The engineering problem changes with the sector.
Healthcare, telecommunications, insurance and agriculture, to name a few. What stays the same is the calibre of the people and the method. What changes is the data, the constraints, and the regulatory weight around them.
Adoption is decided. Governance is the bottleneck
Canadian financial institutions have stopped debating whether to use AI. OSFI and FCAC put adoption among federally regulated institutions at around 50% in 2023 and projected 70% by 2026, with 75% planning AI investment over three years.
KPMG's February 2026 read on Canadian banks and insurers found 86% investing in generative AI despite economic uncertainty, and 60% upgrading core systems specifically to embed it. Yet only 37% of banks report measurable productivity gains, and 30% name data quality as a major barrier.
The constraint is not appetite it is proving a model is governable. OSFI's E-23 enterprise-wide model risk guideline has institutions holding back until they can evidence explainability, bias monitoring and lifecycle controls. In a bank, an AI system that cannot be explained to a regulator cannot ship, however well it performs.
What we supply into it
Data engineers for the quality and lineage work that has to land before anything else does, platform and DevOps engineers for core modernisation programmes, and QA capacity where the evidence burden is heavy.
For forward-deployed work, the deliverable in this sector is never just a working model it is the evaluation, monitoring and audit trail that let risk and compliance sign it off.
Autonomy is now an integration problem, not a research one
Operators have decided. In NVIDIA's State of AI in Telecommunications survey, published February 2026, 89% said they would increase AI spending this year up from 65% the year before and 90% reported AI was already increasing revenue or reducing costs.
What they have not solved is the engineering. 65% say network automation is now AI-driven, but 88% place themselves at autonomy levels 1 to 3 on the TM Forum scale early stages, with a human in every meaningful loop. 77% expect AI-native networks to arrive before 6G does.
Closing that gap is unglamorous work: wiring models into OSS and BSS estates that predate everyone on the team, building evaluation for decisions that affect live traffic, and earning enough operational trust to take the human out of the loop. It is integration, reliability and data engineering, not model research.
What we supply into it
Engineers who have worked at carrier data volumes and are comfortable in legacy estates data engineering, platform and DevOps, QA and test automation. Where the work is taking an automation pilot into live operations, forward-deployed engineers who own it through to production.
The economics are forcing the automation
Deloitte's 2026 Global Insurance Outlook expects the P&C combined ratio to worsen from 97.2% in 2024 to around 99% in 2026. At that level, underwriting margin is close to gone and expense ratio becomes the lever which is why carriers are moving on automation now rather than in a planning cycle.
The flagship moves are public. AIG has deployed a generative AI underwriting assistant that ingests and prioritises every new excess and surplus submission without adding headcount. Zurich uses machine learning for claims fraud anomaly detection. Deloitte estimates real-time fraud analytics could save P&C insurers up to US$160 billion by 2032.
The blocker is the same everywhere: what Deloitte calls "fragmented, messy data sprawl and outdated systems". Underwriting and claims run on documents and rules, which makes them the best ground for AI in production and the least forgiving about accuracy and auditability.
What we supply into it
Data engineers who can make a fragmented policy and claims estate queryable, and forward-deployed engineers for document-heavy automation where the evaluation and audit trail matter as much as the model.
Deloitte also found only 25% of insurance executives had taken tangible action on talent, against 90% agreeing it was urgent. Capacity is part of the problem we solve.
A compliance deadline is driving the integration work
The CMS Interoperability and Prior Authorization Final Rule takes effect in 2026, and it is the dominant force in health IT engineering right now. It converts interoperability from an aspiration into a dated obligation, with FHIR-based APIs as the mechanism.
The underlying problem is older. Few patients are treated entirely within one EMR, so coordination depends on data crossing systems that were never designed to share it. Precision medicine is exposing the limits further practitioners describe falling back to PDFs for molecular results because genomic data standards are missing.
So the engineering is FHIR terminology services, normalisation and mapping across systems, and integration that satisfies an auditor. Every change has to be proven before it touches a patient record, which lengthens release cycles and raises the bar on test coverage.
What we supply into it
Integration and data engineers used to working inside tight access controls, plus QA and test automation where the burden of proof is high. Our delivery model fits the governance: our people work on your devices, inside your environment, under your controls so patient data never leaves your estate to reach us.
Sensing is outgrowing the software that has to read it
Precision agriculture was valued at US$9.50 billion in 2025 and is forecast to reach US$17.29 billion by 2031, a 10.5% CAGR, with North America holding over 35% of the market. Remote sensing is the fastest-growing technology segment in that forecast satellite and aerial imagery feeding crop monitoring and variable-rate application.
That creates an engineering problem rather than an agronomy one. Imagery and sensor telemetry arrive continuously, at volume, from equipment and platforms that do not agree on formats. The named barriers in the same research are ecosystem fragmentation and interoperability, rural connectivity, and workforce skill gaps.
Add the constraint that makes agritech distinctive: delivery windows are set by a season. Miss a planting window and the next opportunity to validate is a year away.
What we supply into it
Data engineers comfortable with imagery and telemetry pipelines, platform engineers for systems that must degrade gracefully on poor connectivity, and forward-deployed engineers where a model has to reach the field rather than a notebook.
Capacity beyond our own bench
We hold a master services agreement with a delivery partner operating across the US, Canada and India, with 15 years of enterprise delivery behind it. It means the scale of an engagement is not limited by our own headcount.
So if a requirement is larger or more specialised than one firm would normally take on alone, we can still service it with one accountable contract on your side.
We will also tell you plainly, before you sign anything, which parts of an engagement are Virdapt's own people and which come through the partner. You should never find that out afterwards.
Where the bench has delivered
- Canadian tier-1 banks and a national insurer
- National telecom carriers in Canada and the US
- A national airline and a national retailer
- A provincial public-sector registry
- Global technology, semiconductor and industrial firms
These are a few of the sectors we work in, not the limit of them. Tell us yours and we will say honestly whether our experience is directly relevant or adjacent.
Tell us about your sector.
We will tell you where our experience is directly relevant and where it is adjacent. You should hear the difference from us rather than find out later.