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Case Studies

See the problem, what we changed, and how the day-to-day work improved. Client details stay private where they should.

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Invoice matching — anonymised client

Automating Purchase Invoice Matching

75% staff time saved and approximately £80,000 annual saving

The Challenge

Hundreds of invoices arrived each week. Four people spent much of their time matching them against purchase records and chasing the exceptions.

Our Solution

A workflow that completes routine matches automatically and sends uncertain or incorrect items to the team

Results Achieved

  • 75% of staff time saved
  • 65% reduction in manual processing within 90 days
  • Approximately £80,000 annual saving
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Named client — Jason Tanner, The Menu Partners

The Menu Partners: Lead Research & Qualification

A 37% improvement in conversion

The Challenge

Every new enquiry triggered hours of manual research, so prospects waited and each salesperson started the conversation with a different level of context.

Our Solution

A research workflow that prepares the same useful briefing for every lead and delivers it to the sales team before follow-up

Results Achieved

  • Conversion improved by 37% after the workflow gave the sales team more consistent prospect context
  • Client name and conversion reference published with permission
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Retail — anonymised client

AI-Powered Inventory Optimisation

A qualitative, anonymised implementation account

The Challenge

The team was tying up cash in slow-moving stock while popular products still went unavailable across several locations.

Our Solution

A demand-planning workflow that uses sales history and seasonal patterns to support earlier, more consistent ordering decisions

Results Achieved

  • Earlier, more consistent ordering decisions
  • Less dependence on individual judgement and spreadsheet reconciliation
  • Client identity and unapproved numerical claims are not published
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Professional Services — Named Capability Study

SIMARA AI: Building an AI-Native Consultancy

How a small consultancy keeps context moving without automating away judgement

The Challenge

Client work, research, proposals, and delivery all depended on context that was too easy to lose between tools and conversations.

Our Solution

A connected way of working where AI prepares repeatable work and people remain responsible for priorities, claims, approvals, and client relationships.

Results Achieved

  • More consistent client experience across the engagement lifecycle
  • Business knowledge captured as reusable operating intelligence
  • Repeatable work supported without weakening expert oversight
  • Clear review and verification built into AI-enabled workflows
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Consumer AI — Named Product Study

Vedara: Building a Trustworthy Consumer AI Platform

Complex, calculation-dependent expertise made personal, useful and dependable

The Challenge

The product had to feel simple to a first-time user without hiding errors that an expert would spot immediately.

Our Solution

Verified calculation produces the facts; AI explains them in plain language; product checks keep the two aligned.

Results Achieved

  • Complex expertise translated into approachable personal experiences
  • A trusted source of truth separated from language generation
  • Quality checks integrated into the product lifecycle
  • Shared foundations supporting coherent growth across journeys
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Frequently Asked Questions

Are these case study results typical?▾
No two workflows produce the same result. Volume, exception rates, data quality, and the existing process all matter. We use the figures here to explain what changed in that project, then estimate your opportunity from your own numbers.
Why are some client names or implementation details not shown?▾
Some clients are happy for us to share the outcome but not their name, data, or internal setup. We respect that. Each study includes enough to understand the problem and our approach without turning a client relationship into public documentation.
How long did these projects take to deliver?▾
We usually begin with one workflow. A small, well-understood process moves faster than a broad programme, but the real timeline depends on system access, data quality, and how many exceptions the team handles today.
Do you replace our existing systems?▾
No. Every project here automated around the systems the client already had — Power Automate and SQL Server in the invoice case, the existing CRM and Slack in the lead research case. Rip-and-replace is slow, expensive, and rarely the reason a workflow is painful.
What size of business are these projects suited to?▾
UK SMEs, typically 10–250 staff, where a specific manual process has become a bottleneck. If the process is small or genuinely infrequent, we will tell you that automation is not worth it.

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