The power of AI has revolutionised software development and the implementation lifecycle. We're at the forefront applying AI to rapid incremental delivery, intelligent agents and exceptional ongoing support.
Five stages, engaged in sequence or on their own. Most work starts with a single use case and grows from there.
Aligning artificial intelligence with your business goals to uncover high value automation opportunities. This is where most engagements start, identifying where AI genuinely reduces manual work or risk rather than applying it for its own sake.
Designing robust infrastructure to securely ingest data and scale intelligent workloads, built on Azure AI and the wider Microsoft Cloud stack you are likely already running on.
Adapting leading large language models to your specific operational workflows and proprietary data, so outputs reflect how your business actually operates rather than a generic default.
Embedding AI models cleanly into your existing systems with strict data privacy and compliance standards. This is where enterprise grade governance gets built in from the start, not retrofitted after a pilot succeeds.
Continuous monitoring and optimisation to ensure your models stay accurate, relevant and secure over time, rather than degrading quietly after go-live.
AI work only pays off when it sits on real platform depth and real governance. That is the part most AI engagements skip.
Our AI First methodology runs from discovery and design through build and support, so AI is not a separate project sitting next to your core systems.
We build with Copilot Studio and Azure AI alongside leading models from OpenAI and Anthropic, picking what fits the use case rather than defaulting to one vendor.
Rapid, incremental delivery means you validate a use case with real data before scaling it to production.
Data privacy and compliance standards are part of the architecture, not an afterthought bolted on once a pilot works.
AI work sits on top of genuine Dynamics 365 and Power Platform expertise, not a standalone AI practice with no platform context.
Specific to the AI First practice.
Scope is the main driver. A Strategy & Use Cases workshop is a small, fixed scope engagement, while a full build through to Lifecycle Support (MLOps) scales with data complexity, integration points, and how much fine tuning your use case needs. We size this during the initial use case discovery rather than quoting a flat rate upfront.
The starting points with the highest value are usually ones with clear, repetitive manual work behind them: deal or case summarisation, drafting an initial response, flagging items at risk before a human would notice. These tend to show value fastest and build confidence before moving to more ambitious use cases.
Through a rapid proof of concept, a small pilot on real data that tests whether the use case actually holds up in practice, before any commitment to a production build. This is deliberately fast and low risk, so a use case that does not pan out gets caught early rather than after significant investment.
Integration & Governance is treated as a core stage, not a compliance checkbox at the end. Models are embedded into your existing systems under the same data privacy and compliance standards your organisation already operates under, inheriting the wider Microsoft security model where applicable.