Data & AI

Machine Learning & AI Prototyping

Practical AI prototypes and machine learning experiments, scoped around a real business problem and measured against clear success criteria before you commit to a full build.

Discuss your project How we work
evaluate.py
results = evaluate(
    candidate="ticket-classifier-v2",
    dataset="eval/support_tickets.jsonl",  # 400 labelled examples
)
print(results.summary())

accuracy       0.87    (baseline 0.71)
needs_review   0.09
latency_p95    1.2 s

01 · Overview

Prove the value before you scale the investment

AI projects most often fail for ordinary reasons: unclear goals, poor data, no way to measure quality and no plan for production. We start with the problem and the data, agree what success looks like, and build the smallest prototype that can show whether the idea works.

Our work covers classic machine learning with scikit-learn, applications built on hosted language model APIs or open models, and integration of those capabilities into your existing products. We are candid about limitations, costs and risks, and we will tell you when a simpler, non-AI solution would serve you better.

Focus areas

  • Proofs of concept
  • Model training and evaluation
  • AI API integration
  • Model deployment

Technologies

  • Python
  • scikit-learn
  • PyTorch and TensorFlow
  • Hugging Face
  • LLM APIs
  • MLflow

02 · Capabilities

Practical AI, carefully scoped

Engineering-led experimentation that turns an AI idea into evidence.

AI prototypes

Working prototypes on your own data, with a simple interface so stakeholders can try them.

Machine learning experiments

Baselines, feature engineering and model comparison with honest, reproducible evaluation.

AI API integrations

Language model providers integrated with prompt versioning, caching, cost limits and fallbacks.

Data preparation

Collecting, cleaning and labelling the data a model needs, including evaluation sets.

Automation

Document processing, classification and extraction workflows, with human review where accuracy matters.

Model integration

Models served through APIs or batch jobs and connected to your product, with monitoring in place.

03 · Approach

A lean, measured cycle

Short iterations that end in a clear recommendation: proceed, adjust or stop.

  1. 01

    Problem and data framing

    We define the use case, check the data and agree how success will be measured.

    • Use case definition
    • Data audit
    • Success metrics
    • Feasibility check
  2. 02

    Rapid prototyping

    A simple baseline first, then iterations on models or prompts.

    • Baseline
    • Model or prompt iteration
    • Error analysis
    • Explainability checks
  3. 03

    Validation and integration

    Results measured on held-out data and exposed through a working interface.

    • Evaluation report
    • API endpoint
    • Demo interface
    • Cost and latency review
  4. 04

    Handover and next steps

    Everything you need to decide and, if justified, move towards production.

    • Training or prompt pipeline
    • Monitoring plan
    • Documentation
    • Production roadmap

04 · Technical considerations

Responsible, measurable AI

We treat AI as engineering. That means evaluation, safeguards and honest reporting.

Baselines first
Every model is compared with a simple baseline, such as rules or keyword matching, so gains are real.
Representative evaluation sets
Held-out examples that reflect real inputs, including awkward edge cases, decide whether a prototype is good enough.
Human in the loop
Where errors are costly, outputs are routed for review and confidence thresholds decide what is automated.
Data protection
We agree what data may leave your systems, anonymise where needed and choose providers or self-hosted models accordingly.
Cost and latency
Per-request cost and response time are measured alongside accuracy, because both affect whether an idea is viable.
Monitoring for drift
Inputs and outputs are monitored after launch so changes in data or behaviour are noticed and addressed.

05 · Outcomes

What you can expect

  • A clear go or no-go decision backed by evidence
  • A working prototype on your own data
  • Known costs, risks and limitations
  • A realistic path to production

06 · FAQ

Machine Learning & AI Prototyping: common questions

Ask us something else
How quickly can you deliver a proof of concept?

A focused proof of concept usually takes one to three weeks: data review and a baseline first, then iteration, then an evaluation report and demo. The timeline depends mostly on data access and quality.

Do we need labelled data?

Not always for building. Pre-trained models and few-shot techniques reduce labelling needs. For evaluation, however, we always need representative examples with known correct answers, and we help you create them.

Can you work with large language models?

Yes. We build on hosted model APIs and open-source models, including retrieval over your own documents, structured extraction and classification, with evaluation and cost controls built in.

How do you handle our data?

We agree up front which data can be used and where it is processed. Where needed we anonymise data, use providers with suitable data handling terms or run models within your own infrastructure.

What happens after the prototype?

You receive an evaluation report and a recommendation. If the results justify it, we plan the production build, including integration, monitoring and ongoing evaluation.

Related services

All services

Backend Development

Application architecture, business logic, background jobs and integrations that hold up under load.

Test your AI idea properly

Tell us the problem you want AI to solve and the data you have. We will tell you honestly whether and how to prototype it.

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