We believe Artificial Intelligence should solve actual problems, not generate slide decks
Too many AI projects start with technology and end in confusion. We start with your bottleneck, your data, your team. Then we build something that works on Monday morning.
This page explains what we stand for, how we operate, and when we are the wrong fit. Read it like a manifesto, because that is what it is.
Six principles we refuse to compromise on
Data before algorithms
A mediocre model trained on clean, well-labelled data outperforms a sophisticated architecture trained on garbage. Our first engagement milestone is always a data audit, not a model prototype.
Measurable outcomes only
Every project ships with a success metric agreed in writing before work begins. If we cannot define what "better" looks like in numbers, we do not take the contract.
Your team runs it after we leave
We train your engineers to maintain, retrain, and monitor every model we deploy. Vendor lock-in is a business model we actively refuse.
Smaller models, faster answers
We default to the smallest model that meets the accuracy threshold. Inference cost matters. Latency matters. A 200 MB model that runs on a single GPU often beats a 7 B parameter monster for production workloads.
Bias is a defect, not a feature
We run fairness audits on every classifier before deployment. If the model treats demographic groups differently without a defensible reason, it does not ship.
Plain language reporting
Stakeholders get weekly reports in English, not in LaTeX. Confusion kills projects faster than bad code.
Capability map
We do not offer vague "AI strategy." Here is the specific work we perform, the typical engagement length, and who on your side needs to be involved.
| Capability | What we deliver | Typical duration | Your team's role |
|---|---|---|---|
| Data readiness audit | Gap analysis, quality scoring, pipeline architecture recommendations | 2–4 weeks | Data engineer + domain expert available for interviews |
| Custom model development | Trained, validated ML/DL model with documented performance benchmarks | 6–14 weeks | Access to labelled data and a staging environment |
| NLP and document intelligence | Extraction, classification, or summarisation pipelines for unstructured text | 4–10 weeks | Sample documents and acceptance criteria |
| Computer vision systems | Detection, segmentation, or quality-inspection models for images or video | 8–16 weeks | Annotated image sets or access to annotation tooling |
| Model deployment and MLOps | CI/CD pipeline, monitoring dashboards, retraining triggers | 3–6 weeks | DevOps or platform engineer on your side |
| Team upskilling | Hands-on workshops for your data team covering model evaluation, experiment tracking, and production monitoring | 1–3 weeks | 2–8 participants with Python experience |
Why most AI pilots fail — and how to avoid it
According to a 2023 Gartner survey, roughly half of AI pilots never reach production. The reason is rarely technical. Projects fail because the problem was poorly scoped, the data pipeline was fragile, or no one defined what success looked like before the first line of code was written.
We spend the first two weeks of every engagement on scoping alone. That means interviewing stakeholders, profiling your data, and writing a one-page success contract that both sides sign. Only then do we open a notebook.
It slows the start. It dramatically improves the finish.
"We went through two other vendors who promised results in weeks. Pillar AI told us it would take three months. They delivered in eleven weeks, and the fraud detection model has been running in production for fourteen months without a single false-positive incident above our threshold."
— Head of risk, UK fintech (name withheld under NDA)Three pathways into working with us
I have a clear problem and data
Perfect. Send us a brief description and a sample dataset. We will respond within two business days with a scoping proposal and a fixed-fee estimate for the first phase.
I think AI could help, but I'm not sure where
We run a paid discovery workshop (one or two days, on-site or remote) that maps your operations against feasible AI applications. You walk away with a prioritised opportunity list and honest cost estimates.
I need my existing models to work better
We audit deployed models for drift, bias, latency, and accuracy degradation. The output is a concrete remediation plan with effort estimates your own team can execute, or we can do it for you.
We are probably the wrong fit if…
- You need a chatbot built in a weekend. Speed matters, but so does quality. We do not do weekend sprints.
- Your entire dataset is under 500 rows and you expect a deep learning model. Statistics will serve you better, and we will tell you so.
- You want a proof-of-concept with no plan to productionise. We build things that run, not things that demo.
- Budget is under £8,000. Our smallest engagement is a data readiness audit, and that starts at roughly that figure.
Infrastructure-aware engineering
We design models for the hardware they will actually run on. Whether that is a single on-premise GPU, a Kubernetes cluster, or a serverless endpoint, we optimise for your real constraints, not a hypothetical cloud budget.
How a typical engagement unfolds
Week 1–2: Scoping and data profiling
We interview stakeholders, profile your data sources, and draft a success contract with quantified targets. No code yet.
Week 3–5: Baseline and experimentation
We build a simple baseline model first. Then we iterate, testing architectures and feature sets against the agreed metric. You see results in a shared dashboard.
Week 6–10: Hardening and integration
The best-performing model gets packaged into a deployable service. We write tests, set up monitoring, and integrate with your existing systems.
Week 11–12: Handover and training
Your team receives documentation, a recorded walkthrough, and a live workshop covering retraining procedures and alert response.
"The handover documentation was better than anything our internal teams have ever produced. Six months later, our junior ML engineer retrained the model independently using their runbook."
— CTO, healthcare analytics startup, LondonStart a conversation
Tell us what you are working on. We reply within two business days, and we never share your information with third parties.