ROIMVP in prod in 30–45 days — without hiring a senior

≈ €75–105k saved over 10 monthsfrom €4,500/ month (≈ 1/3 of a fully loaded senior's cost)

Agents + iASSET gates · dev & cloud included · pipeline live in 1 week

Calculate my ROI
Ex-Scaleway · MCP · Agents

Put AI in production without hiring an agents team

MCP connectors, agent orchestration and controlled cloud deployment — with iASSET gates before every merge. For CTOs and CEOs who want measurable outcomes, not a ChatGPT demo.

4–8 wks

typical POC → prod

MCP + RAG

connected to your systems

iASSET gates

security before every merge

See the pipeline

The AI pipeline, step by step

Each phase = an executive deliverable and a verifiable KPI. Same iASSET workshop logic: spec, gates, prod.

Deliverable

Use case map, sensitive data and system boundaries. Go/no-go report with cost/latency estimate and compliance risks.

Executive KPI

Clear decision

Go / no-go in 7–10 days

Animated journey — hover to pause, click a step to explore.

What it costs you — and what you gain

Trade-offs a CTO or CEO must make — translated into risk and time-to-market.

Without structured support

  • AI disconnected from systems

    Isolated chatbot, manual copy-paste, no access to business APIs

  • POC that never ships

    Laptop demos, no prod pipeline or observability

  • Compliance risk

    Sensitive data, GDPR, HDS — not scoped before deployment

  • Unpredictable AI costs

    Tokens, GPU, inference — no monitoring or guardrails

With JST6 pipeline

  • Agents connected to systems

    MCP + orchestration on your real tools

  • Prod in weeks

    4–8 wks POC → prod depending on scope

  • Compliance by design

    Security gates before every merge

  • Controlled costs

    Continuous AI cost/latency monitoring

Field proof

Agents, MCP and iASSET pipelines in prod — not isolated chatbots.

MCP · Agents

HODOR — identity & policy gateway

Production platform: MCP gateway securing AI agents to GitHub, Notion, Linear, Supabase, Stripe and Vercel. Per-agent identity, granular permissions, full audit trail.

  • Native MCP protocol
  • 6+ typed SaaS connectors
  • Centralized governance & audit
MCPNestJSOAuth2PostgreSQLAWS
Visit hodor.ai

iASSET blueprint

OAuth2 & gRPC Gateway — Cursor agents

iASSET case study: versioned spec, Cursor-assisted development, CI spec+security gates on auth gateway and gRPC microservices.

  • Spec → agents → CI gates
  • OAuth2, scopes & versioned proto
  • Human review before merge
CursoriASSETOAuth2gRPC
View gateway blueprint

iASSET blueprint

Gemini CLI pipeline in prod

End-to-end guide: skills, hooks, policy and merge checklist. Industrialize AI-assisted delivery without vibe-coding.

  • CI skills spec + security on diff
  • Automated MR via GitLab API
  • Jest barrier — no merge on LLM output alone
Gemini CLISkillsPolicyCI
Open Gemini CLI guide

3 entry points

Clear scope, clear deliverable, clear decision — not a vague tech catalog.

7–10 days

AI & systems fit diagnostic

On quote

Free 15 min discovery call

  • Use case & data mapping
  • MCP or orchestration go / no-go
  • Cost/latency estimate

4–8 weeks

AI integration to prod

On quote

Scoped perimeter, phased deliverables

  • MCP servers or API porting
  • Orchestration & RAG
  • Cloud deployment + monitoring
Recommended

10-month subscription

AEP workshop — AI included

€4,500/month

Dedicated pipeline · agents · human gates

  • Product + infra + agents
  • Systematic iASSET gates
  • Founding Engineer in command

What is the iASSET methodology?

Evaluation and delivery grid: five mandatory controls from spec to prod — agents allowed to execute, humans to decide.

How we evaluate / ship

Fit audit (green / amber / red) → isolated pipeline → agents in sandbox on versioned spec → human gates (arch, security, tests, obs) before merge → cloud deploy. If the project is not pipeline-compatible, we say so at the fit.

[A]

Architecture

Explicit code structure — boundaries, modules, where agents may or may not generate.

[S]

Versioned spec

Typed contracts, machine-readable intent before execution.

[S]

Security

Explicit security points — auth, secrets, compliance — verified on the diff.

[E]

Human gates

Founding Engineer review before prod merge.

[T]

Agentized CI

Auto tests, AI cost/latency monitoring, reproducible pipeline.

Capitalize your expertise rather than suffer a future legacy.

Still master, not be mastered.

See the full iASSET method →

15 minutes to scope your AI

We'll identify whether the lever is a diagnostic, a targeted integration or a full AEP workshop.

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