AI Automation & Agentic AI Development

Automation is software that does the same thing every time, given the same inputs. Machine learning is software that learns patterns from data and applies them to new cases. AI is the broader category that includes machine learning, large language models, and other techniques that produce outputs that look intelligent. The right tool depends on the problem — repeatable workflows want automation, pattern recognition wants machine learning, language understanding and generation want LLMs. We pick by problem fit rather than by what is fashionable at the time of the engagement.

Build costs typically range from twenty to a hundred thousand pounds for a focused production AI feature, and from one hundred and fifty thousand upwards for an enterprise platform. Ongoing model costs vary widely — from pennies per request for cheap-model use cases to several pounds per complex agentic workflow. We model expected ongoing costs during the discovery phase so the business case is clear before any significant investment is made.

A focused AI feature typically goes from kick-off to production in twelve to twenty weeks. The prototype usually arrives in four to six weeks, with the remaining time spent on production engineering, safety review, observability, and controlled rollout. Larger platforms move in phases over six to twelve months. Anyone promising a few weeks for a production-ready system is usually skipping the work that makes the system actually reliable.

Several controls in combination. Retrieval-augmented generation grounds the model in real source documents instead of relying on training memory. Structured outputs constrain what the model can return. Verification chains check claims against sources before they reach the user. Confidence thresholds escalate uncertain answers to humans. Clear UI signals tell the user when the system does not have the answer. Evaluation harnesses catch regressions before they reach production. No single control is perfect; the combination is reliable.

Privacy and compliance are designed in from the first sprint rather than retrofitted at audit. Data classification drives model selection — sensitive data routes to providers and regions that satisfy your obligations, with self-hosted models available for the most sensitive cases. PII redaction happens before data leaves source systems where possible. Audit logs capture every model call. EU AI Act, GDPR, ISO/IEC 42001, NIST AI RMF, and sector-specific frameworks are mapped explicitly per project and evidenced throughout delivery.

Yes. Many engagements involve productionising an internal prototype, stabilising a vendor solution that is underperforming, or unifying fragmented AI experiments into a governed platform. We start with a written audit, deliver a stabilisation plan, and then move into improvement work. We work patiently with existing vendors where possible to keep continuity and avoid throwing away investments that have value.

Yes, and it is a significant portion of our AI work. We have shipped AI in banking, insurance, healthcare, and government settings — including under scrutiny from regulators such as the FCA, SBP, SECP, SAMA, and CBUAE. Regulator-friendly AI is not less innovative than consumer AI; it is more disciplined. We design for both innovation and compliance simultaneously, rather than treating them as trade-offs.

It depends on the use case. For high-stakes fully autonomous decisions, a measured approach is sensible. For productivity gains — drafting, summarising, search, classification, document processing — the technology is already production-ready and early movers are pulling ahead measurably. The best investments today combine current AI capability with proper engineering, so the system improves as the underlying models improve rather than needing a full rebuild each time.