Architect + AI team

We find the real bottleneck. Then we ship the fix.

Custom software for companies that need a tool that fits, not a SaaS that almost does.

Tell us your problem

Senior engineering delivered to a top-three global asset manager, a global consumer brand, and leading European banks — through enterprise outsourcing engagements. for enterprise engagements via outsourcing partners · see LinkedIn ↗

how we work

One architect directs an AI engineering team.
That's how we ship without a quarter-long discovery.

Enterprise-grade work at prototype speed. The architect is human and owns every judgement call. The team — research, build, tests, ops — scales as the engagement needs.

zoom · week 1–2 · map the bottleneck

Discovery + spike.

live
humans directing
ARCHITECT
judgement · risk · sign-off
ai · research
Discovery & mapping
ai · prototype
Spike & validate
ai · build
Integrate & ship
ai · tests
Hardening & QA
ai · security
Scan & review
ai · ops
Monitor & maintain
discover map spike 3 variants build 18 files test 142 green scan clean deploy healthy
services

Three outcomes. One focus: finding and fixing the real bottleneck.

No service catalog, no project phases. Three engagements sized to the problem — pick one, combine, or keep it rolling.

01 · discovery

Find the bottleneck.

Map the real problem, not the symptom. One architect, one week — a tight written brief with options and what we'd ship next.

typical · ~1 week · fixed scope
02 · build

Ship the fix.

A custom tool or integration that solves the mapped problem. Built on stacks we've already run at a top-three asset manager and leading European banks. The architect gates every PR; the AI team handles throughput.

typical · 4–8 weeks · to production
03 · operate

Run it reliably.

Monitoring, patching, small measured changes — by the same architect who built the fix. For teams without an in-house dev team.

typical · retainer · month-to-month
case studies

Shipped work — enterprise depth and AI-builder velocity.

Enterprise depth on one side, AI-builder velocity on the other. The track record for both halves of how we work.

group a · enterprise proof

Anonymized global asset manager · trade middleware + event hub.

horizon: 2021–2026 stack: JVM · Kafka · Cassandra confidentiality: NDA
Bottleneck: operational silos across trading and revenue systems — legacy monolith pieces, brittle integrations, telemetry gaps in event-driven flows.
Shipped: senior engineer on cross-functional scrum teams shipping the trade-charge & commissions middleware, revenue event hub, and internal CRM. Event-driven on Kafka with Cassandra / Sybase / MSSQL persistence; Grafana + Prometheus telemetry; Azure DevOps pipelines.
Outcome: stable additions to high-availability trading systems across four sustained engagements; legacy monoliths progressively decommissioned in collaboration with adjacent platform teams; junior engineers mentored across teams.
via outsourcing partner NDA-bound

Virgo · greenfield benefits-card platform.

horizon: current stack: Spring Boot · PostgreSQL · AWS role: engineering
Bottleneck: greenfield FinTech employee-benefits platform — card-based benefit distribution from zero, integrating bin sponsor, card issuer, and invoicing providers.
Shipped: senior engineer on the founding team — contributing to domain models, REST API contracts (OpenAPI), async messaging (Amazon SQS), Keycloak auth, and S3 document storage. Spring Boot + Spring Data + Hibernate + PostgreSQL on AWS.
Outcome: production-ready services shipped with the engineering team against shifting product specs; Anthropic Claude + Claude Code subagents integrated into the back-end / review / docs flow, lifting delivery velocity across the team.
direct engagement sign-off pending
group b · AI-builder proof

Anonymized European property developer · listings site.

horizon: build complete stack: static · Cloudflare Workers team: architect + AI team
Bottleneck: listings site for a European property developer launching residential units — no in-house web team, specific editorial voice to preserve, hard go-to-market deadline.
Shipped: a custom static listings site presenting 14 residential apartments with floor plans, availability filter, parking + amenity specs — designed and built by an architect directing a small AI engineering team.
Outcome: preview build complete on staging; full URL + brand published once the developer goes to market (Q3 2026).
direct engagement client launch pending

Internal product · multi-tenant short-term rental SaaS.

horizon: in development stack: Spring Boot · Next.js · PostgreSQL team: architect + AI team
Case study in preparation. White-label SaaS built end-to-end by an architect-led team; first usable milestone targeted late Q2 2026, specifics published once the product reaches public release.
internal build in development
about

About the team.

why this way

The bottleneck isn't throughput — it's misdirected work.

One architect keeps the plot. The agents handle throughput at full speed without drift. That's the whole bet — and the reason this delivery model exists at all.

the team

One architect + a specialised AI team.

One senior architect directs a coordinated set of AI engineering agents. That's the org chart. The architect owns every judgement call; the agents handle throughput.

how we scale

Senior ex-colleagues, on demand.

When an engagement needs more hands, senior ex-colleagues from prior enterprise builds join as contributors. Real plural, not fake scale — the same people, not a staffing agency.

track record

7+ years, enterprise depth.

Senior backend engineering at a top-three global asset manager, a global consumer brand, and leading European banks. The CV is the baseline, not the headline — we don't list logos on proof we don't own.

7+ yrs senior backend engineering top-three asset manager global consumer brand leading European banks
contact

Tell us about the bottleneck.

One paragraph is enough. Reply in ≤ 2 business days.

Thanks — message received.

We'll read it and reply within 2 business days, from info@vrgitsolutions.eu.

— Marcell

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