We turn AI from a slide in the strategy deck into software that works in production. From generative AI assistants and NLP pipelines to autonomous agents that run multi-step workflows, we design, build and deploy AI that's accurate, secure and measured against real business outcomes.
The more you tell us, the sharper the first reply.
Use cases ranked by ROI
Your data stays private
Evaluated before launch
Humans in the loop where it matters
Future-Ready AI Development for Real Businesses
Most AI initiatives stall between a promising demo and a dependable product. The gap is engineering: clean data, the right model for the job, guardrails against wrong answers, integration with existing systems and monitoring once real users arrive.
That's the work we specialise in. Our AI engineers, data scientists and product designers start by identifying where AI will genuinely move a metric, prove it with a focused proof of concept, then build it into production systems your team and customers can rely on.
AI solutions shipped to production
60+
Typical proof-of-concept timeline
4–6 Weeks
Projects with evaluation before launch
100%
Find Your Path
Where are you starting from?
Most teams come to us in one of three situations. Pick the one that matches yours.
AI Development Services That Deliver Measurable Value
From language models to computer vision, we build AI capabilities into your products and operations — always starting from the business outcome, not the technology.
A cross-section of the AI systems we've built. Each one began with a specific operational problem and was judged by its impact in production, not in a demo.
SaaS
Support Knowledge Assistant
The Challenge
A software company's support team answered the same questions daily, while answers were buried across docs, tickets and wikis.
The Solution
A retrieval-augmented assistant that answers customer questions from approved documentation, cites its sources and escalates to agents when unsure.
The Impact
Answers grounded in approved docs
Confident hand-off to human agents
New articles indexed automatically
Fintech
Document Processing Pipeline
The Challenge
Loan applications arrived as scanned PDFs that staff keyed in manually, slowing approvals and introducing errors.
The Solution
An NLP and vision pipeline that classifies documents, extracts fields and flags inconsistencies for human review.
The Impact
Key fields extracted automatically
Low-confidence items routed for review
Full audit trail per application
Healthcare
Clinical Intake Agent
The Challenge
Patients spent long periods on intake forms and staff re-entered information into the records system.
The Solution
A conversational intake agent that collects symptoms and history, summarises them for clinicians and writes to the EHR via API.
The Impact
Structured summaries for clinicians
Data written directly to records
Privacy controls for patient data
Ecommerce
Demand Forecasting Model
The Challenge
A retailer regularly overstocked slow sellers and ran out of popular items during promotions.
The Solution
A machine learning forecasting model combining sales history, seasonality and promotions to recommend purchase quantities.
AI projects typically range from $25,000 for a focused proof of concept to $400,000+ for enterprise AI platforms, depending on data readiness, model complexity, integrations and compliance needs.
AI Proof of Concept
Validate the idea
$25,000 – $50,000
A focused build that tests one high-value use case on your real data, with an evaluation report and production roadmap.
AI projects carry unique risks — data quality, accuracy, cost and trust. Our process tackles them early so production launch is a confident step, not a leap.
01
Discovery & Use Cases
We identify where AI can move a business metric and rank opportunities by value, feasibility and risk.
02
Data Readiness
We assess, clean and prepare your data, and design secure pipelines for training or retrieval.
03
Model & Architecture
We select models and design the system — RAG, fine-tuning, agents or classic ML — for accuracy and cost.
04
Build & Integrate
We develop the AI system and integrate it with your applications, data sources and workflows.
05
Evaluate & Harden
Accuracy, bias, safety and cost are tested against agreed benchmarks, with guardrails added where needed.
06
Launch & Monitor
We deploy, monitor quality and usage in production, and improve the system as real data arrives.
How We Keep Enterprise AI Secure and Compliant
AI touches sensitive data and makes consequential decisions. We build governance into the architecture — not as an afterthought — aligned with the frameworks your organisation answers to.
Data Privacy by Design
Data minimisation, anonymisation and private deployments keep sensitive information protected.
GDPR & CCPA
Lawful processing, consent and data subject rights respected across AI pipelines.
HIPAA Alignment
Safeguards for AI systems that process protected health information.
EU AI Act Readiness
Risk classification, documentation and transparency practices for AI regulation.
NIST AI RMF
Risk management practices aligned with the NIST AI Risk Management Framework.
Bias & Fairness Testing
Outputs evaluated across user groups to detect and reduce unfair outcomes.
Explainability & Audit Trails
Logged inputs, outputs and decisions so AI behaviour can be reviewed.
OWASP LLM Top 10
Defences against prompt injection, data leakage and other LLM-specific risks.
AI Models & Technology We Work With
We're model-agnostic. We choose commercial or open-source models based on accuracy, cost, latency and where your data is allowed to go.
OpenAI GPT
Anthropic Claude
Google Gemini
Llama
Mistral
LangChain
LlamaIndex
Hugging Face
PyTorch
TensorFlow
Python
PostgreSQL
Pinecone
Weaviate
Databricks
Google Cloud
AWS SageMaker
Azure OpenAI
Docker
MLflow
High-Value AI Capabilities We Build In
The capabilities that make AI useful and trustworthy in production — combined to fit each use case.
01Retrieval-Augmented Generation
02Source Citations
03Tool & API Calling
04Human-in-the-Loop Review
05Multilingual Understanding
06Document Extraction
07Sentiment Analysis
08Voice & Speech
09Usage & Cost Monitoring
10Prompt-Injection Defences
Industries We've Built AI Solutions For
AI delivers the most value where it understands the domain. We bring sector knowledge so models respect the data, rules and risks of your industry.
01
Healthcare
Clinical documentation, intake, triage support and medical image analysis.
02
Finance & Insurance
Document processing, fraud detection, risk scoring and advisor copilots.
03
Ecommerce & Retail
Recommendations, demand forecasting, search and shopping assistants.
04
Logistics
Route optimisation, ETA prediction and automated document handling.
05
Legal & Professional
Contract review, research assistants and knowledge management.
06
Education
AI tutors, content generation and personalised learning paths.
07
Real Estate
Valuation models, lead qualification and listing generation.
08
Manufacturing
Visual inspection, predictive maintenance and quality analytics.
Need AI Systems That Stay Compliant Beyond Launch?
A proof of concept typically costs $25,000–$50,000, production AI solutions $50,000–$180,000, and enterprise AI platforms more. Data readiness, integrations, accuracy requirements and compliance are the main cost drivers.
How long does it take to build an AI solution?
A proof of concept usually takes 4–6 weeks. Production systems take 3–6 months depending on scope, data and integrations.
Will our data be used to train public models?
No. We use enterprise APIs with no-training guarantees or deploy models privately, and we design data flows so your information stays under your control.
How do you prevent AI from giving wrong answers?
We ground answers in your approved data, add guardrails and confidence thresholds, evaluate against test sets before launch and route uncertain cases to humans.
Do we need a lot of data to use AI?
Not always. Many generative AI solutions work with your existing documents. Custom machine learning models need more historical data, which we assess during discovery.
Which AI model is best for our project?
It depends on accuracy needs, cost, latency and data restrictions. We benchmark several models on your use case and recommend the best fit.
Can AI integrate with our existing systems?
Yes. We integrate AI with CRMs, ERPs, help desks, databases and custom applications via APIs.
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