-service): Dedicated GPU hosting, AI infrastructure and MLOps support for building and running AI workloads.

## Industries

- [Financial Services](https://ettgroup.ai/industries/financial-services): Automation for reporting, compliance workflows, finance operations, audit support and AI agents for finance teams.
- [Retail and eCommerce](https://ettgroup.ai/industries/retail-and-ecommerce): Customer service automation, order visibility, retail workflow automation and conversational intelligence.
- [Manufacturing and Supply Chain](https://ettgroup.ai/industries/manufacturing-and-supply-chain): Process automation, supply chain visibility, document handling, scheduling support and workflow improvement.
- [Healthcare](https://ettgroup.ai/industries/healthcare): Appointment handling, referral workflows, patient admin, communication support and governed automation.
- [Logistics and Delivery](https://ettgroup.ai/industries/logistics-and-delivery): Voice automation, WISMO handling, multilingual support, delivery updates and operational query management.
- [Utilities, Oil & Gas](https://ettgroup.ai/industries/utilities-oil-and-gas): Field and asset workflows, outage and billing queries, compliance reporting and governed automation for critical operations.

## Case studies

Delivered client engagements, published anonymously. Vendor and platform
names are real; client names are withheld.

Published 2026-09-11. Figures are as measured at the end of each
engagement and are not restated afterwards.

### Turning 220 siloed automations into a reusable framework

https://ettgroup.ai/our-work/modular-automation-framework-global-logistics

Sector: Logistics and supply chain. Region: UK and Europe. Duration: Phased programme, 4 to 6 week discovery then 12 to 18 months.

Consultancy engagement that moved a 220-automation estate from one-off builds to a governed library of reusable modules, cutting delivery time by 60 to 70 percent and ending duplicated support.

Measured results:

- Delivery time for new automations reduced by 60 to 70 percent against the previous 30-day average
- Support aligned to modules rather than individual bots: fixes made once and applied across every automation that uses the module
- Reusable module catalogue established and governed, with ownership and version control in place
- Centre of Excellence stood up and the 30-person in-house team trained to run it
- Agentic adoption roadmap agreed, with the estate prepared for self-healing modules and decision-support agents

Technology: UiPath, RPA, OCR, Microsoft Power Platform, SAP, Oracle, BlueYonder

### An AI assistant for social workers, kept entirely inside the platform

https://ettgroup.ai/our-work/ai-case-file-assistant-social-care

Sector: Social care and public sector technology. Region: UK. Duration: 3 month proof of concept, then phased roadmap.

A regulation-aware AI overlay that lets social workers question a case file in plain language, surfaces urgent risks, and never sends data outside the client's environment.

Measured results:

- AI assistant delivered inside the case file view within the 3-month proof of concept and piloted with practising social workers
- Contextual question and answer, regulatory checks and urgent risk surfacing in production use on live case files
- All processing on-platform: no case data leaves the client's environment, and every interaction is logged for audit
- Data quality prompts flagging missing risk assessments and inconsistent records at the point of entry
- Roadmap for proactive analytics, foster carer and young person app extension, and cross-case pattern detection agreed for subsequent phases

Technology: Private cloud LLM deployment, Fine-tuned language model, Retrieval on regulatory corpus, SSO and MFA integration

### Evidence before automation for a financial services operator

https://ettgroup.ai/our-work/process-discovery-automation-foundation-financial-services

Sector: Financial services. Region: Canada. Duration: 12 week discovery programme.

Discovery-led engagement mapping how priority processes actually run across systems and people, standing up UiPath Automation Cloud, and producing a ranked roadmap to act on it.

Still in delivery. No results are published. Designed to deliver:

- An objective, evidence-based view of how priority processes really run
- Quantified manual effort, error and rework rates as a baseline for investment decisions
- Undocumented and missing processes surfaced, reducing key-person dependency
- Automation opportunities ranked by return, led by commission and rewards processing
- A UiPath platform foundation reused by every subsequent automation, not a point solution

Technology: UiPath Process Mining, UiPath Task Mining, UiPath Task Capture, UiPath Automation Cloud

### Email-to-case-system automation as a group-wide foundation

https://ettgroup.ai/our-work/intelligent-document-processing-medico-legal-group

Sector: Healthcare and legal services. Region: UK. Duration: 4 to 6 week proof of concept, 13 week production build.

Automating the intake, extraction, validation and submission of instruction letters and medical reports into Proclaim, saving over £120k a year and built as the first module of a group-wide automation platform.

Measured results:

- Over £120,000 in annual savings from the removal of 5 to 8 FTE equivalents of manual re-keying
- Around 100 submissions a day processed automatically through UiPath IXP into Proclaim
- Cases initiated in hours rather than days, with faster client turnaround and revenue recognition
- £20,000 to £30,000 a year saved in rework and compliance cost through fewer data entry errors
- Full audit trail on every submission, with exceptions handled through UiPath Action Centre
- Payback inside 12 to 18 months, and a reusable platform for follow-on group use cases

Technology: UiPath IXP, UiPath Document Understanding, UiPath AI Centre, UiPath Action Centre, UiPath Orchestrator, UiPath Automation Cloud, UiPath Automation Ops, Proclaim, Blue Prism

### Taking the year-end squeeze out of finance audit and reconciliation

https://ettgroup.ai/our-work/finance-audit-reconciliation-automation-investment-management

Sector: Financial services. Region: UK. Duration: 8 weeks.

Extending an existing UiPath Automation Cloud platform into finance audit and year-end reconciliation with document extraction, anomaly detection, approval workflows and live audit KPIs, with no new licensing.

Measured results:

- Manual effort on audit and reconciliation reduced by up to 80 percent
- Analyst data extraction time cut from around 12 hours a week to under 2
- Audit preparation reduced from around 3 weeks a year to under 1
- Total annual benefit of around £150,000 across time saved, error reduction, faster decision-making and strategic enablement
- Payback in around 7.5 months, with year one savings exceeding delivery cost
- No additional licensing: delivered entirely on the platform the client already owned

Technology: UiPath Automation Cloud, UiPath Document Understanding, UiPath AI Centre, UiPath Action Centre, UiPath Insights, Sage, Power BI

### From inbox to ERP without the re-keying

https://ettgroup.ai/our-work/sales-order-processing-automation-manufacturing

Sector: Manufacturing and distribution. Region: UK. Duration: 4 week proof of concept, 8 to 11 week production build.

Automating purchase order intake from customer emails and PDFs into OrderWise, with machine learning to resolve inconsistent product codes and an Action Centre queue for anything ambiguous.

Measured results:

- Proof of concept validated on live purchase orders and SKU data within 4 weeks, before any licence commitment
- Production email-to-OrderWise automation live, removing manual order entry from the intake process
- Customer SKU descriptions matched to internal product codes by an AI Centre model that retrains on exceptions
- Ambiguous orders routed to Action Centre rather than guessed, with clarification emails to customers where needed
- Peak-period volume handled without adding staff, with throughput and exceptions tracked in UiPath Insights
- Internal IT enabled to own the platform, with the integration modularised for the planned OrderWise cloud migration

Technology: UiPath IXP, UiPath AI Centre, UiPath Action Centre, UiPath Orchestrator, UiPath Insights, OrderWise

## Insights

- [How to evaluate AI retrieval quality and cost on your own documents](https://ettgroup.ai/insights/how-to-evaluate-ai-retrieval-quality-and-cost): Vendor benchmarks tell you how a system performed on someone else's corpus. Here is how to run the test on yours, which measures to keep apart, and the comparison most evaluations quietly avoid.
- [When a rules-based workflow is enough, and when an AI agent adds value](https://ettgroup.ai/insights/when-rules-are-enough-and-when-an-ai-agent-adds-value): Not every workflow needs an agent. Deterministic automation is cheaper to build, cheaper to run and easier to assure — here is how to tell which one you are looking at, before you pay for the wrong one.
- [How to calculate automation value without confusing time released with cash saved](https://ettgroup.ai/insights/time-released-is-not-cash-saved): Hours released and money saved are different claims, and presenting one as the other is the fastest way to lose a finance stakeholder. Here is how to calculate both, and how to say which one you have.
- [What drives the cost of integrating voice AI with a contact centre](https://ettgroup.ai/insights/what-drives-the-cost-of-voice-ai-in-a-contact-centre): The licence is rarely the expensive part. Integration scope, intent count, language coverage and testing depth are what move the number — and each one is a decision you can make differently.
- [What an AI proof of value should include before it reaches production](https://ettgroup.ai/insights/what-an-ai-proof-of-value-should-include): A proof of value earns its name by being able to fail. Here is what one has to contain — criteria set before the build, a real workflow, a defined failure path — and what it costs you when any of that is missing.
- [The Cost of Asking: why AI bills rise as prices fall](https://ettgroup.ai/insights/the-cost-of-asking-why-ai-bills-rise-as-prices-fall): Token prices fell by roughly two thirds in a year while enterprise AI budgets grew about sixfold. The gap is volume, and the cause is structural: a language model has no index into a document, so every question pays to read everything.
- [How to measure the ROI of enterprise AI](https://ettgroup.ai/insights/how-to-measure-roi-of-enterprise-ai): The ROI of enterprise AI is measured by operational outcomes — time saved, errors reduced, faster decisions and better service — not by activity or model performance alone. Defining success metrics before deployment is essential.
- [What is conversational AI and voice automation, and where does it deliver value?](https://ettgroup.ai/insights/what-is-conversational-ai-and-voice-automation): Conversational AI and voice automation use natural language to handle interactions — answering questions, routing requests and triggering actions — while connecting to the systems behind them. The value comes when conversations lead to real operational outcomes.
- [From pilot to production: how enterprises operationalise AI](https://ettgroup.ai/insights/how-to-move-ai-from-pilot-to-production): Moving AI from pilot to production depends less on the model and more on orchestration, data readiness and governance. A structured approach — orient, prove, govern, scale, compound — is what makes the difference.
- [A practical guide to AI governance for enterprises](https://ettgroup.ai/insights/ai-governance-for-enterprises): AI governance is the set of policies, controls and oversight that keep AI systems safe, compliant and accountable as they scale. Done well, it enables AI adoption rather than blocking it.
- [What is AI orchestration, and why does it matter for enterprise AI?](https://ettgroup.ai/insights/what-is-ai-orchestration): AI orchestration is the layer that connects AI models, data, systems, workflows and people so automation works reliably and under control. It is what turns isolated AI tools into operational systems.
- [What makes business data AI-ready?](https://ettgroup.ai/insights/what-makes-business-data-ai-ready): An explanation of the data foundations needed for reliable automation, AI grounding and governed decision-making.
- [Why AI projects stall at pilot stage](https://ettgroup.ai/insights/why-ai-projects-stall-at-pilot-stage): A look at why enterprise AI initiatives lose momentum and what needs to be in place before they can scale.
- [What is agentic AI, and why should enterprises be paying attention?](https://ettgroup.ai/insights/what-is-agentic-ai): A practical introduction to agentic AI, orchestration and the move from AI experimentation to AI operation.

## Analect

Analect is an enterprise knowledge platform from ETT that decomposes unstructured documents into addressable knowledge fragments, so AI systems answer from the fragments a question needs instead of re-reading whole documents. On ETT's own published test, direct factual questions used 70 to 77 percent fewer context tokens than reading the documents whole, and the fragment layer for one document measured around 91 percent smaller than its source file. Those figures come from a small sample of nine questions across two documents; a pilot establishes whether they hold on your own material.

- [Analect platform](https://ettgroup.ai/analect): Analect turns document content into addressable knowledge fragments so AI retrieves what a question needs. Measure answer quality, token use and storage on your own documents before you deploy it.
- [Analect Forecast](https://ettgroup.ai/analect/forecast): Analect Forecast turns your AI invoice into workloads, model choices and a plan. Price every job across the model catalogue, with the pricing date on every forecast, before you commit the spend.
- [Analect Proof](https://ettgroup.ai/analect/proof): Analect Proof asks the same questions of your documents and their fragments, then reports agreement, correctness and completeness separately alongside the tokens each route used.
- [Analect Explorer](https://ettgroup.ai/analect/explorer): Analect Explorer turns documents into explorable knowledge fragments: entities, relationships, connections and gaps, with every fragment tied to its source sentence.
- [Analect Store](https://ettgroup.ai/analect/store): Analect Store weighs every form your documents could be kept in, in actual bytes, and models keeping your sources against replacing them. Bytes reduced and bill reduced are reported separately.
- [Whitepaper](https://ettgroup.ai/analect/whitepaper): Why enterprise AI bills keep rising while token prices fall, and how knowledge fragmentation changes the arithmetic. Download the public edition or request the full edition under NDA.
- [Pilot](https://ettgroup.ai/analect/pilot): A fixed-fee pilot on your own documents. We measure answer quality against a baseline you choose, token use and storage, and report the results that came back flat. If the numbers do not justify going further, the report is yours and you walk away.

### The four workflows

#### Analect Forecast

Analect Forecast is the AI cost modelling workflow in the Analect platform. It captures your workloads in plain language or from your provider bill, prices each one across a catalogue of public models, and shows the reasoning behind every recommendation. Prices move constantly, so every forecast carries the date of the model catalogue it was priced against.

#### Analect Proof

Analect Proof is the measurement workflow in the Analect platform. It takes a document and its decomposition, asks the same questions of both against a model you choose, and totals the tokens each route consumed. It reports three things separately: whether the answers agreed with each other, whether they were correct against ground truth you supply, and whether they were complete.

#### Analect Explorer

Analect Explorer is the decomposition and exploration workflow in the Analect platform. It reads unstructured documents and produces addressable knowledge fragments, then lets you explore what the material actually says: entities, relationships, connections between any two things, and the gaps.

#### Analect Store

Analect Store is the storage analysis workflow in the Analect platform. It measures every form your material could be kept in, in actual bytes, raw and compressed, and models both scenarios: keeping your source documents alongside the fragment layer, and replacing them. It distinguishes bytes reduced from bill reduced, and returns a verdict that is allowed to be no.

### Analect: the claims we publish

These are the only measured claims ETT makes publicly about Analect. Each
comes from ETT's own material. A pilot applies the same measurement approach
to a customer's own documents; it does not promise the same figures.

- 70 to 77 percent fewer retrieved context tokens on direct factual questions, on our own two-document sample
- Fragment answers agreed with the same model's whole-document answers on 8 of 9 benchmark questions
- One factual question about a clinical guideline answered from 355 tokens of fragments, against 8,000 tokens to read the document whole
- A fragment layer measured at roughly 300 KB against a 3.5 MB source document, about 91 percent smaller — the fragment layer alone, excluding source retention, metadata, indexes, replicas and backups

Scope: These figures are from a small ETT sample: nine direct factual questions across two documents, compared against the same model reading each document whole. They show the effect exists. They do not predict its size on your estate, and they exclude the cost of decomposing documents in the first place.

### Whitepaper

"The Cost of Asking" sets out why enterprise AI bills keep rising while token
prices fall, and how changing the unit of knowledge changes the arithmetic.
The public edition is available at https://ettgroup.ai/analect/whitepaper. A fuller
edition, with the detailed measurements and method, is shared under a standard
mutual NDA on request.

### Pilots

ETT runs fixed-fee pilots that measure accuracy, tokens and storage on a slice
of a customer's own document estate, and report the results that came out flat
as well as the wins. If the numbers do not justify going further, the report is
the customer's to keep. Details: https://ettgroup.ai/analect/pilot

## Partners

- [OpenAI](https://ettgroup.ai/partners/openai): Foundation models. ETT builds on OpenAI models where they are the right fit for a client's use case, grounding them in governed enterprise data and wrapping them in the orchestration, oversight and security controls production deployments demand.
- [NVIDIA](https://ettgroup.ai/partners/nvidia): AI compute. ETT designs AI infrastructure on NVIDIA hardware, including dedicated H100 GPU capacity delivered through CUDO Compute, so clients get predictable performance for training and inference without owning a data centre.
- [CUDO Compute](https://ettgroup.ai/partners/cudo-compute): GPU cloud. CUDO Compute is ETT's infrastructure partner for dedicated GPU-hosted IaaS — the platform behind the H100 compute capacity ETT provisions for clients building AI native to their business.
- [UiPath](https://ettgroup.ai/partners/uipath): Automation platform. ETT designs and delivers UiPath automations as part of its agentic automation practice — from first process candidates through to scaled, governed automation estates with centre-of-excellence enablement.
- [Databricks](https://ettgroup.ai/partners/databricks): Data intelligence. ETT uses Databricks to build the data and analytics foundations that ground AI systems — pipelines, governance and real-time intelligence that turn raw enterprise data into something models can safely rely on.
- [PolyAI](https://ettgroup.ai/partners/polyai): Voice AI. PolyAI is a core partner in ETT's conversational AI practice — the voice layer ETT deploys when clients need enterprise-grade call automation that customers actually enjoy speaking to.
- [GetVocal AI](https://ettgroup.ai/partners/getvocal-ai): Hybrid AI agents. ETT works with GetVocal AI where clients need governed, multilingual customer-facing agents with strong human oversight — a natural fit for regulated sectors where transparency and control are non-negotiable.
- [CrowdStrike](https://ettgroup.ai/partners/crowdstrike): Endpoint security. Within ETT's vCISO practice, CrowdStrike provides the endpoint and workload protection layer for AI platform deployments — securing the environments where models, agents and sensitive data operate.
- [Check Point](https://ettgroup.ai/partners/check-point): Network security. ETT deploys Check Point as part of the secure-by-design network foundations underneath AI platforms — controlling the traffic, segmentation and threat prevention around systems that handle sensitive data.
- [Cisco](https://ettgroup.ai/partners/cisco): Networking. ETT builds on Cisco for the networking and connectivity layer of client environments — reliable, secure infrastructure that AI systems, data pipelines and automation platforms run across.
- [Cavelo](https://ettgroup.ai/partners/cavelo): Data discovery. Cavelo powers ETT's data discovery and classification work — mapping and classifying sensitive data before it goes anywhere near a model, so AI initiatives start from a known, governed data estate.
- [Hunters](https://ettgroup.ai/partners/hunters): SIEM & SOC. ETT uses Hunters as the detection and response backbone in security architectures for AI deployments — visibility across the platforms, identities and data flows that AI systems introduce.

## Who runs ETT

### George McKenna, Chairman & CTO

Eighteen years in enterprise IT before ETT, most recently leading solution sales at Trustmarque and running cloud sales and operations at Ultima Business Solutions. That was a career spent on the supplier side of large IT programmes, watching good ideas stall because nobody could say where to start, which is the problem ETT was set up to solve. He is accountable for ETT's technical direction.

LinkedIn: https://www.linkedin.com/in/glmckenna

### Ryan Carter, Group Chief Executive Officer

Twenty years building and scaling technology ventures, most of it in secure communications and privacy technology. He has run global market development at Bittium, led corporate transformation as president of Vital Group of Companies, and founded two companies of his own before that. He leads the group, and is accountable for growth and commercial strategy across manufacturing, financial services, healthcare, retail and logistics.

LinkedIn: https://www.linkedin.com/in/ryandalecarter

## Company and engagement

- [About ETT](https://ettgroup.ai/about): who we are and how the firm is set up.
- [How we work](https://ettgroup.ai/how-we-work): the five stages of an engagement, what each produces and what it decides.
- [Our work](https://ettgroup.ai/our-work): published case studies, plus sample deliverables showing what a delivery output looks like.
- [Trust and security](https://ettgroup.ai/trust): how we handle data, access and oversight.
- [Automation readiness assessment](https://ettgroup.ai/automation-assessment): a short questionnaire returning an indicative readiness view.
- [Executive AI acceleration session](https://ettgroup.ai/executive-ai-acceleration-session): the usual first conversation.

## Contact

ETT. sales@ettgroup.ai. https://ettgroup.ai

Headquarters: Dallas, Texas, United States.
Other locations: London, United Kingdom; Vancouver, British Columbia, Canada.
Sitemap: https://ettgroup.ai/sitemap.xml