Applied AI research & implementation
Nine5 Labs is a research and development consultancy. We take a problem that looks like it might be solvable with machine learning, find out whether it actually is, and then build the thing properly if the answer is yes.
Scoped per project. We would rather give you a range we can defend than a single number we cannot.
Most AI projects do not fail at the model. They fail at the data that fed it, the evaluation nobody built, and the handover to a team that was never shown how it works. That is the part we take seriously.
§1 Practice
Four connected areas. Most engagements start in one and move outward as the problem gets clearer.
Taking a capability from "this works in a notebook" to something running against real traffic, with error handling, cost controls, latency budgets and a rollback path. Retrieval systems, document processing, classification, agentic workflows and assisted internal tooling.
Delivered as deployable code and documentation, not slides.
Structured investigation of whether a proposed approach can work at all, before anyone commits a build budget to it. We define the question, run it against your real data, and write up what we found — including when the finding is that the approach is not worth pursuing.
Output is a written report with the method reproducible.
The pipelines, storage and interfaces the models depend on. Ingestion, cleaning, labelling workflows, feature and vector stores, API layers, and the integration work to connect all of it to the systems your business already runs on.
Built to be maintained by your team after handover.
Test sets, scoring rubrics, regression suites and live monitoring, so you can tell whether a change made the system better or just different. This is usually the missing piece when a project stalls, and it is the piece we insist on.
Every claim we make about a system comes from one of these.
§2 Method
A gate at the end of each stage. If the evidence does not support continuing, we say so and you stop — that outcome is a successful engagement too.
We turn a business objective into something testable: what the system must do, what counts as good enough, what data exists to support it, and what the constraint actually is. Ambiguity here is the single most expensive thing in the whole project.
Before anything sophisticated, we build the simplest thing that could work and measure it. Everything after this is judged against that baseline, which keeps the conversation about results rather than architecture.
The test set and scoring method are built before the real system, not after. It is the only way to know later whether a change helped, and the only way to hand the system over without handing over a guess.
Then the real implementation: reliability, cost, latency, monitoring, access control, and the failure behaviour when a model or an upstream service is unavailable. Shipped with documentation and a working local setup.
Walkthroughs with the people who will own it, written runbooks, and a defined support window. We are not trying to become a permanent dependency; the measure of a good handover is that you stop needing us.
§3 Engagements
A bounded investigation into one question, run against your data, delivered as a written report with the method, the results and a recommendation.
End-to-end delivery of a system: research, evaluation, implementation and handover, against milestones agreed at the start and reviewed at each gate.
Our engineers working alongside yours on an ongoing basis, for organisations building internal capability rather than outsourcing it.
§4 Contact
Not the solution you have in mind — the problem. Tell us what you want the system to do and what data you have. We will tell you honestly whether it is a good fit, including when it isn't.