AI That Works
In The Real World,
Not Just Demos

Many businesses worry they don't have enough data, but the threshold is lower than most think. For supervised learning tasks, hundreds to thousands of labelled examples can be sufficient. For automation and rules-based AI, no training data is required at all. We assess your data maturity in our initial audit and recommend approaches scaled to what you actually have — including transfer learning from pre-trained models when your dataset is limited.

Automation executes predefined rules and sequences without variation — useful for structured, repetitive tasks with known inputs. Machine learning uses statistical models trained on data to make predictions or decisions in situations the system hasn't explicitly seen before. AI is the broader capability that combines ML, reasoning, and perception. In practice, the most valuable implementations combine all three: automation handles the workflow, ML makes the intelligent decisions within it, and AI interfaces handle unstructured inputs like language and images.

Simple automation projects can be deployed in 4–8 weeks. ML models for well-defined classification or prediction tasks typically take 2–4 months from data assessment to production. Complex AI systems involving custom model development, data pipeline engineering, and MLOps infrastructure usually require 4–8 months. We break projects into phases so you get value early — the first phase often delivers measurable ROI before the full system is complete.

Automation and workflow AI projects typically start at $15,000–$40,000. Custom ML model development with data pipeline work ranges from $50,000–$150,000. Full AI platform development with MLOps infrastructure and multiple models can exceed $200,000. We provide fixed-scope estimates after the discovery phase — no open-ended retainers with unpredictable invoices.

We insist on testing with your actual data, in your actual infrastructure, against your actual performance requirements before any production deployment. We define success metrics upfront — accuracy thresholds, latency limits, false positive rates — and we don't sign off on deployment until those benchmarks are met. We also implement monitoring and drift detection so you know immediately if model performance degrades after launch.

All production AI systems we build include human-in-the-loop escalation paths, confidence thresholds below which decisions are flagged for human review, and full audit logging of every AI decision. We design for graceful failure — the system degrades to human oversight rather than making confident wrong decisions. We also include explainability mechanisms so when the AI is wrong, your team understands why.

Yes — integration is a core part of every engagement. We connect AI systems to your CRM, ERP, data warehouse, APIs, and workflow tools. For legacy systems without APIs, we build integration middleware. The goal is AI that augments your existing tools and workflows rather than requiring your team to use yet another separate platform.

Both, depending on the use case. For many NLP and generative AI applications, fine-tuning or prompt-engineering existing foundation models (GPT-4, Claude, Llama) on your data is faster and more cost-effective than building from scratch. For domain-specific prediction tasks, classification, or computer vision on proprietary data, custom-trained models typically outperform generic alternatives. We recommend the right approach based on your accuracy requirements and data situation.

Data security is integrated into the project architecture from day one — not bolted on at the end. We implement data anonymisation, access controls, encryption at rest and in transit, and can deploy entirely within your private cloud infrastructure for sensitive use cases. For regulated industries (healthcare, finance), we design architectures that meet HIPAA, GDPR, and relevant compliance requirements.

Only if it's maintained. Data distributions shift, business conditions change, and models trained on last year's data become less accurate over time. Every production AI system we build includes monitoring dashboards, data drift detection, automated retraining pipelines, and alerting when model performance drops below defined thresholds. We offer ongoing MLOps retainers to keep your models performing at the accuracy level they were deployed at.