Natural language processing
Sentiment analysis, entity extraction, document classification, and summarisation pipelines. We build models on your domain corpus so accuracy reflects your actual vocabulary, not generic benchmarks.
2–6 week engagementsArtificial Intelligence services directory
Find the right AI capability for your project. Each listing below describes a specific service we deliver, the typical engagement length, and who it suits best. Scroll the sidebar to jump between categories, or browse the full board here.
Predictive analytics
Time-series forecasting, churn prediction, demand planning. We handle feature engineering and model selection, then hand you a containerised API endpoint you can call from any stack.
3–8 week engagementsComputer vision
Object detection, image segmentation, OCR, and quality inspection models. We train on your labelled data or help you build a labelling workflow from scratch if you have raw images only.
4–10 week engagementsAI strategy consulting
Not every problem needs a neural network. We audit your data estate, map feasible use cases, estimate ROI, and produce a prioritised roadmap your board can actually approve.
1–3 week sprintDocument intelligence
Invoice parsing, contract extraction, compliance scanning. We combine OCR with fine-tuned language models so you get structured JSON from messy PDFs, scans, or handwritten forms.
3–6 week engagementsConversational AI
Task-oriented chatbots and voice assistants grounded in your knowledge base. We use retrieval-augmented generation so the bot answers from your docs, not from hallucinated guesses.
4–8 week engagements
Built on real infrastructure
Our models run on dedicated GPU clusters in UK-based data centres. Your data stays within jurisdiction, latency stays low, and you get SSH access to the training environment if you want it. No black boxes.
Service comparison matrix
Use this table to compare what each service tier includes. All tiers come with a post-delivery support window; the length varies.
| Feature | Starter | Growth | Enterprise |
|---|---|---|---|
| Data audit and feasibility report | ✓ | ✓ | ✓ |
| Custom model training | — | ✓ | ✓ |
| API deployment | — | ✓ | ✓ |
| Ongoing retraining schedule | — | — | ✓ |
| Dedicated engineering contact | — | — | ✓ |
| Post-delivery support | 7 days | 30 days | 90 days |
| Typical budget range | £2k–£8k | £10k–£35k | £40k+ |
How an engagement typically unfolds
Discovery call
A 30-minute video call where we learn what you want the AI to do, what data you have, and what constraints matter (budget, timeline, regulatory). No preparation needed on your side.
Data review
We look at a sample of your data under NDA. Sometimes we find the data is ready; sometimes we recommend a cleaning or labelling phase first. Either way, you get a written assessment within five working days.
Prototype sprint
We build a working proof of concept, usually in two to three weeks. You test it against real scenarios and tell us where it fails. Failure at this stage is cheap and informative.
Production build
Once the prototype proves the concept, we harden it: error handling, monitoring, autoscaling, documentation. The output is a containerised service with an OpenAPI spec.
Handover and support
We transfer the code repository, walk your team through the architecture, and remain available during the support window. If you need ongoing retraining, we set up an automated pipeline before we step back.
Who we work with
Most of our clients are mid-size companies with 50 to 500 employees. They have data but lack the in-house ML expertise to turn it into a product. A few are startups building an AI-first product and need a technical partner for the first version.
We have declined projects where the data was insufficient or the expected outcome was unrealistic. That honesty saves everyone time. If your idea is feasible, we will tell you exactly what it takes.
Readiness checklist
Before you reach out, a quick self-check. You don't need to tick every box, but having at least two of these in place shortens the discovery phase considerably.
- You can describe the business outcome you want in one sentence (e.g. "reduce manual invoice processing time by half").
- You have at least a few hundred examples of the data the model would need to learn from.
- Someone on your team has the authority to approve data sharing under NDA.
- You have a rough budget range in mind, even if it is wide.
Common questions
Not necessarily. If your data is unlabelled, we can set up a labelling workflow using tools like Label Studio and guide your team through the process. For some tasks, pre-trained models or few-shot approaches reduce the labelling burden significantly.
All training happens on UK-based GPU servers. Data is encrypted at rest and in transit. We sign an NDA and a data processing agreement before any data transfer. Once the project ends, we delete your data from our systems within 14 days unless you ask us to retain it.
Yes. We deliver models as REST APIs with OpenAPI documentation. If you use a specific platform (Salesforce, SAP, a custom ERP), we handle the integration layer so the model fits into your workflow without your team needing to learn ML tooling.
That is why we start with a prototype sprint. If the prototype shows the task is harder than expected, we discuss options: more data, a different modelling approach, or an honest conversation about whether AI is the right tool. We have walked away from projects at this stage when the evidence pointed that way.
Fixed price for clearly scoped work, time-and-materials for exploratory phases. We always agree the pricing model before work starts, and we cap T&M engagements so you never face an open-ended bill.
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