Deep functional and technical bench strength across the full D365 F&O lifecycle, from greenfield implementation through to ongoing support and continuous improvement.
Customer Engagement and Contact Centre sit inside the same practice, so a client running several D365 workloads deals with one team rather than three separate ones.
Why the D365, data and AI practices sit in one firm: each stage feeds the next, and the failures rarely happen in the model — they happen in the ground truth underneath it.
Functional and technical capability across the full F&O module set, delivered by consultants who have run these implementations before.
General ledger, accounts payable and receivable, fixed assets, budgeting, consolidation and statutory reporting across multi-entity structures.
Procurement and sourcing, inventory, warehouse management, demand forecasting and supplier collaboration.
Discrete, process and lean production, resource scheduling, bills of material and shop-floor execution.
Order management, distribution, transportation management and cross-border trade configuration.
Project accounting, resource scheduling, time and expense capture, and revenue recognition.
Power Apps, Power Automate, Dataverse, and the integration layer connecting D365 to the wider estate.
Greenfield implementations and multi-country rollouts, with localisation and statutory requirements scoped up front rather than discovered during UAT.
Version upgrades and platform migrations, including legacy AX estates moving to current D365 F&O.
Ongoing application management and production support, with named consultants rather than a rotating queue.
Owned in-house rather than subcontracted. It is the area where most implementations lose time, so we keep it under our own accountability.
Independent assessment of an in-flight or inherited implementation, delivered as findings you can act on rather than a scored report.
Pre-screened D365 consultants against a defined scope, with a three-day SLA on placement.
Because it is where the model meets customer data, and where the surrounding governance work determines the result.
We treat the Copilot layer as part of the platform design — which means deciding what it should have access to, what it must never surface, and how you will establish whether it is improving anything. Those are data and governance decisions, and they determine whether Copilot becomes genuinely useful or becomes shelfware. It is also why this practice sits alongside our data engineering practice rather than in a separate silo.
Configuration and extension across the CE estate, integrated with the systems it must interoperate with rather than replace.
Grounding, scope and boundaries — treated as governance decisions rather than feature flags.
Including AI-assisted paths, evaluated on the same framework we apply to any agentic system.
Answered deliberately, and in writing.
What data is Copilot grounded in, and who approved that list?
What is it explicitly not permitted to surface?
How will improvement be measured?
What is the process when it is confidently wrong?
Settling these at the outset takes longer than enabling the feature. It is considerably less costly than revisiting them after rollout.
We combine our D365 F&O depth with applied AI to build accelerators targeting four dimensions: speed, accuracy, consistency and cost.
The objective is not novelty. It is that the same team delivers a rollout in less elapsed time, with fewer defects reaching UAT.
Shorter elapsed time from kickoff to a working configuration.
Fewer defects reaching user acceptance testing.
Repeatable output across rollouts and legal entities.
Less manual effort in the phases that historically absorb it.
We deliver Oracle EBS, Oracle Fusion and SAP engagements, and several of our longest client relationships began there. Dynamics is what we are asking to be known for.
If your estate is Oracle or SAP, tell us. We will give you a direct assessment of whether we are the right partner for it, including where we are not.