Simform

Premier digital engineering company and Microsoft partner specializing in Product Engineering, Cloud, Data, Agentic AI, and Enterprise Platform Innovation

[Simform @simform]

Watch the full webinar here 👇

https://t.co/mE7LILgbgh

[Simform @simform]

Most organisations are ready to invest in AI. Their data is not.

This is the pattern we see across retail and manufacturing engagements:

- Over a third of retail organisations are not yet ready to begin their AI adoption journey, and the bottleneck is not budget or intent

- Data spread across ERP, POS, IoT, and supply chain systems creates inconsistencies, duplicates, and gaps that make a trustworthy AI foundation impossible to build

- The cost of running and maintaining these fragmented legacy systems keeps growing, while the decisions they support stay a day old or worse

The real AI barrier is not the model. It is the data sitting underneath it.

In this video, Anamika Shaw, Sr. Data Architect at @Simform, Rachael Villalaz, Cloud & AI Specialist at @Microsoft, and Matthew Wendel, Principal Solutions Consultant at @Simform, cover how manufacturing and retail organisations can build a governed, AI-ready data foundation using Microsoft Fabric.

#MSPartner #MicrosoftPartner #MicrosoftFabric

[Simform @simform]

The next test of AI readiness isn't whether your agent can act.

It's whether your company can explain, govern, and stop that action when it matters.

Speed is easy. Accountability is the hard part everyone's skipping.

[Simform @simform]

Most companies racing to deploy AI agents can't answer one basic question:

When an agent makes a bad call in production, who's actually responsible?

Nobody.

And that's about to become the most expensive blind spot in enterprise AI.

[Simform @simform]

And you can't bolt this on later.

Retrofit it after deployment and the system already lacks the traces, ownership boundaries, and intervention points needed to explain its own behavior.

You're debugging a black box you built yourself.

[Simform @simform]

Simform has been recognized as an Aspirant in Everest Group's Software Product Engineering Services PEAK Matrix® Assessment 2026, featured in both the Global and EMEA editions.

@EverestGroup evaluates 52 global engineering service providers across this matrix on market impact and depth of capability. Being included reflects Simform's growing momentum in helping enterprises, ISVs, and digital-native businesses design, build, and scale modern software products.

We are grateful to Everest Group for this recognition and to every @Simform member who makes this possible.

Here is what this recognition is built on:

- PexAI brings AI-native practices across the full SDLC, backed by accelerators like NeuVantage, CodeTools, and ThoughtMesh for faster modernization and Agentic AI delivery

- A "customer zero" delivery approach where Simform validates AI-native and agentic SDLC practices within its own engineering workflows before extending them to clients

- Co-engineering pods spanning frontend, backend, platform, and quality, embedded within client product organizations and aligned to measurable outcomes

- Enablement offerings and AI-native GCCs to help enterprises and ISVs adopt AI-driven SDLC practices and build India-based engineering centers that deliver long-term value

Read the full Everest Group report: https://t.co/HYIcMytnjc

[Simform @simform]

https://t.co/K40gA5LjC1

[Simform @simform]

Legacy modernization and AI production governance are not different but are the same infrastructure problem.

At Red Hat Summit 2026, the announcement by @Microsoft and Red Hat around ARO (Azure Red Hat OpenShift) was not primarily about a new Kubernetes release. It was about a platform that runs legacy VMs and containerized AI workloads side by side, under the same identity controls, the same compliance policies, and the same operational model.

There are major consequences for engineering teams running a sequential roadmap, meaning modernization in Phase 1 and AI governance in Phase 2.

Because you are not running two projects. You are accumulating two separate compliance burdens.

Governance-by-default is now the baseline expectation.

The platform decision your team makes this quarter determines whether your AI workloads and your legacy estate share a compliance model or accumulate two.

At @Simform, when we work with engineering leaders on migrations & modernizations, the constraint we keep encountering is not the migration tooling itself. It is the upstream assessment bottleneck: which workloads move first, at what refactoring cost, and in what sequence.

𝐍𝐞𝐮𝐕𝐚𝐧𝐭𝐚𝐠𝐞, our AI-powered modernization accelerator, is specifically built to compress that assessment cycle so teams can make the platform transition without the months of manual triage that typically slow it down.

This does not mean modernization got easier. It means the cost of keeping it sequential just got higher.

#MSPartner #MicrosoftPartner

[Simform @simform]

Your first 10 AI agents worked. Your next 50 probably won't, and the reason has nothing to do with the models.

The first wave of agents succeeds because informal governance holds them together.

Someone knows what each agent does,
roughly what it costs,
what data it touches,
and who owns it.

That proximity creates stability. It is not a system; it is human attention acting as a system.

Cross 50 agents, and that model breaks. No single person can hold the full picture anymore.

Agent identity starts to blur. Ownership fragments across teams. Agents hand off work to other agents without structured contracts, so interaction volume grows faster than agent count.

Enterprises end up paying for conversations no one designed, authorized, or measured.

Meanwhile, access permissions quietly expand. Outputs accumulate with no traceable audit trail. And the AI spend that looked forecastable at 10 agents becomes unpredictable at 50.

@Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, not because the agents fail to work, but because the operating model around them does not exist.

At @Simform, we see this inflection point consistently. We built 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐌𝐞𝐬𝐡 as a reference architecture specifically for what breaks here: agent identity, governance boundaries, evaluation, and cost attribution at fleet scale.

Scaling agents is the easy part. Building the governance architecture that keeps 50 of them from compounding your risk is where the real engineering work starts.

[Simform @simform]

We work with enterprises across BFSI, healthcare, retail, supply chain & more — in North America, the UK, EMEA, and beyond.

👇Read the full @MarketsandMarkets report here
https://t.co/31Iio9ROA6

#ApplicationModernization #360Quadrant

[Simform @simform]

Simform has been recognized as a 'Pervasive Player' in the Application Modernization Services Market by @marketsmarkets on its 360Quadrants platform — one of only 35 vendors selected from 200+ evaluated globally. 🧵

[Simform @simform]

In practice:

→ 70% reduction in quote generation time for a manufacturing enterprise
→ 30% lower manual operational overhead for a last-mile logistics platform