700+
High Tech solutions and digital product engineering
Owning outcomes, accelerating
delivery, increasing operational
efficiency
From your product to the platforms and operations
behind it
People
225+
with 9 or
more years
150+
Projects
delivered
ISO 27001:2022
10+
Years
building software
products
Why high tech companies choose
NewVision as their engineering partner
Engineering first
Architecture, test coverage and release discipline lead, proven on multi-tenant platforms under continuous release.
Digital Product Studio
Product discovery, design sprints and MVP definition before build, so the roadmap is validated before it is funded.
AI inside the lifecycle
Agents working across development and assurance: code generation, code review, log analysis, test generation and traceability.
Backed by SoftServe: 30+ years|10,000+ engineers|15 countries
What high tech leaders
are balancing
Product plans move faster
than teams can build them.
Each quarter adds more to the roadmap than the last one cleared, and hiring closes the gap slowly.
Legacy is usually what blocks
the AI the board is asking for.
The product runs on code that predates the plan, and every new idea has to pass through it.
AI spend is easier to
approve than to prove.
Licences, pilots and tools get funded, and the gain they produced is hard to show.
Code gets written faster
than it gets tested.
AI reached writing first and testing last, so releases speed up while confidence in them stays flat.
What we build, assure and run
Software solutions for high tech companies, across the product lifecycle.
- Product Discovery & Design
- Product & Platform Engineering
- Modernization & Cloud
- Product Sustenance & Support
- Carve-out & Integration
Product Discovery & Design
Roadmaps, prototypes and design sprints that turn an idea into a validated product plan with a scoped first release.
Product & Platform Engineering
Next-generation digital products and platforms, engineered on an AI-driven SDLC and released through automated pipelines.
Modernization & Cloud
AI-led modernization that migrates workloads, replaces legacy components and brings cloud spend under FinOps control. Digital transformation in high tech, delivered in phases.
Product Sustenance & Support
Mature product lines maintained alongside new development, covering feature updates, defect resolution, releases and performance.
Carve-out & Integration
Mature product lines maintained alongside new development, covering feature updates, defect resolution, releases and performance.
Proof from inside high tech
How much is your engineering
actually gaining from AI?
Accelerators built inside high tech work
AssureIT
Automates data validation across staging, transformation and mastering, so quality gates run at every stage, from data review through transformation and mastering
Test Lens
AI tool built by NewVision to optimize bulky regression suites, used to cut one enterprise suite from 300,000 test cases to 125,000.
STRIDE
AI root-cause assistant for production support, reading tickets and exceptions so engineers spend their time on the fix
SmartWizard
Automates the routine SDLC work around development and QA, from story preparation to test generation.
AI across the SDLC and STLC
Agents working in development and assurance: code generation, code review, log analysis, test generation and traceability.
Agentic Engineering Suite for Software Delivery
Multi-agent workflows that modernize legacy code and generate UI, tests, pull requests and infrastructure, with policy-as-code and observability built in.
Private Equity Framework
Technical due diligence through to post-acquisition transformation, for portfolio companies and the firms that own them.
Engagement excellence, from
trust to accountability
Most start with one scoped piece of work, and the scope grows as results come in.
Adaptation
Gain Trust
Transformation
Take responsibility
Optimization
Become accountable
Focus
- One scoped piece of work
- Access agreed with security
- release discipline set
- Modernization across the product
- Pipelines and test automation scaled
- AI embedded in SDLC and STLC
- Cost and cloud spend measured
- Duplicate work removed
- automation where volume justifies it
What you get
- A team in place within 15 days
- Delivery proven on your own systems
- Senior engineers on the work from day one
- One accountable team across build, test and release
- Releases planned around close and settlement windows
- Governed AI with evidence at every step
- An optimisation plan each year with the savings named
- Benchmarks against your own baseline
- Senior review of architecture and spend
Outcomes
- A working baseline for coverage and quality
- Your systems documented as they stand today
- Capability retained in your team
- Faster release cycles, with assurance keeping pace
- Fewer production incidents reaching the business
- Reporting and risk data traceable to source
- Lower cost to serve
- Better return on the systems you already own
- Systems you can defend to a regulator
Where firms start
Three ways to start, each scoped and costed before it begins.
2 to 4 weeks
Health Check
Architecture, test coverage, pipeline
health and AI readiness on one
system, with a costed roadmap.
4 to 6 weeks
Blueprint
Target architecture, delivery plan and
a costed first release, from the team
that would build it.
6 weeks
AI Engineering Assessment
What AI is changing across
development and QA, measured
against your baseline.
Talk to the practice
Enquiries from this page reach engineers
who have owned a release calendar.
What happens next
1
Harshad Mandlecha, Client Partner,
reads your note and brings in the right people.
reads your note and brings in the right people.
2
You hear back within one business day.
3
A 30-minute conversation
with an engineer who works on audit and tax platforms.
with an engineer who works on audit and tax platforms.
Harshad MandlechaClient Partner
We work under NDA from the first conversation. Source access, environments and data handling are agreed with your security team before work starts.
Let's Discuss
Share your priority. We'll bring the right specialist to it.
What software companies ask before
choosing an engineering partner
Do you build new products, or take over existing ones?
Both. The Digital Product Studio takes concepts to commercial platforms, and our sustenance teams take mature product lines off your roadmap, including lines currently run by another supplier, with structured knowledge onboarding so the incoming team is productive early.
How do you measure what AI is actually doing?
With a baseline before anything is rolled out. At one card and transaction processing company we evaluated GitHub Copilot against AWS Whisperer on live development and QA work, then built the enablement framework behind the rollout. Developer and test productivity rose 65%.
Who owns the code and the IP?
You do. The product, the repositories and the intellectual property stay with you throughout, and our engineers work inside your environment and your process.
How do you handle an acquisition or a carve-out?
Due diligence first, then TSA planning, data and tenant migration, and Day 1 readiness. We separated one Fortune 500 network infrastructure provider into three independent businesses, moving 4,614 users and over 400 TB.
Do you work on cloud cost as well as cloud migration?
Yes. FinOps is part of the modernization work: right-sizing, usage visibility, and spend brought back in line with what the product actually needs.
What do you decline?
Hardware and silicon. We build software, platforms and the data and AI inside them, including IoT platforms, and leave device engineering to specialists.
