Data Discovery & Ingestion
Paved-road patterns for data discovery, ingestion, modelling and transformation, so every data team builds on the same governed foundations instead of inventing its own.
Reliable Foundations for Growth
Platform Engineering
When every team builds its own pipelines, networking and environments, you pay for the same problem five times and get five different answers. And when the alternative is raising a ticket and waiting, engineers stop asking. We build internal platforms with golden paths teams consume on self-service, so shipping fast and staying governed stop being a trade-off.
Golden Paths · Self-Service · Guardrails
What is Platform Engineering?
Platform engineering is the discipline of treating your internal platform like a product, with your engineers as the customers. Instead of every team solving networking, pipelines, security and environments from scratch (or worse, raising tickets and waiting), the platform team paves golden paths: templated, governed routes to production that teams consume on self-service.
The foundation is an Azure landing zone aligned to the Cloud Adoption Framework, with identity, networking, policy and cost management defined entirely in Bicep or Terraform. The platform layer makes that foundation usable: a new service goes from repository template to deployed-with-guardrails in under a day, because the compliance conversation happened once, in code, instead of per project.
The same discipline covers data and AI platforms, which is where our practice is unusually strong. The Fabric lakehouses, Databricks estates and Azure AI Foundry environments our other teams build all stand on platform engineering from this one, so the paved roads extend past applications into analytics and agents.
The payoff is compounding: every team that onboards inherits the guardrails, the observability and the cost controls, and the platform gets better through pull requests rather than committees. It is the difference between a cloud estate that scales with headcount and one that scales with automation.
Platform engineering, built on Microsoft Azure
What We Deliver
Key capabilities
Optimised Analytics Environments
Databricks, Synapse and Azure Data Lake environments provisioned from templates, tuned for the workloads they serve and inheriting the platform's cost and security guardrails.
Continuous Delivery Pipelines
Deployment pipelines provided as a platform capability, so teams get reliable releases from day one rather than building CI/CD from scratch on every project.
Cloud-Native Data Pipelines
Real-time and batch pipelines on Azure-native services, engineered to survive failures and recover on their own instead of paging a human to restart them.
Hybrid Data Engineering
Patterns that bridge on-premises and cloud estates, so data workloads run under one set of platform controls regardless of where the data has to live.
Governed Platform Architecture
Governed, scalable Azure platform architecture designed with security, compliance, and organisational standards built in from the foundation.
- CAF-aligned landing zones defined in Bicep or Terraform
- Azure Policy so governance enforces itself
- Golden-path templates for the workloads teams build most
- Cost controls and observability inherited by every environment
Deployment & Performance Optimisation
Ongoing tuning and managed operation of the platform itself, so it keeps pace as more teams onboard rather than quietly degrading into the next bottleneck.
AI, ML & Advanced Analytics
Platform support for AI, ML and IoT analytics workloads, so the teams building models and agents inherit the same paved roads as everyone else.
6 wks
From concept to an internal developer platform MVP
100%
Golden-path paved road for product teams
< 1 day
From repository template to deployed with guardrails
IaC
Everything defined as code, improved through pull requests
Common Use Cases
Where Platform Engineering earns its keep
Internal developer platform
Give engineering teams self-service templates for apps, data and AI workloads so they ship faster without reinventing compliance each time.
Azure landing zone at scale
Operate a CAF-aligned landing zone across dozens of subscriptions with consistent networking, identity, security and policy.
Zero-downtime data pipelines
Engineer reliable batch and streaming pipelines with Databricks, Synapse or Fabric that survive failures without manual intervention.
Container platform engineering
Run AKS as a product, with ingress, service mesh, policy, observability and developer tooling baked in.
Observability and SRE practices
Introduce golden signals, SLOs, error budgets and on-call so reliability is measured and continuously improved.
Platform ops for data and AI
Operate data and AI platforms as a shared service with capacity, cost, security and governance handled centrally.
How We Work
A proven delivery approach
- 01 Step
Discover
Understand developer and data-team workflows, current pain points, and the highest-value paved roads to build.
- 02 Step
Design
Define target platform capabilities, self-service templates, security and compliance guardrails and operating model.
- 03 Step
Build
Deliver the platform iteratively, templates, pipelines, observability and documentation, and onboard the first product teams.
- 04 Step
Run
Operate the platform with clear SLOs, continuous improvement and a product backlog driven by consumer feedback.
Operating Model
Platform engineering or ticket-driven ops?
Most organisations do not choose ticket-driven operations; they accumulate it. Every environment request becomes a queue item, every queue item becomes a wait, and eventually the wait becomes the culture. Here is what changes when the platform becomes a product.
| Criteria | Ticket-driven ops | Platform engineering |
|---|---|---|
| Getting an environment | Raise a ticket, wait, chase, escalate | Self-service from a template, minutes not weeks |
| Consistency | Every environment is a hand-crafted snowflake | Every environment inherits the same guardrails |
| Security & compliance | Reviewed per project, argued per project | Designed once into the paved road, enforced by policy |
| Ops team workload | Drowning in requests, no time to improve anything | Building the platform; the platform handles the requests |
| Cost visibility | Reconstructed at month-end, disputed at month-end | Tagged and attributed automatically at creation |
| Scaling | More teams means more tickets means more ops hires | More teams means more consumers of the same paved roads |
The shift does not need a big-bang reorganisation. We start with one golden path for your most common workload, prove the speed difference on a real team, and grow the platform from demand rather than decree. Six weeks to a working MVP is the honest timeline, and the ops team usually becomes the platform's biggest advocate once the ticket queue starts shrinking.
FAQ
Frequently asked questions
What is platform engineering, practically?
Treating your internal cloud, data and AI infrastructure as a product with paved roads, templates and self-service – so product and data teams get what they need quickly, without giving up on security, reliability or cost control.
How long does it take to build an internal platform?
A useful MVP with a handful of paved roads and real consumers typically takes 10–16 weeks. Mature internal developer platforms are a long-running product investment rather than a one-off project.
Do we need a large platform team?
No. Many clients start with a small core platform team (4–6 people) and scale as consumption grows. Synapx can run the platform team on your behalf, or work alongside yours to uplift capability.
How do you balance self-service with security?
Paved roads are secure by default. Templates bake in networking, identity, encryption, policy-as-code and monitoring, so product teams get safety without having to become security experts themselves.
Does platform engineering include data and AI?
Yes. We extend the same paved-road thinking to Fabric, Databricks, Azure ML and Azure AI Foundry so data and AI teams benefit from the same developer experience and guardrails as application teams.
Can Synapx run the platform long-term?
Yes. Synapx-as-a-Service covers ongoing platform operations, enhancement, SLO management and consumer support – leaving you free to focus on what runs on the platform rather than the platform itself.
Our Clients
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David, Skanska
Project/Programme Manager
Mike, Mount Anvil
Head of Technology Applications
Testimonials
From the very first conversation, Synapx stood out. They didn't offer a one-size-fits-all solution; they listened, understood our workflows, and constructively challenged our assumptions. Since launch, the difference has been night and day: engagement is up, performance is dramatically better, and our teams now have a single, connected space for learning and communication. I'd recommend Synapx to any organisation looking to get more out of Microsoft technology.
Skanska
Project/Programme Manager
Working with Synapx has been transformative for Mount Anvil. They didn't feel like an external supplier; they became an extension of our team. Their technical expertise in implementing Microsoft Fabric solved our immediate challenges and positioned us for future growth, including AI and machine learning. What truly set Synapx apart was their transparency, commitment, and cost-optimisation through Xscale, making Fabric financially sustainable.
Mount Anvil
Head of Technology Applications
Synapx automated complex, manual processes, modernised legacy systems, and delivered scalable Power Platform solutions that streamline workflows and provide real-time insights. Their standout achievement was integrating SiteOps for accurate, real-time management of materials and compliance across multiple sites. Thanks to their future-proof architecture, we now have a robust digital ecosystem that empowers our teams.
Seras Energy Ltd
Head of Projects & Service Delivery
Exceptional communication. The final product exceeded our expectations.
The tool has streamlined our trade management process, reduced manual workloads and significantly improved efficiency and accuracy across our operations. What truly set Synapx apart was their exceptional communication, project management, and dedication to delivering excellence. They kept us informed at every stage, addressed challenges proactively, and ensured the final product exceeded our expectations.
Nuevo Partners UK LLP
Head of Operations
Their team delivered exactly what we needed, taking time to understand the purpose and specific requirements. By linking our Typeform online surveys with our Raisers Edge database, using Power Automate, they have enabled us to automate our incoming data processes. From our initial enquiry, scoping and delivery, we found them incredibly responsive and very good to communicate with.
The Ocean Conservation Trust
Head of Fundraising
Our go-to data partner. We recommend them to every one of our clients.
We work with Synapx as our go-to and trusted partner for anything Power BI, or data in general, so much so we recommend them to all our clients! The team at Synapx work with us in a way that makes it feel like they are part of our business, our 'data team'; projects and on-going support are executed easily and effortlessly.
Lanware
Chief Technology Officer
Start with a platform maturity review
We map how your teams get from commit to production today, where the queues form, and which golden path would pay back first. You leave with a prioritised platform backlog.
Book a platform maturity review