Selected engagements

Names withheld.
Numbers are real.

We work under NDA, so you won't see logos here. Each study below is anonymised to protect client confidentiality. Same shape every time: industry and scale at the top, the problem and the shape of what we shipped in the middle, three numbers at the bottom.

Pharmaceutical · Data & AI infrastructure

IndustryPharmaceutical RegionUS Duration12 months Team3 engineers, 1 ML lead

A pharma R&D team that can finally query its own data without a ticket.

Problem: Three problems had piled up at once: data pipelines that couldn't keep pace with the volume coming out of research and manufacturing, analytics that only a dedicated team could run, and no way for non-technical staff to ask a question of the company's own data without filing a ticket and waiting.

We built: A rebuilt data-processing backbone sized for the actual volume, a leaner analytics layer their own team could extend without firefighting, and a set of AI agents that let people across the business ask direct questions of their data and get a trustworthy answer back.

We didn't: Touch the manufacturing systems that weren't broken, or build one do-everything agent where three focused ones did the job better.

65%

Reduction in data processing time

40%

Lower infrastructure cost

Days → minutes

For a non-technical team to get an answer from their own data

Systematic hedge fund · Data infrastructure

IndustrySystematic hedge fund RegionUS · New York Duration11 months Team2 engineers, 1 ML lead

A hedge fund that can finally see why its data pipeline keeps breaking.

Problem: Data processing had outgrown what the team could observe. When something slowed down or broke, nobody could tell if the cause was the infrastructure or the pipeline logic — every incident turned into a days-long hunt.

We built: Observability across both the infrastructure and the pipelines first, so every slowdown pointed to an exact cause — then fixed what the evidence actually pointed to, on both sides.

We didn't: Rewrite the pipeline architecture before knowing what was actually wrong.

Hours → minutes

To locate the source of a data-processing incident

~45%

Cut in recurring processing delays

Full visibility

Across infrastructure and pipelines, replacing guesswork

Marketing-tech platform · Team build-out

IndustryMarketing technology RegionUS Duration14 months Team3 lead engineers, 1 engineering manager, 1 ML engineer

The startup that landed five-year contracts before its platform even shipped.

Problem: An early-stage company wanted one platform where enterprise marketers could manage campaigns across TV and social from a single place. The scope was broad and timelines were already promised to their own customers: engineers to build it, someone to own delivery and architecture, and a specialist to build the recommendation engine at its center.

We built: We staffed the full team the roadmap needed — lead engineers on the platform, an engineering manager owning delivery and architecture, and an ML engineer on the recommendation system — inside the timelines already negotiated with their customers.

We didn't: Make product calls that were the founders' to make. Our job was to make their roadmap real, not set it.

5-yr contracts

Signed with two of the largest TV & marketing platforms in the US, while still an early-stage company

100%

Of roles filled within the timeline negotiated with the client

Missed hiring or delivery deadlines across the engagement

Global professional-services firm · AI proof-of-concept program

IndustryGlobal professional-services firm RegionMulti-region Duration6 months Team1 AI architect embedded per project

Five AI experiments, one process, three now headed to market.

Problem: Promising AI ideas were scattered across different divisions, each a one-off proof-of-concept with no shared way to design, build, or judge them. Good ideas were stalling for lack of a common process.

We built: One AI architect embedded per project, leading the build and carrying a consistent, repeatable way of shipping AI agents across every division involved.

We didn't: Build one-off tools and walk away — the brief was a process their own teams could repeat without us.

Divisions now building AI agents the same way

3 of 5

Proof-of-concepts now being taken to market

Weeks, not quarters

From idea to a go/no-go decision on each POC

A note on anonymisation

What you won't see here, and why.

No client names. No logos. No screenshots of internal tools. No exact dollar figures where a competitor could back into a P&L. No architecture diagrams that could be replicated without our clients' written consent. We work under NDA in domains where the systems we build are competitive assets; treating that seriously is part of the offer.

Every study above reflects a real engagement. Names, exact figures, and identifying details are withheld or rounded to protect client confidentiality — that's why some numbers above look rounder than you'd expect.

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Names withheld.Numbers are real.