The engineering under every agent.

Both — and the order matters. Our founders came out of the IBM webMethods consulting practice, so webMethods is where we have the deepest practitioner depth. From that foundation we deliver MuleSoft Anypoint, Dell Boomi, Azure Integration Services, and Apache Kafka programmes — and we have opinions on where each one fits. The honest position: webMethods for governed, audit-heavy estates; MuleSoft for Salesforce-centred enterprises; Boomi for cloud-first hybrid; Azure when the rest of the estate is already on Azure. We deliver all of them. We do not pretend they are interchangeable.

Yes — programme rescue is a regular engagement type. We start with a short architecture and operational audit (usually two to three weeks), produce a remediation plan that distinguishes "now" from "later," and then either embed senior engineers alongside the existing team or take the programme over outright. We do not require the original consultancy to be removed before we engage; we work with whoever is in place.

Enough to fit the estate. A focused refactor of the top-tier integrations plus an API Gateway tier in front of legacy ESB is usually six to nine months for a mid-sized estate. A full strangler-fig migration off webMethods to a different platform is more often eighteen to thirty months. We will not give a deck-friendly number until we have walked the estate; the integrations that look simple are rarely the ones that cost time.

Informatica is still the right answer for enterprise MDM, data governance, and the regulated reporting workloads where its lineage and rule-engine capability are hard to replicate. The modern stack (Snowflake or Databricks for compute, dbt for transformation, Fivetran for ingestion, a BI layer on top) is a strong fit for analytics-first estates without heavy MDM requirements. Most enterprise estates we work in need both for different workloads; the architecture question is which is the system of record for which domain.

Narrowly, on purpose. The most common MDM failure is to start with "unify all customer data" and discover, two years in, that the political work was the real programme. We start by picking a bounded domain (one critical business entity), shipping a working golden record for that entity in three to six months, and demonstrating the operating model. Expansion follows demonstrated success. The technology is rarely the bottleneck.

Data governance is an operating model, not a tooling decision. Tools matter — we deliver on Informatica Axon, Collibra, and the cloud-native equivalents — but the governance only works when the data stewardship roles are defined, accountable, and tied to real business outcomes. We design the operating model alongside the tooling, and we will not start a tooling rollout if the role definitions are absent.

Azure is the default for our regulated industry and government clients — most operate in an identity, productivity, and partnership relationship with Microsoft, and the residency story across Azure regions is well-suited to Gulf-region government work. AWS is more common in our North American programmes, AI/ML-heavy workloads, and event-driven architectures. We have shipped both at production scale; the choice is rarely technical.

Less often, but yes — usually for ML/AI workloads where Vertex AI is the right tool, or for clients with an existing GCP estate. For most regulated enterprise work we engage with, the procurement and partnership story leans toward Azure or AWS.

Use a managed cluster (AKS, EKS, GKE, or OpenShift), and treat Kubernetes as platform infrastructure, not a per-team responsibility. Most enterprise Kubernetes failures we have seen come from teams being asked to operate a cluster they did not have the capacity to operate. We design the cluster, the GitOps deployment topology, observability, identity, and the operating model — then transfer the operating model to a platform team that can run it. Application teams should not be cluster operators.

When the workflow shape is well-understood, the user count is in the hundreds to low thousands, the integration surface is finite, and the platform's data model can absorb the domain without contortion. Low-code is excellent for workflow apps, inspection systems, permit platforms, and internal admin tooling. It is a poor fit for high-throughput consumer experiences, deep domain modelling, or platforms that need to evolve faster than the vendor's release cadence.

OutSystems for the regulated, integration-heavy work we do most of — government regulators, energy authorities, large internal platforms. The lifecycle tooling and integration story is, in our experience, more enterprise-ready. Mendix is the right call for data-intensive applications inside Siemens-aligned estates or where domain modelling is the dominant complexity. Both can be delivered well; both can accumulate technical debt fast if the governance is weak.

Only if the governance is weak. The same drag-and-drop interface that accelerates delivery also makes it easy to ship something that does not survive contact with the next requirement. We deliver low-code with the same discipline as custom development: architecture standards, code-review gates, naming conventions, reusable component library, ALM, and a Centre of Excellence model. Low-code without that discipline is a liability waiting to surface.

Platform operations (monitoring, incident triage, on-call response), continuous enhancement (small change requests, defect fixes, patches), change management (release coordination with the client's processes), capacity and cost management, security posture maintenance, and regular service reviews. The SLA tier — Standard, Enhanced, or Premium — defines coverage hours and response time. The day-to-day work is platform engineering, not a help desk.

No. We take on platforms we did not build when the architecture is recoverable and the operating model can be transferred to our team. The intake is more involved — discovery, runbook reconstruction, observability assessment, sometimes remediation work before we can take operational accountability. Some platforms turn out to need a remediation phase before managed services makes sense; we are explicit about that during scoping.

Three tiers, simplified: Standard is business-hours response on defects and change requests, suitable for internal platforms. Enhanced extends to extended hours and adds an on-call rotation. Premium is 24/7 with named incident-response tiers and audit reporting. The tier is chosen against the business impact of the platform, not against the deck-friendly version of the requirement; most clients sit at Enhanced.