Our Work

Work we have done.

Selected engagements, anonymized to protect client confidentiality. Real organizations, real workflows, verified outcomes.

Global research company, 100+ staff

The situation

Proposal drafting, reporting, and client deliverables consumed senior staff time across marketing, business development, and operations. Turnaround was slow and quality varied by author.

What we did

We rebuilt the proposal and reporting workflows around AI tools, trained the teams that own them, and documented the new ways of working in playbooks each function could maintain on its own.

What changed

Proposal drafting moved from days to hours. Report production followed the same path. Teams continue to run and extend the workflows without outside help.

Retail group, multi-outlet operations

The situation

Product data arrived from dozens of suppliers in inconsistent formats. Cleaning it for the ecommerce platform was manual, listings lagged behind stock, and weekly performance reporting took a full day of copying between spreadsheets.

What we did

We built an AI-assisted pipeline that standardizes and enriches supplier data before it reaches the ecommerce platform, then automated the reporting layer on top of it. The team that owns product data was trained to run and adjust the pipeline themselves.

What changed

New products now reach the online store the day stock is confirmed instead of trailing it. Product information is consistent across the catalog. Weekly reporting runs the same morning it is asked for, and the day it used to consume goes to merchandising decisions instead.

University, teaching and research

The situation

Student progress signals lived in separate systems, so struggling students were often identified only after results were in. On the research side, administrative work around literature reviews, grant reporting, and ethics documentation was absorbing time meant for the research itself.

What we did

We built workflows that bring attendance, submission, and assessment signals into one regular view so faculty can follow up early, and rebuilt the recurring research administration tasks around AI tools. Academic and administrative staff were trained on their own live cases.

What changed

Faculty now see which students need attention weeks earlier than before, while the follow-up decisions stay entirely with the academic staff. Researchers spend measurably less of their week on administration, and the supporting documents they produce are more consistent.

SME training, bank enablement program

The situation

A leading bank wanted its small and medium enterprise clients equipped to use AI practically, not just introduced to the concepts.

What we did

We designed and delivered hands-on training where business owners applied AI to their own operations: marketing, customer communication, and administration.

What changed

Cohorts of SMEs completed the program with working AI practices in place and materials they continue to use.

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