From unread query to review-ready response.
Mach Lilies AI Helpers monitor unread messages in 1-800Accountant’s Queries workflow, recognise account-validation and missing-information requests, and prepare draft responses for the client team.
1-800Accountant
- 67% less Reported time spent Internal pre/post staff comparison
- $666K Estimated annualised gross staff-capacity value Client labour-hour model
- 1 inbox Existing Queries workflow Human-controlled drafts
Based on a client-reported internal pre/post comparison and client labour-cost assumptions. The $666K figure is annualised gross staff-capacity value, not an independently audited cash saving.
A high-volume virtual accounting workflow.
1-800Accountant combines accounting technology with human oversight for small businesses, freelancers and startups. Its Queries workflow receives customer messages that can require account validation or requests for additional information before the team can continue.
Opening the email was not the work.
- Unread query
- Understand what the customer needs
- Identify an account or information issue
- Collect the relevant context
- Draft the next response
- Return the case to the team
Repetitive handling time accumulated in the interpretation and response work around each query. The opportunity was not to automate professional judgement; it was to prepare the next safe, reviewable action sooner.
The same queue, run two ways.
Before: a person opens the unread query, determines the request type, checks what account or information context is missing, drafts the response, and the workflow continues manually. With Mach Lilies: the Helper monitors the agreed unread-query route, recognises the account-validation or missing-information context, and prepares a contextual draft; the client team then reviews and controls the next client-facing action.
Before
- A person opens the unread query.
- The person determines the request type.
- The person checks what account or information context is missing.
- The person drafts the response.
- The workflow continues manually.
With Mach Lilies
- The Helper monitors the agreed unread-query route.
- The Helper recognises the account-validation or missing-information context.
- The Helper prepares a contextual draft.
- The client team reviews and controls the next client-facing action. People decide
The Helpers prepare drafts; people retain control of client-facing decisions.
Watch a query become a review-ready draft.
The reconstruction below follows one synthetic case through the stages the Helpers actually work: an unread message in the Queries inbox is recognised, the missing information is identified, a draft is prepared, and the case waits for a person.
Queries inbox
- Customer 024 Account information required Draft prepared
- Customer 031 Validation check requested Awaiting reviewer
- Customer 017 Further information needed Unread
Case rail · Customer 024
- Issue recognised Account-validation request identified
- Missing information identified Required account details not on file
- Draft prepared Contextual response ready for review
- Human review Awaiting reviewer
Draft response · Customer 024
Prepared by the Helper · awaiting reviewer · nothing sends without a person
Less handling time, expressed honestly.
Reported query-handling time, indexed
- Reported baseline time index
- 100
- Reported time index with the Helpers
- 33
Difference: 67% lower
The client’s internal labour-hour model valued the reported time release at approximately $666K on an annualised gross-capacity basis. This describes the estimated value of released staff time; it does not state that payroll or another cash expense fell by $666K.
AI-supported. Human-owned.
AI Helper scope
- Monitor the agreed unread-query route.
- Recognise account-validation and missing-information requests.
- Prepare draft responses.
- Keep the next action reviewable.
Human-owned scope
- Decide whether the draft should be used.
- Make accounting, tax and professional judgements.
- Handle sensitive, unclear or exceptional queries.
- Control client-facing decisions.
How the result was measured and described.
- Relationship
- Ongoing live proof of concept
- Deployment start
- 4 November 2025
- Workflow
- Queries inbox response drafting
- Primary result source
- Internal client pre/post staff comparison
- Reported outcome
- 67% less time spent on the defined query-handling workflow
- Financial classification
- Estimated annualised gross staff-capacity value
- Source currency
- USD
- Audit status
- Not independently audited
The result is specific to this workflow, client environment and internal comparison. It is not a guarantee of the same result for another practice.
Not an MTD case study. A proven operating pattern.
This deployment did not concern MTD quarterly record chasing. It demonstrates the underlying inbox pattern that Quiet Quarter applies to UK accountancy practices: identify the request, recognise missing information, prepare the next action and keep a person in control.
Read How Quiet Quarter is scoped for accountancy practices, or see both deployments side by side.
Bring us one chase loop.
We will tell you whether it is worth installing, what can move safely and what must stay human-led.