Machine learning & data engineering
Machine learning systems that survive contact with production.
We design, build and ship data and machine learning systems — from the first prototype that proves the idea to the pipeline that keeps it running afterwards.
- London, United Kingdom
- info@randomforest.solutions
Services
What we take on
Most engagements start in one of these and grow into the next. We are happy to own a whole system or to slot into a team that already has one.
-
Machine learning engineering
Model development, training pipelines, hyperparameter search, and evaluation you can defend in a review.
-
Data platforms & pipelines
Ingestion, storage and transformation that stay correct when volume grows and schemas shift underneath you.
-
Web & application development
Production web applications and the APIs behind them, built to be handed to someone else later.
-
Technical consultancy
Architecture reviews, feasibility work and second opinions — usually before a budget gets committed.
-
Technical documentation
Decision records, runbooks and handover material written for the engineer who arrives after you.
-
Project & delivery management
Scope, milestones and visible progress, for teams without a technical lead to spare.
How we work
Four steps, in this order
The sequence matters more than the labels: we spend money on the risky part first, while changing direction is still cheap.
-
01
Scope
A call to understand the problem, then a written scope: what we will build, what we will not, and how we will both know it works.
-
02
Prototype
The riskiest component first. If the hard question has no good answer, you find out in week two rather than month five.
-
03
Build
Working software in front of you throughout, with direct access to the people writing it. No reveal at the end.
-
04
Hand over
Documentation, runbooks and a walkthrough, so your team can run and change the system without us in the room.
Why work with us
We specialise in taking on the awkward parts
-
Short feedback loops
We ship early and often, without trading away the things that make a system maintainable a year later.
-
Direct access
You talk to the engineers building your system. There is no account-management layer between you and the work.
-
Applied AI experience
Reinforcement learning, distributed data collection and model tuning, across both research and commercial settings.
What we build
The shape of the work
Representative examples of the kinds of systems we design and ship, drawn from our own work and research. Client engagements are covered by NDA and are discussed on a call.
-
Distributed systems · ML
Real-time scraping at scale
A coordinated network of agents collecting data in real time, feeding a training pipeline that fine-tunes a model with hyperparameter search layered on top.
-
Reinforcement learning · Security
Adversarial resilience testing
Two competing reinforcement learning agents simulating a denial-of-service failure mode, to find where a system gives way before someone else does.
-
Reinforcement learning · Networks
Routing & spectrum allocation
Custom Gymnasium environments for routing and wavelength assignment in optical networks, with policies trained against realistic traffic.
-
Reinforcement learning · Simulation
Control policies from simulation
Agents trained in simulation and moved towards production-shaped environments, from arcade-game benchmarks through to control problems that matter.
Get in touch
Have a project? Tell us what you are trying to build.
Send a few lines about the problem — what exists today, what should exist instead, and roughly when. We will reply with questions, an honest view on feasibility, and a suggested next step.
or write directly to info@randomforest.solutions