Inside the AI-native delivery practice behind Simform’s CRN 2026 AI Innovators Recognition

We are pleased to share that Simform has been named to CRN’s 2026 AI Innovators list and featured among 15 solution providers transforming their own businesses with AI.

CRN’s inaugural AI Innovators list recognizes North American solution providers for their technical capabilities and work delivering AI-focused solutions to customers. Its accompanying feature recognized 15 honorees who are also applying AI within their own organizations to change how they operate and deliver.

This recognition reflects how Simform has woven AI and accelerators of its own design into every part of its IT and engineering services, so that modernization, data, software delivery and managed services all run on AI-native practices.

The AI Innovators list is new this year. CRN, part of The Channel Company, created it to spotlight North American solution providers, a group spanning MSPs, VARs, systems integrators, and consultants, whose AI work for end customers stands out for its quality and technical depth. CRN’s editors chose the honorees.

The emphasis falls on delivered work. CRN describes its honorees as having built inventive AI solutions that produce real business improvements for their customers. That focus on solutions already running in customer environments sets the list apart from rankings built on capability claims or pilot activity.

The accompanying feature looks inward at how a group of honorees changed their own engineering, operations and delivery with AI. For buyers choosing an AI partner, that lens is a useful signal, because a provider that runs its own work on AI brings practices already tested under real delivery pressure.

Simform was recognized on both counts, for the AI systems it builds for customers and for the way AI now runs through its own delivery chain.

Simform turns AI productivity into customer outcomes by pairing its proprietary IP and frameworks with a delivery model it transformed around AI. For engineering and IT leaders, that combination translates into several practical benefits.

• A faster start on every program. Simform’s pre-built accelerators and AI-driven solutions take care of the common groundwork, so budget and engineering time go into the work that is unique to the business.
• One partner across the delivery chain. Application modernization, data, software engineering, and operations can stay within one delivery model instead of moving between disconnected teams. Decisions and engineering context carry forward from one stage to the next.
• Speed with control. AI agents can take on routine engineering work, while test verification and human approval remain part of the path to production. Teams gain speed without removing engineers from the decisions that matter.
• Practices already proven in practice. Simform has applied these AI-native methods inside its own engineering organization before bringing them into client programs. Customers start with practices that have already been exercised and refined through day-to-day delivery.
• See where cloud costs are coming from. After go-live, SimOps tracks spend down to individual AI workloads, flags unexpected spikes, and shows which team owns each cost. That gives engineering and FinOps teams a clearer basis for optimization.

At the centre of Simform’s AI delivery model is a portfolio of AI-powered accelerators and frameworks designed to reduce implementation complexity and accelerate time-to-value.

Each one is built around a specific engineering problem and brought into client engagements to give a head start, so teams begin from tested components and consistent engineering standards that shorten the path from the first sprint to production.

Our portfolio of pre-built accelerators and AI-driven solutions span across the full IT and engineering delivery chain:

Cloud operations that keep cost and reliability in check after go-live

Once systems are live, cloud spend and reliability tend to drift gradually. Idle resources, oversized clusters and costs that no team claims as its own build up a little at a time. They become expensive to unwind later, and hard to explain to finance when the bill arrives.

SimOps, Simform’s AI-driven cloud management platform, gives FinOps and engineering teams full visibility into multi-cloud spend. It allocates cost down to individual containers, SaaS subscriptions and AI workloads. Automated anomaly detection flags unexpected spikes in usage or cost as they happen, so teams can respond before a surprise reaches the monthly bill.

Simform runs SimOps as part of its managed services, configured around each client’s existing cloud usage and architecture. Tagging and allocation standards support showback and chargeback to business units, so every cost has a clear owner. Monthly reviews compare forecast spend with actuals and turn SimOps analytics into specific optimization actions, such as rightsizing workloads, retiring unused resources and capturing savings plan and reserved instance opportunities. Training and onboarding help internal teams build their own FinOps maturity over time.

Application modernization grounded in a mapped codebase

Legacy applications accumulate dependencies that documentation rarely captures in full. A modernization plan built on assumptions about how components connect tends to break the moment the first service is separated from the rest.

NeuVantage uses AI to read a legacy codebase and chart how its systems depend on one another. With that map in hand, teams settle on a modernization route much sooner. Leaders decide what to modernize first, and in what order, based on how the system actually behaves. Risks that would otherwise appear mid-migration surface during planning, when they are far cheaper to address.

Data modernization that ends on an AI-ready platform

AI and analytics programs inherit whatever condition the underlying data is in. When that data sits across legacy warehouses and multiple clouds, with inconsistent definitions between sources, every model and report built on top of it carries the same inconsistencies forward.

TrueMorph moves a client’s data out of aging warehouses and scattered sources onto Microsoft Fabric, in a form AI workloads can use. It automates much of the cleaning and mapping that makes consolidation slow when done by hand, so reporting and AI draw from one consistent foundation. TrueMorph is also Azure IP co-sell eligible. Clients that already hold Azure commitments can purchase it through the same channel, which keeps buying simple.

Software delivery run through an agentic SDLC

Coding assistants speed up individual tasks, but releases are still slowed by what happens between those tasks. Context gets lost at each handoff, requirements drift from what gets built, and testing and review queue up at the end of the cycle.

PexAI grew out of how Simform’s own engineers build and run software. The practices that held up inside Simform’s delivery and internal operations became an agentic SDLC platform with 19+ specialist agents and 45+ reusable skills. Every agent draws on a shared project context, so agents and engineers work from the same understanding of the product as tasks pass between them. Execution controls set the limits of what each agent can do, and an engineer signs off before work moves on.

Programs delivered through PexAI modernize faster and bring down the cost of keeping legacy systems running, which frees budget for new product work. AI-generated regression suites take over testing that would otherwise consume days of each release, so features reach customers on a shorter cycle. Because every agent completion is test-verified before it moves forward, engineers can hand routine work to agents with confidence and spend their own time on architecture, review and product decisions.

The value of this model is showing up in client programs where AI-driven solutions are being used to shorten engineering cycles, standardize repeatable work, and move AI and data initiatives into reliable deployment faster.

Retail automation and vending operator consolidating multi-cloud data through AI-automated cleaning and mapping

A retail automation and vending operator runs its business across the US, UK, Canada and Mexico, and its operational data sat across multiple clouds. Every Power BI report its teams used depended on that data being gathered, cleaned and mapped into a usable shape before anyone could act on it.

In consolidation programs like this one, cleaning and mapping is where the time goes. Field definitions have to be reconciled, duplicate records resolved and formats aligned across sources before a single report can be trusted. When that work is done by hand, every new data source adds to the delay.

Simform used TrueMorph to automate the data cleaning and mapping, and consolidated the operator’s multi-cloud data onto a single Microsoft Fabric environment. Power BI reporting now draws from one accurate data foundation, without having to stitch sources together for each report.

With consistent data across its regions, the operator cut product stockouts by 30%. It can now onboard a new regional operator in eight weeks, which gives the business a faster route into new markets.

AI is now being applied across more of the IT and digital engineering delivery lifecycle. The challenge is making it work consistently across those stages, with the right engineering practices, controls, and operating model in place.

Simform’s approach is to embed AI across its delivery model through reusable frameworks, proprietary accelerators, agentic platforms, and full-lifecycle engineering services. The focus is not only on introducing tools, but on solving the operating conditions that determine whether it can scale, including data readiness, governance, reliability, cost-performance optimization, and long-term ownership.

Simform will continue to deepen its AI-native engineering capabilities through investments in talent, internal delivery transformation, and the Innovation Lab, where emerging enterprise AI needs are translated into new engineering patterns, accelerators, and platforms.