Case studies

Measured outcomes, recorded against a baseline.

Every engagement below states the business problem, the baseline recorded before deployment, the intervention, the governance model and how the outcome was measured. Client names are withheld where the engagement is under NDA, and available on request under a mutual confidentiality agreement. No figure on this page is an estimate.

Discuss a comparable engagement

−31%

Overstock — Retail, UAE

4.2×

Qualified leads — PropTech, United States

12×

LLM citations — Professional Services, Global

2×

Inbound pipeline — Financial Services, United Kingdom

Figures are outcome measurements from Cognito Strategy client engagements, 2024–2026, recorded against a pre-deployment baseline. Methodology and baseline detail available on request. Client names are withheld where the engagement is under NDA.

Retail · UAE · AI Implementation

Demand-forecasting agents for a regional fashion retailer

Replaced spreadsheet-driven merchandise planning with an agentic forecasting system across more than 600 SKUs.

Overstock
−31%
To production
90d
Planner throughput
2×
Client context
Regional fashion retailer operating across UAE and wider GCC markets. Client identity available under NDA.
Business problem
Weekly buy decisions were made in spreadsheets from prior-year sales alone. Overstock was absorbed as a cost of doing business and planners spent most of the week assembling data rather than deciding on it.
Baseline
Prior-year same-period overstock and planner throughput, measured by the client merchandising team.
AI intervention
Agents ingest sales, returns, weather and event signals to generate weekly buy recommendations, and post overstock alerts into the merchandising Slack channel.
Technology & integration
Retail data warehouse and POS feeds, external signal APIs, Slack for distribution.
Governance
Recommendations are advisory. Merchandising sign-off is required before any buy is committed.
Deployment
Phased across categories over a 90-day period.
Measured outcome
−31% Overstock · 90d To production · 2× Planner throughput.
How it was measured
Outcome measured against same-period prior-year baseline by the client merchandising team. Client details available under NDA.

PropTech · United States · Revenue & Customer Intelligence

AI lead-matching and routing agent for a property platform

Enriches inbound leads, scores them against 14 signals and routes each to the right advisor with a brief pre-loaded.

Qualified leads
4.2×
Response time
−58%
Build
11 wk
Client context
Property marketplace platform with a distributed advisor network. Client identity available under NDA.
Business problem
High inbound volume, no consistent qualification, and first-touch response times long enough that a material share of leads had already engaged a competitor.
Baseline
90-day pre-launch qualified-conversion rate and first-touch response time, taken from the client CRM.
AI intervention
A scoring and routing agent that enriches each lead, scores it against 14 signals, and routes to the best-matched advisor with a pre-written brief.
Technology & integration
CRM integration, third-party enrichment sources, advisor notification workflow.
Governance
Advisors can override routing. Score inputs are visible on the lead record for challenge and audit.
Deployment
Built and in production in 11 weeks.
Measured outcome
4.2× Qualified leads · −58% Response time · 11 wk Build.
How it was measured
Qualified lead conversion measured over a 90-day post-launch window against a 90-day pre-launch baseline in the client CRM. Client details available under NDA.

Professional Services · Global · AI Visibility

Answer Engine Optimisation for a professional services firm

Rebuilt core category pages with structured Q&A, schema markup and entity graph reinforcement, then tracked share of answer weekly.

LLM citations
12×
Category share of answer
Top 3
Time to lift
60d
Client context
Global professional services firm competing in high-research advisory categories. Client identity available under NDA.
Business problem
Strong classical search performance, near-zero presence in AI-generated answers. Buyers researching the category through assistants were not encountering the firm.
Baseline
Citation volume across target category queries in the four weeks prior to implementation.
AI intervention
Entity mapping, structured content rebuild, schema implementation and an authoritative citation plan across category surfaces.
Technology & integration
Schema.org structured data, content management integration, share-of-answer tracking.
Governance
All published claims traced to a primary source before release.
Deployment
Measurable citation lift within 60 days.
Measured outcome
12× LLM citations · Top 3 Category share of answer · 60d Time to lift.
How it was measured
Citation volume measured weekly via share-of-answer tracking across ChatGPT, Perplexity and Google AI Overviews for target category queries. Client details available under NDA.

Financial Services · United Kingdom · AI Advisory

Category repositioning for a payments scale-up

Rebuilt positioning, messaging and the commercial narrative for a Series B payments company entering a crowded category.

Inbound pipeline
2×
To launch
8 wk
Category rank
Top 3
Client context
Series B payments company expanding into an adjacent enterprise segment. Client identity available under NDA.
Business problem
The commercial story had not kept pace with the product. Sales conversations were being lost on category framing rather than capability.
Baseline
Prior-year Q1 inbound qualified pipeline recorded in the client CRM.
AI intervention
Category point of view, messaging architecture by audience and funnel stage, and a rebuilt commercial narrative.
Technology & integration
Website rebuild and sales enablement system.
Governance
Claims reviewed against evidence before publication.
Deployment
Eight weeks to launch.
Measured outcome
2× Inbound pipeline · 8 wk To launch · Top 3 Category rank.
How it was measured
Inbound qualified pipeline measured in the client CRM over Q1 post-launch against Q1 of the prior year. Client details available under NDA.
Our standard

How we report outcomes.

The credibility of a number depends entirely on what it was measured against. These are the rules we hold ourselves to, and they apply to results that disappoint as well as results that do not.

Baseline before build

The pre-deployment figure is recorded in the client’s own system of record before any system goes live. Without it there is no defensible claim afterwards.

Matched observation windows

Post-launch performance is compared over an equivalent period — a 90-day window against a 90-day window, a quarter against the same quarter.

Client system of record

Figures come from the client’s CRM, merchandising reporting or tracking platform, not from our own instrumentation.

Method stated openly

Every case study states how the figure was derived, so it can be challenged. A number without a method is marketing.

No fabricated metrics

We do not publish estimates, modelled projections or illustrative figures as outcomes. Where a field is not evidenced, it is left unstated.

Underperformance reported

Where an intervention does not clear its business case, that is reported to the client at the quarterly value review rather than reframed.

FAQ

Answers to the questions we are asked most

Why are client names not published?

Most Cognito Strategy engagements are delivered under NDA, and several involve commercially sensitive operating data. Client names, contract detail and baseline figures are available on request under a mutual confidentiality agreement, and reference calls can be arranged with client consent at proposal stage.

How are the outcome figures measured?

Each figure is measured against a baseline recorded before deployment, over a matched observation window, using the client’s own system of record — CRM, merchandising reporting or share-of-answer tracking as applicable. The measurement method for each engagement is stated on the case study itself.

Can we speak to a reference client?

Yes, with the client’s consent, typically at proposal stage rather than first conversation. Where a reference is not available for a specific sector, that is stated rather than substituted with an adjacent example.

Want the detail behind these numbers?

Baseline data, measurement methodology and — with client consent — reference introductions are available at proposal stage.

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