Case study · Enterprise AI

Building Jaden Data: a bootstrapped enterprise AI company and its engineering team

From zero to a profitable, bootstrapped €1M+ enterprise AI company: platform, team, delivery standards, ISO 27001 and SOC 2, and a client base of 50+ organisations.

Own company Production

Mandate

Owned technology strategy, product roadmap, architecture, enterprise delivery, security and compliance, the technical budget and the engineering organisation from the first hire to a ten-person team.

Key decisions

  1. Bootstrap on client delivery rather than raise capital, so the platform was shaped by paying enterprise workflows from day one.
  2. Build one multi-tenant, provider-independent platform (entAIngine) instead of bespoke stacks per client, so each deployment made the next one cheaper.
  3. Treat certification as an engineering programme: ISO 27001 and SOC 2 delivered in about three months without freezing the roadmap.

Outcome — measured

€1M+ annual revenue, bootstrapped, no external capital
0 → 10 engineers hired and led across frontend, backend and AI workstreams
50+ organisations served on one multi-tenant platform at 99.9% uptime
Stakeholders
  • Co-founders and shareholders
  • Enterprise clients (procurement, pensions, development finance, pharma)
  • Auditors for ISO 27001 and SOC 2
  • Partner firms
Constraints
  • No external funding
  • Regulated clients with on-premises and data-sovereignty requirements
  • Parallel client deliveries with one small team
Reuse
  • entAIngine platform and reusable process components
  • Testbed evaluation framework
  • Prompt Wizard
  • Delivery standards: code review, CI/CD quality gates, testing requirements

Context

Jaden Data GmbH was co-founded in 2021 as a bootstrapped company delivering AI and distributed-systems work for regulated enterprises. I served as CTO until June 2024 and remain its fractional CTO and consulting partner.

Business problem

Enterprises wanted AI embedded in real back-office workflows, with security, data sovereignty and auditability they could defend to their own auditors. Delivering that as one-off projects does not scale; a small company needs a platform and an operating model that make every deployment cheaper than the last.

My mandate

I owned the technology strategy and product roadmap, the architecture of the platform and of client-specific systems, the technical budget (cloud, third-party APIs, resourcing), security and compliance, technical pre-sales and kick-offs, executive reporting to clients and shareholders, and the engineering organisation from the first hire onwards.

Decisions

  • Bootstrap on delivery and shape the platform around paying workflows rather than a speculative product roadmap.
  • One multi-tenant, event-driven platform on AWS (Lambda, ECS/Fargate, SNS/SQS) with a provider-independent model layer (OpenAI, Azure, AWS Bedrock, Google Gemini, Mistral) and role-based access control, so model choice could follow quality, latency, cost and data-sovereignty needs per client.
  • Certification as an engineering programme: security controls, evidence collection and process changes run alongside delivery, reaching ISO 27001 and SOC 2 Type 2 in about three months.
  • Standards before headcount: code-review rules, CI/CD quality gates and testing requirements were in place before the team grew, so quality did not depend on any one person.

Delivery

Engineering grew from 0 to 10 across frontend, backend and AI workstreams, with structured code review, pair programming and internal knowledge sharing. I led the redesign of an entangled frontend monolith into micro-frontends to give teams deployment independence. The platform reached more than 1,000 concurrent connections and thousands of requests per second with 99.9% uptime.

Delivery shape
  1. 2021 — Bootstrapped start (no external capital)
  2. Platform — One multi-tenant system (instead of a stack per client)
  3. ~3 months — ISO 27001 and SOC 2 (without freezing the roadmap)
  4. June 2024 — Handover as CTO (consulting partner since)

Outcome

A profitable, bootstrapped company with €1M+ annual revenue, a ten-person engineering team, two certifications and 50+ organisations on the platform.

A profitable, bootstrapped company with €1M+ annual revenue, a ten-person engineering team, two certifications and 50+ organisations on the platform.

Reuse

The platform primitives, the Testbed evaluation framework and Prompt Wizard came out of repeated client needs and were reused across every later deployment; the delivery standards became the company’s operating model.

Evidence

Revenue, team size, client count, uptime and certification timeline are company records. See the related deployments for what the platform delivered in production.

Want the same thing done in your environment?

This case is one of several. If the shape looks like your problem, the fastest route is to send me the constraints you cannot move.

Remote-first, on-site when it matters; NDA on request