Past performance, stated precisely enough to check.

• Virginia

  Since 2015

  Registered office and delivery base, across the river from the federal market

• Maryland

  Delivered

  Federal client — engagement not yet named publicly

• Washington, DC

  Delivered

  Federal client — engagement not yet named publicly

• California

  Multi-year

  Southern California Edison — grid and workforce analytics

• Canada

  Delivered

  Government of Alberta — four decades of pipeline incident records

• Oman

  Multi-year client

  Sharek Academy — monitoring, evaluation and student-progress modelling

• Jordan

  Shortlisted

  Ministry of Digital Economy & Entrepreneurship — national AI measurement

• Saudi Arabia

  Delivered

  Tabuk — regional economy and transformation study, published as an application

Three of the four case studies below are not on the map. Their clients are described by sector rather than named — a major US investor-owned utility, an automotive telematics programme, an industrial operator — and putting a pin where we merely suspect they are would make the map more impressive and less true.

Engagements.

• Federal government · Homeland security · Critical infrastructure · 2018 – present · Prime contractor

  Predicting how a hurricane cascades through US critical infrastructure

  A hurricane does not damage one thing. It damages a port, which idles a refinery, which starves a region — and the asset records that would let anyone see that coming lived in dozens of federal and commercial datasets that shared no identifier.

  Client · Cybersecurity and Infrastructure Security Agency (CISA), US Department of Homeland Security

  contractor role, not a sub

  Primecontractor role, not a sub

  the entity-matching engine built for it

  fastEMthe entity-matching engine built for it

  infrastructure sectors linked

  5+infrastructure sectors linked

• Energy · Utilities · 2 years · Subcontracted delivery

  Predicting where a storm will break the grid — five days out

  The utility could see a storm coming. What it could not see was which districts would fail, how badly, or where to put its crews before the first pole came down.

  Client · A major US investor-owned electric utility

  reduction in outage times

  15%reduction in outage times

  forecast horizon

  5 daysforecast horizon

  model stages

  2model stages

• Energy · Environment · Public sector · 38 years of records, 1975–2013 · Direct engagement

  Four decades of pipeline incidents, and the gap nobody had measured

  Alberta had four decades of oil-spill records and no clear picture of what they showed. We analysed roughly 62,000 incidents, and found that the story everyone worried about — how fast spills get reported — was not the problem.

  Client · Government of Alberta

  incidents over 38 years

  ~62,000incidents over 38 years

  reported same day

  94%reported same day

  resolved same day

  20%resolved same day

• Commercial real estate · Hospitality · 2016, then multi-year · Contract engagement

  Matching hotels to buyers, in a market where almost nobody rates anything

  A hotel goes on the market. Somewhere in a book of two hundred thousand investors are the handful who would actually buy it, and finding them took a broker up to three months.

  Client · Jones Lang LaSalle (JLL)

  of the ratings matrix was empty

  99.35%of the ratings matrix was empty

  potential investors in scope

  200,000potential investors in scope

  increase in hotel sales, client-reported

  $13.2Mincrease in hotel sales, client-reported

• Automotive · Telematics · 19,000 km over 11 days · Direct engagement

  Telling drivers apart from the way they drive

  One vehicle, 19,000 kilometres, eleven continuous days, and a rotating crew of drivers. From telemetry alone: how many people drove it, and when did each one take the wheel?

  Client · A global automotive manufacturer

  driver identification accuracy

  78%driver identification accuracy

  continuous route

  19,000 kmcontinuous route

  elapsed

  11 dayselapsed

• Mining · Industrial · Not disclosed · Direct engagement

  Predicting equipment failure before it stops the site

  Unscheduled downtime is the most expensive thing that happens on a mine site. The operator wanted to stop reacting to failures and start scheduling around them.

  Client · A mining and heavy-equipment operator

  unscheduled downtime

  −23%unscheduled downtime

  maintenance cost

  −12%maintenance cost

References.

• “Working with Dr. Abdo was a real pleasure. His abilities to extract value from big data are first class. Creative thinking, attention to detail, and high personal standards ensured that we were successful in meeting our objectives.”

  Adam Wilson · Global Product Director, JLL

• “We have had the pleasure of working with Analytica and their highly skilled team of data science professionals. Their AI and data science consulting services have been invaluable in helping us gain a better understanding of how to leverage these technologies to optimize our business operations.”

  Sharek Academy

Further named references are available on request through the contact form.