01
Monolith → microservices
Strangler-fig migration: new system runs alongside the old; we redirect traffic incrementally. No big-bang cutover.
Legacy Modernization
Modernize legacy systems — without the maintenance window.
Strangler-fig migrations, AI-paired refactors, zero-downtime cutovers. We've shipped COBOL → Java, 15-year-old monoliths → microservices, and on-prem → cloud — all while production keeps running.
12–36 wk typical · Zero-downtime · From $80,000
Signs you need this
If any of these sound familiar, this page is for you.
- 01Your deployment process involves a 47-step wiki page (and most steps are manual).
- 02Only one engineer knows how the legacy system actually works — and they're nearing retirement.
- 03You're paying $50k/month for infrastructure that should cost $5k/month in modern cloud.
- 04Engineers spend 60%+ of their time on maintenance, not features.
- 05The team motto is 'it works, don't touch it' — and the codebase compounds that fear weekly.
What we modernize
Four migration patterns.
02
Legacy databases → modern data layer
Oracle / SQL Server → Postgres + pgvector + cloud-native; or selective decomposition where the legacy DB stays.
03
On-prem → cloud / VPC
Lift-and-shift only when it's the right answer. Most projects need re-architecting, not just relocation.
04
COBOL / legacy stacks → modern languages
Yes, we've shipped COBOL → Java/Kotlin and 15-year-old C# WPF → .NET 8 + React migrations. Documentation included.
How we work
Four phases. Honest gates between them.
01
Reverse-engineering audit
We document your current system, including the parts your last team didn't.
02
Migration strategy
Strangler-fig, parallel-run, or hybrid — chosen for the actual risk profile.
03
Incremental build
New runs alongside old. AI-paired engineering for 2–4× refactor velocity. Phased gates between stages.
04
Cutover + handoff
Zero-downtime cutover, monitoring, runbook, training. Optional managed-AI retainer for ongoing operations.
Featured case study
15-year-old C# WPF app → modern cloud platform in 9 months.
Original estimate was 2+ years. AI-paired engineering and phased migration delivered the new platform in nine months, with no production downtime.
Read the full case study →Outcome
9 mo
vs. 2+ year estimate
Industries we modernize
Featured case study
Related services
Frequently asked
Common questions.
How long does it take to build an AI MVP?
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
What does an AI MVP cost?
AISD AI MVPs typically range $45,000–$120,000 depending on scope. Drivers: number of model integrations, complexity of retrieval/data layer, custom UI surface area, and compliance requirements. We publish indicative bands on the pricing page so buyers can budget before the first call.
What is AISD's discovery sprint?
A paid 1–2 week engagement to scope a project with rigor before committing to a full build. Outputs: a written scope doc with success metrics, a technical architecture, a 1-week throwaway prototype that proves the riskiest assumption, and a fixed-price quote for the build. Typical price: $8,000–$18,000. Customers who run a discovery sprint with us are 3× more likely to ship on time and budget than customers who skip it.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How are deliverables handed off?
Every engagement ends with a handoff package: production deployment, architecture documentation, eval harness with golden test sets, observability dashboards with documented thresholds, on-call runbook, model upgrade procedure, and a recorded walkthrough. Plus a 30-day post-handoff window for questions and clarifications at no cost.
How is success measured on an engagement?
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
Is AISD SOC 2 / GDPR / HIPAA compliant?
GDPR: yes — we handle EU personal data under standard data-processing agreements and apply data-minimization patterns (redaction at source, retention windows, right-to-erasure tooling). SOC 2: Type II audit in progress. HIPAA: we deliver HIPAA-aligned engagements (BAAs available, PHI handling patterns established) but do not yet hold a third-party HIPAA attestation. We will not claim certifications we don't hold.