# ETT (ETT Group of Companies Inc.)

> 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.
A fuller version, with every engagement's deliverables, inputs, measures and
answers to common questions, is at https://ettgroup.ai/llms-full.txt.

## What ETT is

ETT Group of Companies Inc. (ETT) 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 Group of Companies Inc. is a UiPath Authorized Partner and a UiPath
Services Certified Partner, and resells UiPath. Where a platform licence is
part of the solution, it is quoted as its own line, separate from ETT's fee.
ETT Group is also a PolyAI wholesale partner and supplies the PolyAI voice
platform with the delivery around it.
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.

## Delivery platform

ETT Group of Companies Inc. is a UiPath Authorized Partner and a UiPath Services Certified Partner, and resells UiPath licences. Our delivery team holds UiPath technical certifications.

Most ETT automation delivery runs on UiPath: Process Mining, Task Mining and
Task Capture to find the work; IXP, Document Understanding, AI Center, robots
and AI agents to do it; Action Center, Automation Ops and Insights to govern
and run it. Five of the six published case studies below are UiPath
engagements. More: https://ettgroup.ai/partners/uipath

ETT Group is a PolyAI wholesale partner: we supply the PolyAI platform and deliver the voice agent around it under one contract.

Voice agents run on PolyAI. A Voice Agent Proof of Value is analytics on the
client's own call data; nothing is deployed until the case is proven.
More: https://ettgroup.ai/partners/polyai

## How ETT delivers

- Plan and prioritise: [Business Process Workshop](https://ettgroup.ai/services/business-process-workshop), a fixed-price workshop that maps the processes, sets the baseline and ranks what to automate, fix or leave alone.
- Build and connect: [Process Mining and Discovery](https://ettgroup.ai/services/process-mining-and-discovery), then [Agentic Automation on UiPath](https://ettgroup.ai/services/agentic-ai-and-automation): robots, AI agents and people orchestrated as one governed process.
- Govern and operate: [Agentic Managed Services](https://ettgroup.ai/services/agentic-managed-services) and [Analect](https://ettgroup.ai/analect): managed operation with drift checks and a monthly evidence report, and AI outputs checked claim by claim.

## Routes to outcome

ETT's engagements, in the order a buyer takes them. Each states how long it
takes, what the buyer leaves with and the decision at the end. A buyer can
start at any step and stop after any step, keeping what that step produced.
Overview: https://ettgroup.ai/routes-to-outcome

### Step 1: [AI Executive Briefing](https://ettgroup.ai/services/ai-executive-briefing)

Question it answers: Where would AI actually pay back in our operation?
What it is: A working session for your leadership team: a reality check, the scoring method, and a live map of your own operation.
Duration: 90 minutes. Who is involved: Six to ten executives, with a 15-minute sponsor call beforehand.
Leave with: A one-page opportunity map within 48 hours; Two or three candidate processes; An honest next step, including "nothing yet".
Decision at the end: Is there anything worth pursuing, and which route fits it?

### Step 2: [Executive Value Day](https://ettgroup.ai/services/executive-value-day)

Question it answers: Which AI investments will our operations own, and pay back inside nine months?
What it is: One working day, with pre-work, that turns ambition into opportunity charters owned by the executives accountable for the work.
Duration: One day, plus pre-work. Who is involved: Eight to twelve executives, led by a CEO or COO sponsor.
Leave with: An enterprise value map; One-page opportunity charters; A ranked investment shortlist and decisions log.
Decision at the end: Which charters go forward, and who owns each one?

### Step 3: [Business Process Workshop](https://ettgroup.ai/services/business-process-workshop)

Question it answers: What is this process really costing us, and what should we automate, fix or leave alone?
What it is: A fixed-scope workshop that maps the processes, sets the baseline and ranks what to automate, fix or leave alone. No system access needed.
Duration: Four or six weeks. Who is involved: A sponsor, and about an hour each with the people who run the processes.
Leave with: Process maps and a heat map; A baseline and benefits workbook; A prioritised roadmap and executive report.
Decision at the end: Build, fix, or stop.

### Step 3: [Process Mining and Discovery](https://ettgroup.ai/services/process-mining-and-discovery)

Question it answers: How do our core processes really run, measured from the systems themselves?
What it is: The same decision as the workshop, made from system event data and desktop capture, on the UiPath platform the build will run on.
Duration: About 12 weeks. Who is involved: Read-only access to the systems in scope, plus process owners and nominated users.
Leave with: Mined process flows and variants; An error and rework baseline; A ranked roadmap and a platform ready to build on.
Decision at the end: Which processes to build first, on evidence.

### Step 4: [Proof of Value](https://ettgroup.ai/services/proof-of-value)

Question it answers: Does it work on our process, against measures our finance team set first?
What it is: One priority process built on your systems and judged against success criteria signed before the build.
Duration: Three to four weeks. Who is involved: A process owner, a finance signatory and integration access.
Leave with: A working build; A results workbook and an Evidence File; A go or no-go record and a production plan.
Decision at the end: Go, go with conditions, or no-go.

### Step 5: [Pilot build](https://ettgroup.ai/services/agentic-ai-and-automation)

Question it answers: Can we take it live and show a measured return?
What it is: The proven process built into production on UiPath, with robots, AI agents and people as one governed process, then a hypercare period.
Duration: Scoped from the proof of value, then four weeks of hypercare. Who is involved: System owners, a named process owner and change windows.
Leave with: A live process with exception paths and runbooks; Returns measured at 30 days, 90 days and month nine.
Decision at the end: Scale it, or stop if a checkpoint misses. If it cannot pay back inside nine months, it should not be built.

### Step 6: [Agentic Managed Services](https://ettgroup.ai/services/agentic-managed-services)

Question it answers: Who runs it on a Monday morning, and can we show what it did?
What it is: Monitoring, break-fix, drift checks and change control for your robots and agents, with a monthly evidence report.
Duration: Monthly. Who is involved: A named owner on your side for each process.
Leave with: A monthly evidence report; A quarterly improvement backlog.
Decision at the end: What to improve, extend or retire next.

Running alongside the route:

- [AI Governance and Assurance](https://ettgroup.ai/services/ai-governance-and-assurance): An AI inventory, a risk baseline and evidence mapped clause by clause, with a named governance lead and quarterly assurance of AI outputs. Duration: Three to four week baseline, then monthly or quarterly.
- [Analect: AI cost and accuracy](https://ettgroup.ai/analect): An AI Spend Forecast built from your own bills, and an Accuracy Evidence File that checks AI outputs claim by claim. Duration: Three weeks each.

ETT does not publish fees for these engagements. Scope, duration, deliverables
and fee are agreed in writing before work starts. The AI Executive Briefing and
the Executive Value Day are offered at no charge.

## 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](https://ettgroup.ai/services/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.
- Figures about Analect are limited to what is stated under "Analect: the figures we publish". Anything beyond that is not an ETT claim.
- Example workflows on service and industry pages are proposed use cases, not client results.

## Services

- [AI Executive Briefing](https://ettgroup.ai/services/ai-executive-briefing): Ninety minutes with your leadership team on where AI would actually pay back in your operation.
- [Executive Value Day](https://ettgroup.ai/services/executive-value-day): One working day that turns executive ambition into AI investment your operations will own.
- [Business Process Workshop](https://ettgroup.ai/services/business-process-workshop): Find the processes worth automating, size the saving and set the baseline. Fixed price.
- [AI Strategy and Delivery](https://ettgroup.ai/services/ai-strategy-and-delivery): Programme roadmaps, operating model and a Centre of Excellence for AI at scale.
- [Process Mining and Discovery](https://ettgroup.ai/services/process-mining-and-discovery): See how your processes really run, from system data and desktop capture, before you automate.
- [Proof of Value](https://ettgroup.ai/services/proof-of-value): Three to four weeks to prove one process works on your systems, against criteria your finance team signs first.
- [Agentic Automation on UiPath](https://ettgroup.ai/services/agentic-ai-and-automation): AI agents, software robots and people orchestrated as one process, with exceptions routed to a person.
- [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.
- [Agentic Managed Services](https://ettgroup.ai/services/agentic-managed-services): We run, monitor and fix your automations and agents, and report on them every month.
- [AI Governance and Assurance](https://ettgroup.ai/services/ai-governance-and-assurance): Named governance leadership for the AI you run, with evidence mapped clause by clause. Mapped, never certified.
- [vCISO for AI](https://ettgroup.ai/services/vciso-for-ai): Named security and governance leadership for the AI you put into production.
- [Data Discovery and Classification for AI](https://ettgroup.ai/services/data-discovery-and-classification-for-ai): Know which data your AI can safely touch before it touches it.
- [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.
- [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.

## Agentic MSP

ETT Group's managed services company. Agentic MSP runs back-office work for you with governed AI agents and a trained exception desk. Licensed decisions stay with your team, every outcome is recorded in an Evidence File, and the service is billed on verified outcomes rather than seats or hours. For independent insurance agencies and mid-market finance teams in the US and Canada. It has its own site, https://agentic-msp.com.

- [How it works](https://agentic-msp.com/how-it-works)
- [Insurance agencies](https://agentic-msp.com/insurance-agencies)
- [Finance operations](https://agentic-msp.com/finance-operations)

## 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, then build on the same platform.

A won, fixed-fee engagement mapping three priority processes from system event data and from the people who run them, on a UiPath platform hosted in Canada that every later automation will run on.

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

- One validated picture of how each of the three processes really runs, from system data and the people doing the work
- An error and rework baseline per process, including incorrect payments in the rewards process
- Undocumented steps, side spreadsheets and missing processes surfaced, reducing key-person dependency
- Automation opportunities ranked by value, feasibility and risk at a single Business Process Workshop
- A governed UiPath Automation Cloud tenant, hosted in Canada, ready for the first build without re-platforming

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

## Resources

Articles, how-to guides and whitepapers. Overview: https://ettgroup.ai/resources

### Whitepapers

Each whitepaper has a public page with its summary, key takeaways and contents, which may be quoted with a link to that page. The full paper is a PDF sent free of charge to anyone who completes the short form on its page; there is no direct public link to the file.

- [Every call answered. Every answer on the record.](https://ettgroup.ai/resources/whitepapers/every-call-answered-every-answer-on-the-record): Published containment for AI voice agents runs from about a fifth to two thirds of calls, and in regulated firms governance takes far longer than the build. It is for contact center, operations, risk and technology leaders choosing which calls to automate and how to test the case.
- [Automate the process. Not the task.](https://ettgroup.ai/resources/whitepapers/automate-the-process-not-the-task): Automation value sits in the end-to-end process, not the individual task, and agents now make it possible to automate the steps that needed judgment. It is for operations, technology and risk leaders in regulated industries who need to choose a first process, place each worker and govern it.
- [You don't need an AI team. You need the work done.](https://ettgroup.ai/resources/whitepapers/you-dont-need-an-ai-team-you-need-the-work-done): Most small businesses use AI, but only 14% of owners say it is fully integrated into core operations, because running it is the obstacle, not the technology. It is for owners and managers choosing what to hand over first and how to judge a provider.
- [The budget is set. The bill is not.](https://ettgroup.ai/resources/whitepapers/the-budget-is-set-the-bill-is-not): Most 2027 AI budgets are built from list prices and seat counts, so they will miss the real bill, which depends on volume, work per task, model mix and repeats. It is for CFOs and technology leaders, who can use five controls and a 90-day plan to reforecast.
- [Three regulators. One evidence trail.](https://ettgroup.ai/resources/whitepapers/three-regulators-one-evidence-trail): US, UK and Canadian regulators take different positions on AI agents, but all ask the same ten questions, and one case-level evidence trail can answer them. It is for risk, compliance, technology and operations leaders who need to test an agent against those questions and close the gaps.
- [AI Outcomes need evidence.](https://ettgroup.ai/resources/whitepapers/ai-outcomes-need-evidence): Outcome-based pricing for AI-run services only works if buyer and provider agree the outcome definition, the baseline and how each result is verified. This guide helps operations, finance and procurement leaders settle those terms and choose a first process before signing.

### Guides

Step-by-step methods, checklists and templates.

- [How to launch an AI voice agent in a regulated contact centre: a 10-step checklist](https://ettgroup.ai/resources/guides/launch-ai-voice-agent-regulated-contact-centre): A 10-step checklist that runs governance alongside the build: choosing the call type, setting the baseline, disclosure, handover, identity and payments, testing, controlled release and the evidence you keep each month.
- [How to work out AI cost per task before you build](https://ettgroup.ai/resources/guides/ai-cost-per-task): A step-by-step method for AI cost per task: model calls, tokens, hidden reasoning, price multipliers, checking and volume, with a worked example.
- [EU AI Act Article 50 and 2 December: a checklist for UK and North American firms](https://ettgroup.ai/resources/guides/eu-ai-act-article-50-checklist): What Article 50 of the EU AI Act asks of UK and North American firms, what changes on 2 December 2026, and a checklist to start now.
- [An AI agent governance framework: a control template for regulated firms](https://ettgroup.ai/resources/guides/ai-agent-governance-framework): A fill-in template for governing AI agents: register entry, autonomy tiers, approval gates, logging, identity, kill switch and staged rollout.
- [How to measure what an AI summary leaves out](https://ettgroup.ai/resources/guides/measure-ai-omissions): Accuracy checks catch what AI adds, not what it drops. A step-by-step method and table template to check an AI summary for missing information.
- [Process mining, process intelligence or a discovery workshop: which comes first?](https://ettgroup.ai/resources/guides/process-mining-vs-process-intelligence): Process mining, task mining, process intelligence and discovery workshops compared: what each tells you, what it needs, and which to start with.
- [The AI evidence pack: ten records your regulator or auditor will ask for](https://ettgroup.ai/resources/guides/ai-evidence-pack): What evidence do auditors and regulators need for AI? Ten records, who asks for each, what good looks like, and where firms usually fall short.
- [How to build an AI spend forecast your finance team will sign off](https://ettgroup.ai/resources/guides/how-to-build-an-ai-spend-forecast): A step-by-step method for turning AI invoices into a forecast by workload: what to collect, how to measure each job, how to price the alternatives and how to present a budget that survives a finance review.
- [An accuracy score is not evidence: how to measure AI output so the result stands up](https://ettgroup.ai/resources/guides/an-accuracy-score-is-not-evidence): "Our AI is 95% accurate" answers none of the questions an auditor will ask. Measured against what? Counting what? And what about the facts it left out? Here is what the research says a defensible measurement looks like.
- [How to check an AI-written document against its sources, claim by claim](https://ettgroup.ai/resources/guides/how-to-check-an-ai-written-document-against-its-sources): A step-by-step method for verifying a summary, report or letter drafted by AI: fixing what you check, breaking it into claims, finding the evidence, giving honest verdicts, looking for omissions and keeping a record that stands up later.
- [What an AI proof of value should include before it reaches production](https://ettgroup.ai/resources/guides/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.
- [How to calculate automation value without confusing time released with cash saved](https://ettgroup.ai/resources/guides/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.
- [How to evaluate AI retrieval quality and cost on your own documents](https://ettgroup.ai/resources/guides/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.
- [How to measure the ROI of enterprise AI](https://ettgroup.ai/resources/guides/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.
- [From pilot to production: how enterprises operationalise AI](https://ettgroup.ai/resources/guides/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/resources/guides/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.

### Articles

- [The voice is the easy part](https://ettgroup.ai/resources/articles/the-voice-is-the-easy-part): PolyAI says one UK bank's voice agent was built in about six weeks and governed in six months. Here is where the time goes, and what to put in front of your risk team in week 1.
- [Containment is the wrong number](https://ettgroup.ai/resources/articles/containment-is-the-wrong-number): In PolyAI case studies and NS&I's GOV.UK record, voice agents in regulated service lines contain 21% to 42% of calls. That is a good result. What matters more is what happens to the rest, and to the callers who ring back.
- [Your voice agent speaks for you. Who signed off what it says?](https://ettgroup.ai/resources/articles/who-signed-off-what-your-voice-agent-says): Disclosure, vulnerable callers, a route to a person, recording and payments: what the rules expect of an AI voice agent in a regulated firm, and a one-page sign-off sheet to complete before go-live.
- [Your 2027 AI budget will be wrong. Here's how to make it wrong by less.](https://ettgroup.ai/resources/articles/your-2027-ai-budget-will-be-wrong): Gartner has published three different 2026 AI spend forecasts this year. Here's how to set a 2027 AI budget your finance team can defend.
- [Brussels moved the deadline, not the risk](https://ettgroup.ai/resources/articles/brussels-moved-the-deadline-not-the-risk): The EU delayed its high-risk AI rules to December 2027. The parts that didn't move already apply, and on 2 December 2026 more of them start to bite.
- [Everyone counts what AI makes up. Almost nobody counts what it leaves out.](https://ettgroup.ai/resources/articles/what-ai-leaves-out): In one study of clinical notes, AI left things out more often than it made things up. Why omissions need their own number, and how to count them.
- [Field story: the logistics business that stopped building bots one at a time](https://ettgroup.ai/resources/articles/stop-building-bots-one-at-a-time): More than 220 automations, each built from scratch. Here's what changed when the business agreed the parts before building any more machines.
- [Seats or tokens: are your AI licences paying for themselves?](https://ettgroup.ai/resources/articles/seats-or-tokens-are-your-ai-licences-paying-for-themselves): Most organisations now pay for AI twice: per seat for the assistants their people use, and per token for the systems their developers build. Nobody compares the two, so nobody knows which half is the expensive one.
- [What actually drives an AI bill: the levers behind one number](https://ettgroup.ai/resources/articles/what-actually-drives-an-ai-bill): An AI invoice arrives as a single figure, but it is the product of half a dozen separate decisions. Model choice, output length, hidden reasoning, caching and timing can each move the same workload's cost several times over.
- [What the AI hallucination cases mean for any firm that signs AI-drafted work](https://ettgroup.ai/resources/articles/what-the-ai-hallucination-cases-mean-for-firms-that-sign-ai-work): Courts and tribunals on both sides of the Atlantic have now ruled on AI-generated errors. The pattern is consistent: the tool is never the defendant. Whoever put their name to the output answers for it.
- [What drives the cost of integrating voice AI with a contact centre](https://ettgroup.ai/resources/articles/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.
- [When a rules-based workflow is enough, and when an AI agent adds value](https://ettgroup.ai/resources/articles/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.
- [The Cost of Asking: why AI bills rise as prices fall](https://ettgroup.ai/resources/articles/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.
- [What is conversational AI and voice automation, and where does it deliver value?](https://ettgroup.ai/resources/articles/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.
- [What is AI orchestration, and why does it matter for enterprise AI?](https://ettgroup.ai/resources/articles/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/resources/articles/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/resources/articles/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/resources/articles/what-is-agentic-ai): A practical introduction to agentic AI, orchestration and the move from AI experimentation to AI operation.

## Analect

Analect is ETT's platform for running AI as an accountable operating cost. It forecasts what each AI workload should cost and which model should do it, and it certifies AI outputs claim by claim against the documents they came from.

Analect also has its own site, https://analect.ettgroup.ai/, with the demo booking, the free tools, the whitepaper, the public certificate check and the customer sign-in. Its own /llms.txt is at https://analect.ettgroup.ai/llms.txt.

- [Seat break-even calculator](https://analect.ettgroup.ai/tools/seat-break-even): compare AI seat licences with the same vendor's pay-per-use pricing, user group by user group.
- [Sample Evidence File](https://analect.ettgroup.ai/sample-certificate): a synthetic Evidence File with a contradiction, an unsupported claim and an omission.
- [Check a certificate](https://analect.ettgroup.ai/verify): confirm a Certificate of Accuracy was issued by Analect and has not been withdrawn.

- [Analect platform](https://ettgroup.ai/analect): Analect is ETT's platform for running AI accountably: forecast what each workload should cost, and certify AI outputs claim by claim against their sources.
- [AI Spend Forecast](https://ettgroup.ai/analect/ai-spend-forecast): A three-week, fixed-price forecast that turns your AI bills and workloads into model choices, a verdict on seats against tokens, and a budget you can defend.
- [Accuracy Evidence File](https://ettgroup.ai/analect/accuracy-evidence-file): A fixed-price evidence pack: your AI outputs checked against their source documents, claim by claim, showing what was supported, contradicted or left out.

### The two services

#### AI Spend Forecast

The AI Spend Forecast is a three-week, fixed-price service from ETT that turns your AI invoices and workloads into model choices, a verdict on seats against tokens, and a budget your finance team can sign off. It runs on Analect.

#### Accuracy Evidence File

The Accuracy Evidence File is a three-week, fixed-price service from ETT. We check a set of your AI outputs against the documents they came from and show, for every claim, whether the source supports it, contradicts it or never mentions it, and what the output left out. It runs on Analect.

Both are fixed-price and run for three weeks. ETT does not publish their fees;
the fee is agreed in writing for each engagement.

### The workflows behind them

- Analect Forecast: Prices every AI workload across the model catalogue, from your bills or a plain description, with the reasoning behind each pick and the catalogue's date on every figure. Part of the AI Spend Forecast.
- Analect Evidence File: Breaks an AI output into its claims and checks each one against its source with two independent models, quoting the passage every verdict rests on. Part of the Accuracy Evidence File.
- Analect Explorer: Decomposes documents into knowledge fragments, each tied to the sentence it came from, so you can see what a document actually asserts. Access arranged as part of a pilot.
- Analect Proof: Asks the same questions of a document and its fragments, and reports agreement, correctness and completeness separately, with the tokens each route used. Access arranged as part of a pilot.
- Analect Store: Weighs every form your material could be kept in, in actual bytes, and models keeping your sources against replacing them. Access arranged as part of a pilot.

### Analect: the figures we publish

ETT publishes no measured figures for the Analect knowledge engine at present.
An early test on ETT's own two-document sample, reported in the September 2026
whitepaper, is indicative only. Results will be published with their method once
they hold beyond that sample. The one number on the Analect pages is the size of
the Forecast model catalogue: around 200 models from 27 providers.

### 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://analect.ettgroup.ai/whitepaper. A fuller
edition, with the detailed method, is shared under a standard mutual NDA on
request.

### Pilots

Where a service shows something worth going further on, ETT runs fixed-fee pilots
on an agreed slice of a customer's estate, including the knowledge engine compared
with the customer's current retrieval. Pilot reports include the results that came
back flat as well as the wins, and if the numbers do not justify going further,
the report is the customer's to keep. Details: https://analect.ettgroup.ai/demo

## Partners

- [UiPath](https://ettgroup.ai/partners/uipath): Automation platform. ETT is a UiPath Authorized Partner, a UiPath Services Certified Partner and a UiPath reseller. We design, build and run automation on the UiPath Platform for regulated businesses in North America and the UK: process mining and task mining to find the work, document AI, robots and AI agents to do it, and a managed service to keep it running.
- [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.
- [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. ETT Group is a PolyAI wholesale partner. We supply the PolyAI platform and build everything around it: the calls worth automating, the integrations that let the agent act, and the controls and evidence a regulated business needs.
- [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

### Richard Manser, Chief Delivery Officer

Fifteen years delivering technology and automation, with a career rooted in quality engineering and a focus on making sure what gets built works in the real world. He has worked at the point where strategy meets delivery: designing solutions, building delivery capability and bringing the rigour of quality engineering to the way technology is built, governed and operated. At ETT, he owns how client work is scoped, delivered, evidenced and handed over, alongside its quality and commercial performance. His job is simple to describe: 'make sure ETT delivers what it promises'.

LinkedIn: https://www.linkedin.com/in/richard-manser-55890735/

## Company and engagement

- [About ETT](https://ettgroup.ai/about): who we are and how the firm is set up.
- [Routes to outcome](https://ettgroup.ai/routes-to-outcome): the engagements in order, with duration, deliverables and the decision at the end of each.
- [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

ETT USA Sales: +1 469 281 9195.
ETT CAN Sales: +1 604 359 2017.

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