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Industries & Use Cases
The result first: enquiries answered fast, records complete, follow-ups never dropped, and approvals no longer stuck in inboxes. The work that costs you most is rarely the work your clients pay for: chasing inquiries, re-typing data, answering the same questions, and waiting on approvals. Each section below starts with the result to aim for, then shows one typical problem in your kind of firm, how automation could work, where a person stays in control, and what would be measured against a baseline agreed before implementation. No percentage saving, revenue lift, or client outcome is promised here.
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Jump to: Professional services · E-commerce & distribution · Back-office operations · HR admin · Sales follow-up · Customer support · Regulated work (future only)
Who this fits best
This page fits best if: your team repeats the same operational work every week; you already use a CRM or another system that holds the record; and someone senior will own the pilot and agree the baseline it is measured against. If none of those is true yet, the honest first step is still the AI Opportunity Discovery Call — to establish whether a pilot makes sense at all.
First focus
Professional services
The result to aim for: every enquiry answered promptly, recorded completely, and followed up on time — with a person approving every proposal and every outbound message.
Who this covers: consultancies, agencies, accounting and advisory firms, legal and professional practices, and other B2B service firms where work starts with an enquiry and ends with a proposal, engagement, or deliverable.
Typical problem: Intake, qualification, proposals, and follow-up are manual and inconsistent. Enquiries arrive by email, web form, and phone; the same questions are asked twice; proposal drafts are rebuilt from old files; and follow-ups depend on individual memory. The result is slow first responses, incomplete records, and work that is hard to hand over when someone is away.
The business cost is not just slow replies: senior time spent on intake and follow-up is time not spent on billable work, and an enquiry answered late is an engagement a competitor can win first.
How automation could work: An enquiry could be captured once, checked for completeness, and entered in the CRM with its source and next step. A draft qualification summary could be prepared from the enquiry and any approved intake notes. A proposal draft could be assembled from approved templates and fee/scope wording, then queued for a person to edit and approve. Follow-ups could be scheduled automatically, with a draft message prepared — not sent — for human approval. Approved documents could be filed in the document system with the client record, so the next person can see the full history.
Systems to integrate: CRM, email and web forms, document storage, scheduling/calendar, proposal and billing tools where already in use.
What we would need from you: the approved intake questions, proposal templates, and fee/scope wording — names of systems only, no access or passwords at this stage.
Human review points: Qualification decisions and any decision to decline or refer a case; proposal scope, wording, and fees; every outbound message before it is sent; and any change to an existing client record.
A good first pilot could be: one intake route (for example, web-form enquiries only) with a clear start and end, rather than every channel at once.
See it in outline: the lead scoring & routing demo and the sales follow-up demo — illustrative demos with synthetic data, not client case studies.
Possible KPIs to measure: lead response time, share of enquiries with a complete CRM record, qualified-lead rate, follow-up completion rate, and time from enquiry to proposal — each measured against a baseline agreed before implementation. These are measures to track, not results achieved.
First focus
E-commerce & distribution
The result to aim for: customers get order and delivery answers from system records without waiting, and agents spend their time on exceptions and complaints, not repeated questions.
Who this covers: online stores and distributors selling physical or digital products, where pre-purchase questions, order updates, returns, and stock checks create repeated support work.
Typical problem: Pre-purchase questions, order updates, and inventory checks overload support. Customers ask the same delivery, sizing, compatibility, and returns questions; agents switch between the store, the helpdesk, and the inventory system to answer; and order-status replies are delayed during peaks.
For distributors, the same pattern shows up in order processing: stock and delivery questions answered from different systems, order exceptions found late, and returns triaged by hand. The pilot discipline is the same — one question type or one order flow, answered only from system records and approved wording.
The business cost is support time spent answering questions a system already knows the answer to, and orders delayed while a person hunts across three systems.
How automation could work: A customer question could be classified (order status, delivery, returns, product detail, or complaint) and matched to approved product and policy knowledge. Order status could be pulled from the store system and included in a draft reply for an agent to check. Returns and exchange requests could be triaged against approved policy, with the form and next steps prepared. Stock or delivery questions that cannot be answered from approved knowledge could be routed to a person with the full conversation attached, rather than answered by guessing.
Systems to integrate: Store platform, helpdesk, CRM, inventory and order-management system, shipping/carrier tracking where already in use.
What we would need from you: the approved product and policy knowledge the answers must come from, and the store system that holds the order record.
Human review points: Refunds, exchanges, and any goodwill or exception; complaints and sensitive cases; any answer below a confidence threshold; and any reply that changes an order, payment, or delivery.
A good first pilot could be: order-status and delivery questions only, where the answer comes from a system record and approved wording.
See it in outline: the customer support demo — an illustrative demo with synthetic data, not a client case study.
Possible KPIs to measure: first response time, resolution time, escalation rate, share of replies sent from approved knowledge, and repeat-contact rate — measured against the agreed baseline (see above).
First focus
Back-office operations
The result to aim for: documents and requests move without re-typing, approvals stop sitting in inboxes, and the backlog stops blocking month-end.
Who this covers: small and mid-sized firms with heavy back-office work — for example, logistics and distribution, facilities and field services, wholesalers, and service firms processing invoices, forms, tickets, and internal requests every day.
Typical problem: Invoices, forms, tickets, and internal requests are processed by hand. Staff re-type the same data into several systems, approvals wait in inboxes, errors are found late, and nobody has a single view of what is waiting, approved, or posted.
The business cost is rework and delay: errors found late, approvals stuck in inboxes, and month-end work that cannot start until the backlog clears.
How automation could work: A document or request could be received once, classified, and its key fields extracted and checked against existing records (for example, supplier, amount, or reference). Exceptions — missing fields, mismatches, or amounts above an agreed threshold — could be routed to a person with the original document attached. Approved data could then be posted to the operating or finance system, and the requester could receive a status update automatically. Internal requests (leave, expenses, purchases, maintenance) could follow the same pattern: intake, validation, approval routing, and a logged outcome.
Systems to integrate: Document storage, finance/ERP, email, task and approval tools, and scheduling or dispatch tools where already in use.
What we would need from you: one document type, its approval threshold, and the finance or operating system it posts to.
Human review points: Extraction validation before any posting; all approvals, especially above agreed thresholds; any posting to a finance system; and any exception or mismatch, which must be resolved by a person rather than auto-corrected.
A good first pilot could be: one document type (for example, supplier invoices from one source) with a defined approval threshold and an audit trail.
See it in outline: the document automation demo and the purchase-request approval demo — illustrative demos with synthetic data, not client case studies.
Possible KPIs to measure: minutes per document, error rate, cycle time from receipt to approval, share of documents processed without rework, and overdue-request rate — measured against the agreed baseline (see above).
Back-office audience
HR admin
The result to aim for: new starters arrive to paperwork already complete, leave and rota requests answered from one trusted record, and HR files complete and findable — without automating any decision about a person.
Who this covers: HR and people-operations teams in small and mid-sized firms handling the admin around employment: onboarding paperwork, employee documents, leave and rota administration, and HR record hygiene. This is admin work only. Nothing here touches a hiring decision, screening, shortlisting, or selection step — those stay entirely with people, and hiring automation is not a first-phase offer (see Regulated work (future only)).
Typical problem: Onboarding packs are assembled by hand from old files, documents scatter across email and shared drives, leave and rota requests are chased in inboxes, and HR records end up incomplete or duplicated — so a simple question ("is this file complete?", "who approved this leave?") takes a search across three places to answer.
The business cost is delay and doubt: new starters waiting on paperwork, managers chasing approvals, and records nobody fully trusts when a question comes up.
How automation could work: An onboarding checklist could be generated from an approved template when a start date is confirmed, with each document requested once, checked for completeness, and filed against the employee record. A leave or rota request could be captured once, checked against the rules already recorded in the HR system (for example, remaining balance), and routed to the named manager for approval — the system prepares, the manager decides. Duplicate or outdated HR records could be flagged for a person to resolve, never merged automatically. Approved documents could be filed in the personnel file with a clear audit trail, so the next person can see the full history.
Systems to integrate: HR system (HRIS), document storage, email, and scheduling or rota tools where already in use.
What we would need from you: the approved onboarding checklist and document list, the leave and rota rules as recorded in your HR system, and the HR system that holds the record.
Human review points: every leave and rota approval; any change to an employee record; every document filed to a personnel file; and any exception, mismatch, or duplicate, which must be resolved by a person rather than auto-corrected. No hiring decision, screening outcome, or selection step is automated at any point.
A good first pilot could be: one admin workflow — for example, onboarding paperwork for one role type, or leave-request handling only — with a named owner and a weekly review of what was filed, approved, and flagged.
See it in outline: the document automation demo and the purchase-request approval demo — illustrative demos with synthetic data, not client case studies.
Possible KPIs to measure: onboarding file completeness, leave-request cycle time, share of HR records complete and deduplicated, and overdue-request rate — measured against the agreed baseline (see above).
Also relevant across sectors
These two workflows cut across the sectors above; the same pilot discipline and human review points apply.
Sales-driven businesses
The result to aim for: no lead left in an inbox, every follow-up on time, and a pipeline managers can actually trust.
Who this covers: businesses where revenue depends on timely follow-up — for example, agencies, distributors, training providers, and B2B sellers with a pipeline of enquiries, quotes, and renewals.
Typical problem: Leads go cold while reps re-type data and chase follow-ups. Enquiries sit in inboxes, the same lead is entered twice or not at all, follow-up timing varies by rep, and managers cannot see which leads were actually contacted or what was promised.
How automation could work: A lead could be captured from each approved source, checked for duplicates, and entered in the CRM with source, timing, and next action. A lead could be scored against agreed criteria (for example, fit, urgency, and completeness of information) and routed to the right owner. A draft follow-up could be prepared from approved messaging and the lead's stated need, queued for the rep to edit and approve. Activity — calls, emails, and meetings — could be logged automatically, and overdue follow-ups could be flagged for a person to act on, rather than silently dropped.
Systems to integrate: CRM, email, messaging, calendar, web forms, and quoting tools where already in use.
Human review points: Lead-scoring overrides and any decision to disqualify a lead; draft messages before they are sent; proposal terms, pricing, and promises; and any change to pipeline stage or ownership.
A good first pilot could be: one lead source and one follow-up sequence, with a named owner and a weekly review of approved, edited, and rejected drafts.
See it in outline: the sales follow-up demo — an illustrative demo with synthetic data, not a client case study.
Possible KPIs to measure: response time, follow-up completion rate, pipeline data completeness, share of leads contacted within the agreed window, and time from enquiry to first substantive reply — measured against the agreed baseline (see above).
Customer support teams
The result to aim for: consistent answers, urgent cases surfaced first, and agents spending their time where judgement matters.
Who this covers: in-house and outsourced support teams handling tickets by email, chat, or phone, where volume, consistency, and urgency are the daily pressures.
Typical problem: Ticket volume grows, answers vary, and urgent cases are missed. Agents answer the same questions differently, knowledge lives in inboxes and personal notes, and a complaint or outage can sit in a queue behind routine requests because triage depends on who reads it first.
How automation could work: An incoming ticket could be classified by topic, urgency, and sentiment, then routed to the right queue with a suggested priority. A draft reply could be prepared from approved knowledge only, with its sources shown to the agent. A confidence and risk check could decide the path: high-confidence routine drafts go to an agent for a quick check and send; low-confidence, complaint, or policy-sensitive cases go straight to a person. Repeated issues could be grouped so the team can see patterns and update the approved knowledge, rather than answering the same ticket one by one.
Systems to integrate: Helpdesk, knowledge base, CRM, internal notes and status pages where already in use.
Human review points: Low-confidence drafts; complaints, cancellations, and vulnerable-customer cases; any reply involving refunds, account changes, or policy exceptions; and every update to the approved knowledge base.
A good first pilot could be: the most repeated routine topic, with quality review of every draft in the first weeks before any wider rollout.
See it in outline: the customer support demo — an illustrative demo with synthetic data, not a client case study.
Possible KPIs to measure: first response time, resolution time, containment with quality review (that is, cases resolved with an approved draft, counted only where quality was checked), escalation rate, and customer-satisfaction scores where already collected — measured against the agreed baseline (see above).
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Future only — healthcare-adjacent and other high-compliance operations
Important scope note: This section is not a first-phase offer. It is included only so visitors in regulated or high-compliance settings can see the boundary clearly. High-risk automation — including hiring decisions, credit decisions, clinical decisions, and government systems — is not a first-phase offer, because buying cycles are longer, risks are higher, and compliance capability must be established first. No patient data, clinical advice, diagnosis, hiring outcome, or credit decision is handled by any workflow described on this page.
What could be considered later, only after a compliance review: non-clinical, operations-adjacent work in a healthcare-adjacent organisation — for example, internal facilities and maintenance requests, non-clinical staff scheduling support, or internal document handling where no patient-identifiable information is involved. Even then, scope, data handling, retention, access controls, and human approval would need to be agreed in writing, with a named compliance owner on the client side, before any pilot. What would still be excluded without a separate, documented capability: anything that decides, recommends, or records a clinical, employment, or financial outcome for an individual; anything that processes sensitive health, employment, or financial data; and anything that replaces a regulated human decision. If a request falls in that category, the honest answer at this stage is to scope it separately or decline it, not to fit it into a standard automation pilot.
Human review points (future only): every data-handling decision, every approval step, and every exception — with an audit trail. No automated step would post, decide, or notify on a sensitive record without a person.