Intelligent apps thattransform businesses
We build AI-powered applications using machine learning, NLP, and computer vision that deliver real, measurable business value — not just demos.
AI-powered solutions
LLM-Powered Interfaces
Chat interfaces, copilots, and AI assistants built on GPT-4, Claude, and open-source LLMs.
Intelligent Search & Recommendations
Semantic search, personalised recommendations, and content discovery powered by embeddings and ML.
Document Understanding & Extraction
Automated data extraction from PDFs, invoices, contracts, and forms using OCR and NLP pipelines.
Image & Video Analysis
Object detection, image classification, facial recognition, and video analytics at production scale.
Predictive Analytics Dashboards
Real-time dashboards with ML-driven forecasting, anomaly detection, and business intelligence.
Conversational AI Products
Multi-turn chatbots, voice assistants, and conversational interfaces that understand context and intent.
From data to production AI
Problem Definition
We identify where AI can genuinely add value to your business — no hype, just real impact.
Data Assessment
Evaluate your data readiness, identify gaps, and design data pipelines for model training.
Prototype & Validate
Rapid prototyping with real data to validate feasibility before committing to full development.
Build & Integrate
Production-grade AI application development with seamless integration into your existing systems.
Test & Evaluate
Rigorous model evaluation, A/B testing, and real-world validation with your users.
Deploy & Monitor
Production deployment with monitoring, alerting, and continuous model performance tracking.
Use Cases
- AI-powered customer support
- Intelligent document processing
- Content generation platforms
- Visual inspection & quality control
- Fraud detection systems
- Personalised recommendation engines
- Healthcare diagnostic tools
- Smart search & knowledge bases
Tech Stack
Clear deliverables, honest timelines
Deliverables
- End-to-end AI application (frontend + backend + model serving layer)
- Prompt and evaluation framework so you can iterate without us
- Vector store / retrieval layer with reproducible ingestion scripts
- Cost dashboards and rate-limiting per user, tenant, or feature
- Safety guardrails: PII redaction, prompt-injection defences, audit logs
- Documentation for swapping models or providers without rewrites
Typical Timeline
- 1Use-case validation & data audit1–2 weeks
- 2Prototype with real data2–4 weeks
- 3Production build + evaluations6–12 weeks
- 4Rollout & monitoring1–2 weeks
Indicative ranges based on typical engagements. Final timelines are scoped during discovery — we don't commit to dates we can't honour.
Fixed Scope
Tightly-scoped milestones with a flat price — no surprise invoices.
Dedicated Team
A senior engineer or pod working with you weekly on a retainer.
Discovery Sprint
A short paid sprint to de-risk requirements before a larger build.
Questions, answered honestly
Have an AI
project in mind?
Book a free discovery call and let's explore how AI can transform your business.
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