Case studies
They asked for one thing. The problem was somewhere else.
Every engagement here started with a request we could have simply delivered. Measurable Outcomes is one of our three values, so each one carries the numbers it moved.
Clients are described rather than named. We publish a client’s name only with their written permission, which is enforced in the build rather than left to judgement.
A manufacturer running 10,000+ predictive asset alerts a month
The asset told them it was failing. Nobody was reading it.
Failures were found after the breakdown, then handled through manual work orders and technician assignment that took 48 hours to respond. Predictive insight plus automated dispatch cut machine failures 75% and halved response twice over.
- ServiceNow
- Salesforce
- Implementation
- Fewer machine failures
- 75%Fewer machine failures
- Service response time
- 12 hrsService response time
- Live assignment visibility
- 95%Live assignment visibility
A global data centre industry leader operating across the US and UK
Driving innovation for a global data centre leader
Transitioning from two separate service providers, we consolidated ServiceNow instances, standardised core ITSM processes and led phased legacy migrations across multiple geographies.
- ServiceNow
- Implementation
- Migration & Modernization
- Lower operational complexity
- 30%Lower operational complexity
- Higher system efficiency
- 25%Higher system efficiency
- More knowledge sharing
- 50%More knowledge sharing
A life sciences organisation screening 10,000+ Phase II trial participants
A week to find out whether someone was eligible
Phase II screening ran 5–7 days across manual prescreening, consent and eligibility steps, with 30–40% of enrolments delayed and assessments varying between coordinators. Digitising the journey on Life Sciences Cloud made the screening cycle three times faster.
- Salesforce
- Implementation
- Faster screening cycle
- 3×Faster screening cycle
- Less manual effort
- 60%Less manual effort
- Higher enrolment efficiency
- 25%Higher enrolment efficiency
A global energy management company
Revamping IT service management for a global energy leader
Fragmented IT systems, manual workflows and slow incident resolution were dragging on productivity. Consolidating onto ServiceNow ITSM cut response times and gave employees visibility they never had.
- ServiceNow
- Implementation
- Lower response times
- 30%Lower response times
A supplier of electronic consumer products shipping 350+ orders a month
The quote took three days. The customer wanted to know where the van was.
Quotes were built in spreadsheets over 2–3 days, and once an order was placed the customer had no view of it at all. CPQ took the quote-to-order cycle to 90 minutes, and a tracking experience closed the gap between order and doorstep.
- Salesforce
- Implementation
- Quote-to-order cycle
- 1.5 hrsQuote-to-order cycle
- Fewer costly errors
- 40%Fewer costly errors
- Higher customer satisfaction
- 35%Higher customer satisfaction
A professional-services firm, a mid-sized legal and advisory practice
They rolled out Copilot to save time. It kept guessing wrong.
A professional-services firm asked us to roll out Microsoft 365 Copilot. Speed was never the issue; knowledge was scattered and ungoverned, so Copilot grounded on the wrong sources and answered confidently from them.
- Microsoft
- Digital Transformation
- Reclaimed per person, per week
- 5 hrsReclaimed per person, per week
- Faster proposals and RFPs
- 30%Faster proposals and RFPs
- Less time hunting for documents
- 50%Less time hunting for documents
A law firm handling 2,000+ cases a year
Billable work waited on unbillable work
Client onboarding and case setup ran 3–5 days across manual workflows, with roughly 40% of cases delayed and billing errors following the manual handling. A connected Salesforce practice (portal, e-signature and automated billing) cut onboarding by 80%.
- Salesforce
- Implementation
- Less case onboarding time
- 80%Less case onboarding time
- Fewer manual tasks
- 70%Fewer manual tasks
- Improvement in client engagement
- 50%Improvement in client engagement
A tier-2 automotive-components supplier feeding just-in-time under IATF 16949
The parts passed inspection. The defects shipped anyway.
A tier-2 automotive supplier asked us to turn paper inspection sheets into an app. The clipboards weren't the cost; the latency was, and suspect parts left the dock before quality knew.
- Microsoft
- Digital Transformation
- Less manual data entry
- 85%Less manual data entry
- Faster quality containment
- 40 minFaster quality containment
- Lower customer PPM
- 25%Lower customer PPM
A North American consumer-goods manufacturer with six plants and two distribution centres
The information existed. The floor couldn't reach it.
Eight in ten of a consumer-goods manufacturer's people worked a plant floor, a dock or a night shift, and the intranet had been built for head office. Rebuilt for a phone in a pocket, it now reaches the line in the same shift.
- Microsoft
- Digital Transformation
- Of frontline staff use it monthly
- 74%Of frontline staff use it monthly
- To find the current SOP
- 90 secTo find the current SOP
- Read safety bulletins within 24 hours
- 91%Read safety bulletins within 24 hours
An illustrative engagement: a mid-sized manufacturer, with figures representative of comparable Fabric and Power BI deployments
By the time the report landed, the month was already lost.
A manufacturer asked for real-time production and supply-chain dashboards. The gap was never the dashboard: every decision waited for month-end, so by the time a report showed downtime creeping or a supplier slipping, the loss was already banked.
- Microsoft
- Data Analytics
- Faster reporting cycle
- ~90%Faster reporting cycle
- Less unplanned downtime
- ~15%Less unplanned downtime
- Decisions, not monthly
- DailyDecisions, not monthly
An automotive network routing 5,000+ leads a month across 50+ dealer territories
The lead was worth having for two hours. It was routed in five days.
Leads arriving across 50+ dealer territories were distributed by hand, so response could take five days on a lead whose value decays in hours. Territory-based routing, scoring and SLA escalation brought response inside a two-hour target.
- Salesforce
- Implementation
- Lead response target
- 2 hrsLead response target
- Improvement in lead response time
- 60%Improvement in lead response time
- Higher lead-to-opportunity conversion
- 30%Higher lead-to-opportunity conversion
A distributor moving 2,400+ SKUs daily across 340 retail accounts
The shelves were empty. The warehouse was full.
A distributor asked for a better demand-forecasting model. A smarter forecast still died in a two-day manual gap. The issue wasn't prediction, it was that nobody owned the decision.
- Microsoft
- Data Science
- AI & Machine Learning
- Fewer stockouts
- 40%Fewer stockouts
- Less working capital tied up
- 28%Less working capital tied up
- Faster order fulfilment
- 30%Faster order fulfilment
An educational institution processing 5,000+ admissions a year
The offer was decided in minutes. The paperwork took a week and a half.
Admissions ran 7–10 days across disconnected systems, with about 30% of applications delayed and document verification done by hand. A connected Salesforce workflow, with Slack for coordination and Synapse for analysis, cut the cycle to days.
- Microsoft
- Salesforce
- Implementation
- Faster document verification
- 50%Faster document verification
- Less enrolment processing time
- 40%Less enrolment processing time
- Less paperwork handling time
- 60%Less paperwork handling time
A mid-market omnichannel retailer: around 140 stores, an online channel and a wholesale desk
Sales were up. Margin was leaking.
An omnichannel retailer asked for one view of the customer. A 360° view doesn't stop margin walking out the door: the money was leaking through discounts and promotions nobody could see across channels.
- Salesforce
- Implementation
- Gross margin recovered
- 2.4 ptsGross margin recovered
- Fewer off-policy discounts
- 65%Fewer off-policy discounts
- Less trade-account leakage
- 30%Less trade-account leakage
A cement manufacturer onboarding 3,000+ dealers a year
Onboarding a dealer took a fortnight. The order took days more.
Dealer onboarding ran on manual data entry and sequential approvals, stretching to 10–15 days while inquiries went unanswered for over a day. A connected Salesforce lifecycle (onboarding, cases, loyalty and a dealer-facing portal) halved the onboarding time.
- ServiceNow
- Salesforce
- Implementation
- Less dealer onboarding time
- 50%Less dealer onboarding time
- Less order processing time
- 40%Less order processing time
- Higher dealer engagement and annual sales
- 25%Higher dealer engagement and annual sales
A short-term insurer writing personal auto and household cover
Every claim moved at the speed of the hardest one
A short-term insurer asked for a faster claims system. Data entry was never the delay: simple, low-risk claims sat in the same queue as complex ones, waiting on manual triage.
- Salesforce
- Implementation
- Claims straight through
- 25%Claims straight through
- Faster claim turnaround
- 45%Faster claim turnaround
- Lower cost per claim
- 30%Lower cost per claim
A data-centre operator managing 1,000+ tenants a year
Every tenant handoff crossed a system boundary. Each one cost a day.
We slashed tenant processing time by 50% by consolidating our siloed tech stack into Salesforce. We integrated Oracle CPQ and Azure Synapse to bridge the gap between our 24-hour lead-to-quote cycle, billing, case resolution, and reporting.
- Microsoft
- Oracle
- Salesforce
- Implementation
- Data Analytics
- Faster tenant processing
- 50%Faster tenant processing
- Less manual processing time
- 60%Less manual processing time
- Lower administrative cost
- 40%Lower administrative cost
A financial-services firm, a retail bank with a heavily regulated IT estate
Every incident had a ticket. The same ones kept coming back.
A retail bank asked for ServiceNow so IT incidents were finally logged and tracked. Tracking was never the gap; nothing tied recurring incidents to a root cause, so the same outages returned.
- ServiceNow
- Implementation
- Fewer repeat incidents
- 35%Fewer repeat incidents
- Lower mean time to resolution
- 40%Lower mean time to resolution
- Routine tickets deflected
- 30%Routine tickets deflected
A vendor running 2,000+ partner deal registrations a year
The deal waited a week for approval. The SLA did not wait at all.
Partner deals took 5–7 days to approve through manual, disconnected workflows, and about 30% of cases breached SLA because nobody could see a deadline coming. A connected Salesforce experience made both the approval and the clock visible.
- Salesforce
- Implementation
- Faster deal approvals
- 70%Faster deal approvals
- Fewer SLA breaches
- 40%Fewer SLA breaches
- Less manual handling time
- 30%Less manual handling time
A private hospital group
They asked for paperless. The patients were still waiting.
A private hospital group asked us to digitise patient intake. Paperwork was never the bottleneck: patients waited because nobody could see bed and clinician capacity in real time.
- Microsoft
- Digital Transformation
- Less time waiting for a bed
- 40%Less time waiting for a bed
- Lower average length of stay
- 0.5dLower average length of stay
- Less admin time on admissions
- 70%Less admin time on admissions
A manufacturer handling 12,000+ warranty registrations a month
The warranty was in the box. Registering it took a phone call.
Warranty registration and claims ran through the support desk, which is why roughly 40% of every post-sale conversation was about warranty admin. A QR code on the product now opens a self-service journey, and Salesforce runs the workflow behind it.
- Salesforce
- Implementation
- Fewer warranty-related support tickets
- 80%Fewer warranty-related support tickets
- Faster claim processing
- 3.5×Faster claim processing
- More warranty registrations
- 22%More warranty registrations
An illustrative engagement: a regional parcel carrier (280 vehicles across 6 depots, US Midwest)
Last-mile operations copilot
A regional parcel carrier was losing margin at the doorstep. An AI dispatch copilot, delivered as a service on the systems they already ran, lifted first-attempt delivery into best-in-class territory.
- Microsoft
- Data Analytics
- Data Science
- Generative & Agentic AI
- Fewer failed delivery attempts
- 62%Fewer failed delivery attempts
- Less return-to-origin volume
- 40%Less return-to-origin volume
- Payback on the engagement
- <7 moPayback on the engagement
Client details pending review: this engagement is not yet attributed
Republishing the report kept the numbers and broke everything pointing at them
Consolidating shared datasets meant downloading every PBIX, changing its source and republishing, which Power BI treats as a brand new report. Every ID changed and every shared link died. The Rebind endpoint moves the binding and leaves the report alone.
- Microsoft
- Data Analytics
- Per report, rebound via the API
- ~30sPer report, rebound via the API
- Reports repointed in under 30 minutes
- 50Reports repointed in under 30 minutes
- Report URLs broken
- 0Report URLs broken
Client details pending review: this engagement is not yet attributed
A question that should take ten seconds took two days
Business logic lived in semantic models, everything else in the warehouse, and users could query neither, so every question went through an analyst. A conversational layer now routes each one to the right engine, with access control enforced before anything executes.
- Microsoft
- Data Analytics
- Generative & Agentic AI
- Accuracy over 500+ test questions
- 85%Accuracy over 500+ test questions
- Correct semantic model selection
- 93.2%Correct semantic model selection
- Reduction in time to answer
- 90%Reduction in time to answer
Client details pending review; this engagement is not yet attributed
Every carrier invoice was checked by hand. Only the variances needed a person.
Network engineering was validating carrier invoices circuit by circuit against reference data, investigating every variance over $100 before approval. An extraction pipeline and a reconciliation workflow now surface only the discrepancies that need judgement.
- Microsoft
- AI Agents & Automation
- Generative & Agentic AI
- Field extraction accuracy
- 98%+Field extraction accuracy
- Of invoices clear without a human
- 90%Of invoices clear without a human
- Template dependency
- 0%Template dependency
Client details pending review: this engagement is not yet attributed
Retrieval was never the problem. Choosing where to retrieve from was.
Retrieval-augmented generation works well over one corpus and degrades sharply across several. Wrapping each source as a tool and letting an agent choose between them at runtime routes a query to the right system without a single hard-coded rule.
- Microsoft
- Generative & Agentic AI
- Heterogeneous source types unified
- 5Heterogeneous source types unified
- Hard-coded routing rules
- 0Hard-coded routing rules
- To add a new data source
- 1 toolTo add a new data source
Client details pending review; this engagement is not yet attributed
The answer was in the documentation. In two of them, in different places.
Anyone needing an answer that spanned an internal runbook and an official platform reference had to know both existed and read both. A multi-agent assistant now queries them together, with no vector store, no embedding pipeline and no re-indexing when the docs change.
- Generative & Agentic AI
- Vector databases required
- 0Vector databases required
- Sources merged per answer
- 2Sources merged per answer
- Re-indexing when the docs change
- 0Re-indexing when the docs change
Client details pending review: this engagement is not yet attributed
The job titles all matched. The intent was in the comments.
Reps were filtering a contact database by job title, then opening every profile by hand to work out who was worth a message. A prospecting engine now reads the comments under keyword-matched posts, scores buying intent in two AI rounds, and returns a per-person outreach brief.
- Generative & Agentic AI
- Confidence score on every lead
- 1–10Confidence score on every lead
- To a profile dossier
- ~2 minTo a profile dossier
- Sources on one scoring rail
- 5Sources on one scoring rail
Client details pending review: this engagement is not yet attributed
Every document said what it was. Nobody could read them all.
A mixed SharePoint estate held engineering drawings alongside leases, insurance certificates and vendor contracts, with no shared taxonomy. A classification service now reads each file the way a reviewer would and returns category, type, site and the evidence behind the call.
- Microsoft
- AI Agents & Automation
- Generative & Agentic AI
- Document categories classified
- 7Document categories classified
- AI calls per file, adaptively
- 2→1AI calls per file, adaptively
- Files classified concurrently
- 8Files classified concurrently
An illustrative engagement: a law firm switching on Microsoft 365 Copilot
Copilot read every document in seconds. Including the ones it shouldn't have.
They asked us to roll out Copilot so fee-earners could draft, summarize and review documents faster. Speed was the easy part: in a law firm the real question is whether this lawyer should see the document. The unlock wasn't the AI. It was the governance that made the AI safe to switch on.
- Microsoft
- Digital Transformation
- Saved per fee-earner, weekly
- ~4 hrsSaved per fee-earner, weekly
- First-draft turnaround
- Hours, not daysFirst-draft turnaround
- Matters governed
- 100%Matters governed
An illustrative engagement: a law firm assessing its Copilot readiness
The ethical wall was a policy everyone signed. And a permission no system enforced.
They asked us whether they were ready for Copilot, and to fix what wasn't. Readiness was never an AI question: the ethical walls lived in the handbook, not the file system, and documents had sprawled across drives for years. The estate wasn't just Copilot-unready; it was ungoverned.
- Microsoft
- Digital Transformation
- Access remediated
- Need-to-knowAccess remediated
- Governance posture
- Audit-readyGovernance posture
- Estate foundation
- Copilot-readyEstate foundation
An illustrative engagement: a law firm speeding up new-matter intake
The intake was quick. The conflict check was optional.
They asked us for a Power Apps intake with automated routing, conflict-check prompts and status tracking. It was never a typing problem: the slow, manual intake made the one step with real exposure (the conflict check) inconsistent and skippable. Faster intake isn't the goal. A conflict check that runs every time is.
- Microsoft
- Digital Transformation
- Faster intake
- 55%Faster intake
- Fewer errors
- Zero rekeyingFewer errors
- Faster time-to-matter
- Days to hoursFaster time-to-matter
A mid-size law firm in Boston, MA (210 fee earners across six practice groups)
Leaders couldn't see realization, utilization or matter profitability in real time
The books closed monthly and the numbers arrived nine days later. We rebuilt a law firm's reporting on Microsoft Fabric so partners see hours, realization and matter margin while the work is still live.
- Microsoft
- Data Analytics
- Overall realization
- +4.8 ptsOverall realization
- Write-offs
- ↓23%Write-offs
- Lockup (WIP + AR)
- ↓19 daysLockup (WIP + AR)
An illustrative engagement: a US litigation firm (340 lawyers across four offices)
Senior time went into finding the passage, not arguing it.
A 340-lawyer firm wanted AI agents for legal research and deposition summaries. The hard part was never the summarising; it was building something that could read across the firm's work without letting any lawyer see a matter they were not cleared for.
- Microsoft
- Generative & Agentic AI
- Deposition prep
- ↓58%Deposition prep
- Research hours
- ↓61%Research hours
- Analysis time
- 2×Analysis time
A mid-size 3PL in Louisville, KY (8 sites, 40+ carriers, ~9,000 shipments a week)
Real-time shipment, fleet & OTIF visibility
A US third-party logistics provider replaced overnight spreadsheets with a live control tower: TMS, WMS and telematics unified in Microsoft Fabric, surfaced through Power BI for OTIF, dwell and exceptions.
- Microsoft
- Data Analytics
- OTIF performance
- +6.4 ptsOTIF performance
- Exception detection lag
- 14 hrs → 15 minException detection lag
- Average dwell time
- ↓22%Average dwell time
A regional carrier in Kansas City, MO (320 drivers, ~11,000 deliveries a week)
Proof of delivery, off paper and into the app
A US logistics operator replaced paper PODs, emailed damage reports and untracked exceptions with a mobile Power Apps capture for drivers and Power Automate routing behind it.
- Microsoft
- Digital Transformation
- Admin time recovered
- 190 hrs/wkAdmin time recovered
- Exception resolution
- 6.5d → 1.4dException resolution
- PODs with audit trail
- 100%PODs with audit trail
A mid-size 3PL in Memphis, TN (12 distribution centers, ~40M order lines a year)
Migrating legacy TMS/WMS to the cloud
A Memphis-based third-party logistics provider re-platformed an ageing on-prem TMS and WMS onto Microsoft Azure: a phased, minimal-downtime migration built for scale and peak-season resilience.
- Microsoft
- Digital Transformation
- Migration & Modernization
- Infrastructure cost
- ↓20%Infrastructure cost
- Peak-season uptime
- 99.9%Peak-season uptime
- Elastic scale to peak
- 3×Elastic scale to peak
An illustrative engagement: a US logistics operator with drivers across several states
The route changed at six. The driver left at five.
They asked for a mobile intranet so drivers and warehouse teams could reach routes, SOPs, safety info and shift handover. It was never a content problem: logistics work happens away from any desk, so the current answer has to reach the person at the moment they need it.
- Microsoft
- Digital Transformation
- Frontline adoption
- ~85%Frontline adoption
- Faster info access
- ~65%Faster info access
- Engagement
- 2×Engagement
An illustrative engagement: a US logistics operator handling shipper and customer enquiries
The customer asked where their shipment was. So did the agent.
They asked for a customer-service hub with case management and a single view of accounts and shipments. The enquiries weren't scattered for lack of a ticketing tool: the answer lived across inboxes, the TMS and the order system, and the fix often sat with operations, not the service desk.
- ServiceNow
- Implementation
- Faster response time
- 55%Faster response time
- CSAT lift
- +12 ptsCSAT lift
- First-contact resolution
- 80%First-contact resolution
An illustrative engagement: a US logistics operator running a dozen hubs and depots
Twelve depots. Twelve different ways to run a ticket.
They asked for a system to handle incidents, assets and field requests across their hubs and depots, with SLAs and visibility. There was no ticketing gap at any one site: every hub ran its own process and its own definition of urgent, so nothing could be compared across the network.
- Microsoft
- Digital Transformation
- SLA attainment
- 95%SLA attainment
- Faster MTTR
- 45%Faster MTTR
- Less reporting effort
- 60%Less reporting effort
A mid-size retail chain in Columbus, OH (60 stores, ~40,000 SKUs a season)
Demand forecasting and markdown optimization with AI
A mid-size US retail chain replaced spreadsheet-and-instinct buying with forecasting models and Copilot-assisted Power BI dashboards on Microsoft Fabric, buying closer to true demand and marking down smarter.
- Microsoft
- Data Analytics
- Markdown loss avoided
- $3.4MMarkdown loss avoided
- Stockouts on core lines
- ↓24%Stockouts on core lines
- Forecast accuracy
- 78%Forecast accuracy
A mid-size retail chain in Minneapolis, MN (60 stores, ~4,200 store associates)
Store operations and employee service on ServiceNow
A mid-size US retail chain gave 4,200 store staff one place to raise IT, HR and facilities issues, with self-service answers and automated routing replacing phone calls and chased emails.
- ServiceNow
- Implementation
- Median resolution time
- ↓58%Median resolution time
- Register and system downtime
- ↓31%Register and system downtime
- Requests self-served
- 34%Requests self-served
An illustrative engagement: a US online retailer with a deep catalog
The product was in stock. The shopper couldn't find it.
They asked for conversational discovery agents grounded in catalog and inventory to guide purchases. Recommendations were never the gap: shoppers who knew what they needed but not the product name hit a dead end in keyword search and left, past products sitting in stock the whole time.
- Microsoft
- Generative & Agentic AI
- Higher conversion
- 18%Higher conversion
- Larger basket
- 15%Larger basket
- Fewer dead-end searches
- 40%Fewer dead-end searches
An illustrative engagement: a US retailer with hundreds of store associates
The answer was on the intranet. The associate was on the floor.
They asked for a mobile SharePoint hub with promos, training and shift information in one place. It was a reach problem, not a content problem: the information existed but never reached a deskless, high-turnover floor that doesn't use email or sit at a desk.
- Microsoft
- Digital Transformation
- Associate adoption
- 85%Associate adoption
- Faster onboarding
- 40%Faster onboarding
- Comms reach
- 2×Comms reach
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