Voice agent
6,814 calls, 59% no transfer
One anonymised 90-day dataset: routine calls handled, tickets created, complex cases passed to a person with context.
See the voice benchmarkManchester operations desk
Manchester workflow automation for admin-heavy teams
We put AI where it survives real operations: missed calls, quotes, supplier paperwork, support inboxes and slow reporting. Every workflow keeps a human review point, a named owner and a number on the ledger.
Job sheet
Send one messy workflow. You get a reply within 1 working day and a fixed-scope audit (£350–£750) before any build is discussed.
Recent build proofs
Real systems, real volume, no inflated numbers
A few patterns we can back with operational evidence, not promises.
Support desk
24,166 tickets mapped
We catalogue volume by channel, theme and team before automating, so effort lands where the pressure actually is.
See the support benchmarkAudio to actions
266 recordings reviewed
Calls and meetings turned into transcripts, summaries and owner-tagged actions. Recordings stay private.
See the transcription proofControls architecture
811 journaled records
A high-stakes automation lab built controls-first: limits, dry-run, journaling and review. Engineering proof, not trading advice.
See the control architectureFirst commercial step
Start with one workflow before committing to a build
The strongest first move is a focused AI Workflow Audit: one admin-heavy process, real examples, data/risk checks, and a clear route to process fix, MVP build or no-AI-yet.
Workflow audit
£350–£750
Map one workflow, shortlist opportunities and decide whether AI is worth it.
MVP automation build
£2k–£5k
Build one controlled workflow with human review, logging and handover.
Managed automation support
£500–£1.5k/mo
Maintain prompts, rules, APIs, reporting and small workflow changes after launch.
What we actually build
One workflow, from messy input to reviewed output
Messages, documents, calls and data move into a queue; AI drafts the useful bits; humans approve the sensitive decisions; reporting shows what changed.
Inputs
Calls, PDFs, inboxes, tickets, spreadsheets, product catalogues and system exports.
AI layer
Extraction, summarisation, classification, recommendations, semantic search and guardrails.
Operational output
Review queues, dashboards, notifications, handover notes and measurable process changes.
Common starting points
Clear routes for common AI automation problems
Use these pages when a buyer knows the problem: missed calls, manual documents, support queues, SME automation or a first workflow audit.
AI Automation Agency Manchester
Local AI automation for Manchester and North West SMEs: enquiry handling, document processing, CRM updates, reporting and human-reviewed workflows.
Open pageAI Workflow Audit
A focused AI workflow audit that maps one admin-heavy process, identifies automation opportunities, and produces a safe MVP plan.
Open pageAI Receptionist Manchester
AI call, enquiry and missed-lead response workflows for Manchester service businesses, with human handoff and CRM/ticket capture.
Open pageDocument Workflow Automation
Extract, classify and route business documents with review queues, audit trails and fewer manual copy-paste steps.
Open pageSupport Desk AI Automation
AI support-desk workflows for ticket summaries, classification, response drafts, knowledge-base search and escalation packs.
Open pageAI Automation for SMEs
AI automation for SMEs: small controlled pilots around admin, documents, enquiries, reporting and internal knowledge.
Open pageServices
From audit to maintained automation
Start small, prove usefulness with real examples, then scale the workflows that survive real use.
AI Workflow Audit
Map one admin-heavy workflow, score the AI opportunity and leave with a safe MVP plan.
Open pageWorkflow automation
Remove repeat admin work across inboxes, documents, spreadsheets, CRMs and reporting flows.
Open pageDocument workflow automation
Extract, review and route invoices, forms and case files with human approval and audit trails.
Open pageAI receptionist and missed leads
Capture routine calls, qualify enquiries, create tickets and hand off complex cases with context.
Open pageSupport desk AI automation
Summarise tickets, classify problems, draft safe replies and surface repeated issues.
Open pageAI governance
Set safe use rules, approvals, logging and controls so AI work can survive scrutiny.
Open pageImplementation patterns
Use cases grounded in real AI workflow patterns
The use-case library explains voice agents, support-ticket intelligence, RAG product advice, policy assistants, transcription and deploy/reporting automation without fake client names or made-up ROI.
Voice agent handoff
Call handling, ticket creation, reference-number whisper and human transfer based on real voice-agent builds.
Build pattern: Twilio · Realtime AI · Whisper · tickets
Support-ticket intelligence
Zendesk-style ticket summaries, categorisation, lost-context recovery and knowledge-base operations.
Build pattern: OpenAI · classification · summaries · dashboards
Product and kit advisor
RAG/embedding product advice for customers choosing technical kits, quotes or compatible options.
Build pattern: pgvector · semantic search · quote flow
Policy and market assistant
Dense guidance turned into practical question-led decision support with source boundaries.
Build pattern: GPT · embeddings · guidance · matching
Audio-to-actions pipeline
Audio, calls and meetings converted into structured notes, actions and review queues.
Build pattern: Whisper · VAD · summaries · handoff
SEO and deploy ops automation
Crawls, GSC signals, Netlify/static deploy QA and client-facing reports turned into repeatable systems.
Build pattern: crawlers · GSC · Netlify · reporting
Proof style
Examples based on build patterns, not invented testimonials
Generalised public examples are safer and more credible than fake logos, fake team photos or guaranteed savings claims.
Real build pattern, generalisedKnowledge-base and training assistant
Turn policy, product or support knowledge into a searchable assistant that helps staff find the right answer without guessing.
Real build pattern, generalisedInvoice and document control workflow
Extract, summarise and route invoices, finance messages and review queues with human approval and audit trails.
Real build pattern, generalisedCall, note and appointment triage
Convert calls, messages and notes into structured summaries, follow-up tasks and safer handovers for operational teams.
Real build pattern, generalisedVoice agent and handoff automation
Answer routine calls, create tickets, announce reference numbers and transfer complex cases to humans with context.
Real build pattern, generalisedOperations reporting and exception alerts
Spot repeated faults, delayed jobs and backlog pressure earlier through dashboards, crawls and structured reports.
Real build pattern, generalisedSupport desk intelligence
Summarise tickets, classify problems, recover lost context and prepare next actions for support and admin teams.
Real build pattern, generalisedEnquiry and portfolio triage
Route property enquiries, maintenance notes and portfolio data into useful queues, summaries and reporting views.
Real build pattern, generalisedProduct advisor and recommendation engine
Use product data, embeddings and conversational logic to help customers choose the right kit or package faster.
Industries
Built around operational context
Each industry page explains practical starting points, useful workflows and safer implementation boundaries.
Smart Manufacturing
Practical AI, data and automation workflows for smart manufacturing teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageIndustrial Automation
Practical AI, data and automation workflows for industrial automation teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageQuality Control
Practical AI, data and automation workflows for quality control teams: triage, forecasting, reporting, document handling and operational visibility.
Open pagePredictive Maintenance
Practical AI, data and automation workflows for predictive maintenance teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageSupply Chain Optimization
Practical AI, data and automation workflows for supply chain optimization teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageBanking & Finance
Practical AI, data and automation workflows for banking & finance teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageInsurance
Practical AI, data and automation workflows for insurance teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageInvestment Management
Practical AI, data and automation workflows for investment management teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageRisk Assessment
Practical AI, data and automation workflows for risk assessment teams: triage, forecasting, reporting, document handling and operational visibility.
Open pageHow we work
Make AI boring enough to trust
A serious AI project needs a workflow owner, clean enough data, a clear failure mode, user handover, monitoring and a rollback route. That is the difference between a demo and a system the business can rely on.
Next step
Ready to map one workflow before building?
Send one workflow, data source or operational bottleneck. We will help decide whether it needs AI, a simpler automation, better reporting, or no-AI-yet process cleanup.