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AI only works if the systems underneath are modern.

Most AI initiatives stall because the legacy applications underneath cannot support them. We replatform what slows the business down — failing line-of-business apps, brittle commerce, twenty-year-old custom systems — onto modern foundations that integrate cleanly and run AI without a multi-year transformation program.

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Reference architecture

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The problem

Legacy systems block AI adoption. The application that runs your operation today was designed for a world before AI, before modern integrations, and often before cloud. AI deployed on top of it produces brittle results that do not survive contact with real workloads.

Engineering capacity is consumed keeping the old system running. Patches, workarounds, and one-off integrations dominate the roadmap. New capabilities — including AI — get queued behind the maintenance backlog.

Replacement programs fail more than they succeed. Multi-year rip-and-replace efforts ship late, over budget, and frequently with reduced functionality. The risk profile is unacceptable for most operators.

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What we do

We modernize legacy systems pragmatically — extending what works, replacing what does not, and architecting the result so AI can be embedded as a first-class capability rather than bolted on later.

Modernization assessment

A structured 2 to 4 week engagement that audits your existing systems, surfaces the integration and data debt, and produces a written modernization plan with phased scope, costs, and timelines. Fixed-price. Credited against the build.

Platform replacement

When the legacy system is past saving — failing commerce installs, brittle custom apps, vendor-abandoned platforms — we design and build the replacement on a modern stack. Migration plans, data movement, cutover scripts, and zero-downtime transition. Reference build: a 19,000-piece animation art gallery, replatformed off failing Magento, with first-year online sales contributing a 30% revenue lift.

Application modernization

When the legacy system is salvageable, we modernize what needs to change without rebuilding from scratch — refactoring data access, modernizing the integration surface, replacing UI layers, retrofitting authentication. Less disruptive than replacement, faster ROI than transformation.

Database and data-layer modernization

Schema rationalization, migration to modern data platforms, normalization for AI consumption, and the establishment of the unified data layer that downstream AI workloads will depend on.

Integration modernization

Replacing one-off scripts, brittle batch jobs, and undocumented file drops with documented APIs, event-driven architecture, and the integration plumbing that modern AI requires.

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Business outcomes

Extended life of business-critical systems

Modernization typically extends the useful life of core systems by 5 to 10 years at a fraction of replacement cost. The systems that already work for your business keep working — just on foundations that can support what comes next.

AI-ready architecture without rip-and-replace

When AI workloads are ready to deploy, the foundation is already in place. No second transformation program, no parallel rebuild, no waiting for a 24-month modernization to finish before AI can land.

Engineering capacity returned

Modernized systems take less engineering time to maintain. The team that was burning cycles on workarounds and patches gets returned to working on what moves the business forward.

Reduced cost of every future integration

A modern integration surface means every subsequent system you connect costs less and ships faster. The compounding value of modernization shows up in every project that comes after it.

Risk reduction

Legacy systems carry security, compliance, and operational risk that grows over time. Modernization addresses the underlying technical debt instead of layering more workarounds on top of it.

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How it is priced

Engagement model
Legacy System Modernization

Modernization Assessment: fixed-price, $15,000 to $40,000 depending on scope and stakeholder count. 2 to 4 weeks. Credited in full against any modernization build engagement started within 90 days.

Modernization build engagements are scoped and priced after the Assessment, with phased delivery and fixed-price scope per phase. Most modernization programs run 8 to 24 weeks across 2 to 4 phases.

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Legacy System Modernization — frequently asked questions

The questions we get most often, answered. If yours isn't here, ask it on a 30-minute call — we answer the awkward ones too.

What is a Discovery Engagement?
A two-week structured engagement that maps where AI would genuinely move your numbers — and where it wouldn't. Week one is stakeholder interviews and working sessions. Week two is synthesis and recommendations. You leave with a prioritized opportunity map, technical architecture sketches for the top one or two opportunities, realistic investment ranges, and a written report your team can act on.
How much does it cost?
Flat fee, typically $10,000–$25,000 depending on organizational complexity and number of stakeholders. Credited against any build engagement you start with us within 90 days of the Discovery Engagement concluding.
Who is it for?
Leadership teams that know they should be doing something with AI but haven't scoped it. Organizations preparing an RFP that need a technical partner in the room before they write it. Teams with a shortlist of ideas that need an honest read on feasibility, cost, and sequencing. Operators who've been burned by a previous AI consultant and want a serious, practical conversation before committing again.
What if we don't engage you for the build afterward?
That happens, and we're fine with it. The deliverables are written for you to act on — with us as your partner, or without us. We're not trying to lock you in; we're trying to get you to a good decision.
How long does a Skyview Labs AI engagement take?
Most production AI engagements ship in 4 to 12 weeks from kickoff. Discovery Engagements run two weeks. The build phase runs two to eight weeks depending on scope. Integration and testing run one to two weeks. We don't run multi-year transformation engagements — we ship working software early and iterate against real usage.
How much does AI consulting cost?
Discovery Engagements are flat-fee, typically $10,000–$25,000 depending on organizational complexity, and credited against a build engagement that starts within 90 days. Custom build engagements are fixed-price for the scope defined in Discovery — most fall in the $50k–$500k+ range depending on scope. Private AI cloud hosting and Managed AI Operations are monthly engagements priced to workload, typically bundled with the build for a single predictable monthly cost. No per-token surprises.
Where will my data be hosted?
By default, in our private AI cloud — Tier III TierPoint colocation facilities in Marlborough, MA (MRL-01) and Chicago, IL (CHI-01), with regional capacity at our Connecticut office (CT-01). For workloads with strict data sovereignty, ITAR, or air-gapped requirements, we install the entire Skyview stack on-premises in your own data center. Every engagement includes a written data flow document covering every component, every integration, and every external API call.
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Start with an AI & Modernization Assessment

Two to four weeks. Fixed-price. We tell you what needs to be modernized, what it will cost, and what AI will be possible after.

Book an Assessment