Feasibility studies
We test whether a proposed capability can meet known constraints with available data, hardware and dependencies. Findings distinguish solvable engineering work from unresolved research risk.
AI & Machine Learning
Research designed to produce a decision
We turn uncertain technical propositions into explicit hypotheses, reproducible experiments and a clear recommendation to proceed, change direction or stop.
Capabilities
What this service delivers
Algorithm investigation
Competing methods are implemented against the same benchmark and resource envelope. We document assumptions and negative results so the decision does not rest on a favourable demonstration.
Technical prototypes
Purpose-built prototypes validate the riskiest interaction, integration or processing path. They are deliberately scoped and labelled so experimental shortcuts do not enter production unnoticed.
Architecture spikes
Short investigations compare components, protocols or deployment patterns where documentation is insufficient. Measurements cover operability and failure behaviour as well as happy-path speed.
Research engineering
We convert notebooks and informal experiments into repeatable code, datasets and test harnesses. This makes results reviewable and provides a credible bridge to product engineering.
Technology stack
Tools used for the work
PythonJupyterPyTorchNumPyPandasDockerMLflowPostgreSQL
Process
How the engagement runs
01
State the uncertainty
Stakeholders agree the decision to be made, current evidence and constraints. We rewrite broad ambition as falsifiable questions and define stopping conditions.
02
Design the experiment
We select baselines, datasets, measurements and controls before observing results. Risks such as biased samples or unavailable interfaces are recorded explicitly.
03
Prototype and measure
Experiments progress from the cheapest discriminating test to more realistic conditions. Code, inputs, configuration and findings are retained for reproduction.
04
Transfer the evidence
Results are presented with limitations, alternatives and a recommendation. If work should continue, the handover identifies production gaps rather than disguising the prototype as a finished system.
01
Good R&D removes uncertainty rather than adding features
A research engagement is valuable when it changes a decision. We begin with a proposition that could be disproved, such as whether a model meets an error threshold on representative data or a protocol remains viable under intermittent connectivity. Vague goals encourage endless experimentation.
Time and compute are allocated to the assumptions most likely to invalidate the idea. A negative result can be useful because it prevents a costly implementation, provided the test was fair and its limits are understood.
02
Prototype code has a different contract
A prototype may use fixed inputs, manual setup or a narrow platform to answer one question quickly. Those choices are acceptable when visible; they become dangerous when a compelling interface is mistaken for production readiness. We maintain a gap register covering security, scale, data quality, support and integration.
Reproducibility matters even for short work. Dependency versions, seeds, datasets and hardware characteristics are captured, while benchmark code keeps alternatives comparable. Conclusions note confidence and threats to validity instead of overstating what one trial proves.
- Hypothesis and baseline
- Versioned experiment inputs
- Recorded negative findings
- Production-readiness gap register
03
From evidence to an engineering plan
Research output should be usable by the people funding and building the next stage. We explain technical findings in terms of trade-offs, show representative failures and identify dependencies whose terms, availability or cost could alter the result.
When the proposition is viable, the next plan replaces experimental shortcuts in a deliberate order. Interfaces are stabilised, datasets governed, tests broadened and operational controls introduced. When evidence is weak, we recommend a narrower use case or further test rather than presenting certainty that the work did not establish.
FAQ
Common questions
What is the difference between an R&D prototype and an MVP?
A prototype answers a technical question and may intentionally omit product qualities. An MVP is usable by a defined audience and therefore needs security, support, data handling and operational behaviour appropriate to that use.
How long does a technical feasibility study take?
It depends on the uncertainty, access to representative inputs and experiment cycle time. We scope around a decision and milestones rather than promise a duration before those factors are understood.
What happens if the research shows the idea will not work?
We report that conclusion directly, with the evidence and limits behind it. Where possible, we identify narrower alternatives or the specific new evidence needed to reconsider it.
Who owns the prototype and research outputs?
Ownership and permitted use should be agreed in the engagement terms before work starts. We also identify third-party licences, model terms and datasets that may constrain later commercial use.
Can you productionise an existing research notebook?
Yes, after assessing its assumptions, reproducibility and dependencies. Productionisation usually requires data contracts, tests, service interfaces, security, monitoring and a realistic performance evaluation.
Related services
Connected engineering capabilities
Plan a r&d services engagement.
Share the problem, current system and constraints. We will respond with the questions needed to define a credible next step.