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Solutions

Eight solutions across four outcome clusters. Each one names a specific operational problem, a measurable result, and the limits that come with it.

Customer Operations

AI Customer Support and Conversational Systems

Who it is for

Teams answering the same questions every day, where knowledge is scattered across documents and triage is slow.

How it works

  1. A question comes in
  2. A grounded draft is prepared, citing its source
  3. A person reviews it before anything is sent

Operational outcome

Faster first response on supported requests, with a clear line between what the system answers and what a person handles.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

One support channel connected to an approved set of documents, answering with sources, escalating on low confidence or account-specific questions.

Example technologies

Anthropic Clauden8nSupabase

What we measure

  • First-response time
  • Assisted resolution rate
  • Escalation accuracy
  • Unsupported-answer rate

Typical pilot scope

One web or email support channel, one approved set of documents, human review for anything that changes an account.

Limits and dependencies

  • WhatsApp availability depends on Meta approval, client account eligibility and the messaging rules for the use case.
  • Account-specific answers require a verified source. Missing or uncertain evidence is escalated for human review.

Lead and CRM Automation

Who it is for

Businesses whose leads arrive through forms and inboxes but whose CRM records stay incomplete, duplicated or slow to reach the right person.

How it works

  1. A lead comes in from the form or inbox
  2. It is validated, checked for duplicates and routed
  3. A CRM record is created or matched, and an acknowledgement is drafted

Operational outcome

Leads captured consistently from one source, routed by clear business rules, with CRM records that are more complete and easier to trust.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

One lead intake connected to CRM systems through supported APIs and webhooks: deduplicating, enriching from permitted sources only, and routing by rules your team defines.

Example technologies

n8nREST APIs and webhooksCRM systems through supported APIs and webhooksBrevo

What we measure

  • Time to first action
  • Unassigned lead rate
  • Duplicate record rate
  • Required-field completeness
  • Qualified meeting rate, tracked only where attribution is credible

Typical pilot scope

One lead source captured and normalised, deduplicated, enriched only from permitted sources, then created or updated in the CRM and routed by agreed rules.

Limits and dependencies

  • This solution improves lead handling and CRM data quality. It makes no promise of increased revenue, sales or conversion.
  • Enrichment and outreach respect marketing consent, privacy rules and the terms of the platforms involved.

Operations

Operations and Back-Office Automation

Who it is for

Teams losing hours to repeated data entry, status chasing across systems, and manual handoffs between departments.

How it works

  1. An invoice arrives
  2. Its fields are compared against the purchase order
  3. A person reviews and approves any mismatch before accounting is updated

Operational outcome

A single, clearly defined flow runs on agreed rules instead of manual repetition, with uncertain cases routed to a person rather than guessed at.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

One deterministic workflow across the tools involved, using AI only to interpret unstructured content, with an exception queue for anything uncertain or invalid.

Example technologies

n8nMakeREST APIs and webhooksGoogle Sheets

What we measure

  • Cycle time
  • Manual touches per case
  • Error and rework rate
  • Throughput
  • Time to clear a pending exception

Typical pilot scope

A single, clearly defined flow with agreed deterministic rules, AI applied only where unstructured content needs interpreting, and an exception queue for a person to review.

Limits and dependencies

  • Deterministic rules must be agreed with your team before automation starts. Cases outside those rules stay in the exception queue for a person to decide.

E-commerce Operations Automation

Who it is for

Online sellers handling orders or stock across more than one channel, with manual shipment updates and repeated order exceptions.

How it works

  1. An order exception is detected
  2. A customer message or internal task is drafted
  3. A person approves any refund, cancellation or price change before it happens

Operational outcome

Order and stock updates move automatically between channels, exceptions reach a person quickly, and the workflow is built to reduce overselling risk.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

One order-to-notification or exception-handling path, with idempotent actions to prevent duplicate side effects and human approval on anything sensitive.

Example technologies

n8nREST APIs and webhooksGoogle SheetsTelegram

What we measure

  • Exception handling time
  • Duplicate action rate
  • Order status accuracy
  • Support contacts per order
  • Manual steps per order

Typical pilot scope

One order-to-notification or exception-handling path, human approval required for refunds, cancellations, price changes and exceptional orders, and idempotency built in to prevent duplicate actions.

Limits and dependencies

  • This solution is designed to reduce overselling risk across channels. It does not claim to eliminate it entirely.
  • Channel and marketplace API limits, and outages at those providers, are dependencies outside this workflow's control.

Knowledge and Research

Data, Research and Decision Support

Who it is for

Teams that need scheduled monitoring of approved sources or repetitive research and synthesis work, without a current consolidated view to work from.

How it works

  1. A permitted source is checked on schedule
  2. A real change is detected against the previous version
  3. An analyst reviews the summary before it is used

Operational outcome

A defined source allowlist is checked on a stated schedule, with citations and change detection, so decisions are reviewed by a person before they are acted on.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

Collection limited to an approved source allowlist, respecting each source's access terms and applicable law, followed by normalisation, citation and change detection.

Example technologies

PythonPlaywrightn8nAnthropic Claude

What we measure

  • Source coverage
  • Freshness against the stated schedule
  • Citation completeness
  • False-positive rate
  • Analyst time per report

Typical pilot scope

One source allowlist, collection that respects source access terms and applicable law, normalisation with citations and change detection, and human review before any decision or publication.

Limits and dependencies

  • Collection is limited to sources that permit it, respects each source's access terms, and does not bypass access controls. This does not constitute a blanket legal clearance for scraping any source.
  • Data is refreshed on the agreed schedule, not continuously, and is described only by that schedule.

Internal Knowledge and Document AI

Who it is for

Organisations where staff repeatedly search the same internal documents, and where different teams should see different material.

How it works

  1. The same question is asked by two different roles
  2. Each request is checked against that person's access
  3. One gets a cited answer, the other is declined and logged

Operational outcome

Staff find sourced answers inside existing permission boundaries, and the system declines to answer rather than guessing when it has no document to point to.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

Access-controlled document collection with permission-aware retrieval, source citations, schema-validated extraction, and approval before any downstream change.

Example technologies

Anthropic ClaudeSupabaseGoogle DrivePython

What we measure

  • Retrieval relevance on an agreed test set
  • Citation correctness
  • Rate of declining to answer without a source
  • Extraction accuracy
  • Time to locate an answer

Typical pilot scope

One access-controlled document set with permission-aware retrieval, source citations, declining to answer when there is no source, schema-validated extraction, and approval for any downstream change.

Limits and dependencies

  • Retrieval respects existing document permissions; no user sees material they were not already entitled to see.
  • The system declines to answer when it cannot point to a source document, and passes the question to a person.

Digital Systems

Custom Integrations, Dashboards and AI Systems

Who it is for

Businesses whose tools do not share a reliable source of truth, where management reporting is stitched together by hand.

How it works

  1. Other modules' run and event records feed in
  2. They are mapped into one shared, freshness-aware view
  3. An authorised operator can drill in and request a controlled re-run

Operational outcome

One integration boundary or dashboard gives a clearer, better-owned view of the data, with quality and failure indicators visible rather than hidden.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

A defined integration or dashboard with agreed source ownership and refresh interval, surfacing data quality and failure state instead of hiding it.

Example technologies

REST APIs and webhooksSupabasePostgreSQLGoogle Sheets

What we measure

  • Refresh success rate
  • Data discrepancy rate
  • Report preparation time
  • Integration error and recovery time

Typical pilot scope

One integration boundary or one operational dashboard, with defined source ownership and refresh interval, and data quality and failure indicators visible throughout.

Limits and dependencies

  • A dashboard is only as accurate as the systems feeding it; source data quality remains each system owner's responsibility.
  • Refresh intervals are agreed with your team rather than continuous, and are stated explicitly in the scope.

Website Modernisation and Workflow Integration

Who it is for

Businesses with an outdated site, an unclear offer or weak lead capture, and no reliable link between the website and the CRM.

How it works

  1. A visitor completes a high-intent form
  2. The submission is validated the same way it is today
  3. It also creates or matches an owned CRM record, using the lead-crm adapter

Operational outcome

One high-intent journey is rebuilt with a clearer message, working analytics and a CRM handoff, with measured performance and accessibility improvements.

See it running →Illustrative scenario · not a client engagement
Pilot scope and technical detail

The system we build

One journey such as homepage to Fit Call, rebuilt with clear message, evidence, form and analytics, connected to CRM systems through supported APIs and webhooks with consent-aware tracking.

Example technologies

REST APIs and webhooksCRM systems through supported APIs and webhooksPlaywright

What we measure

  • Qualified Fit Call conversion
  • Form completion rate
  • Lead routing success
  • Core Web Vitals field performance
  • Accessibility defect count

Typical pilot scope

One high-intent journey such as homepage to Fit Call, with a clearer message, evidence, form and analytics, performance and accessibility improvements, and a CRM handoff with consent-aware tracking.

Limits and dependencies

  • This solution makes no promise of a specific search ranking or conversion uplift.
  • Design, content, engineering, analytics and any third-party tooling costs are quoted separately from this scope.
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