Operations layer for AI/automation pipelines.
For engineering-led mid-market IT teams. We operate the production-grade last 20% on your infrastructure.
For CIOs/CTOs at $50M-$1B firms in manufacturing, distribution, logistics, financial services, professional services. The Operations-Partner-shape ICP: engineering-led IT teams who DIY'd the first 80% themselves.
What CIOs are actually asking
Five questions dominating CIO conversations in 2026.
Extracted from CIO peer forums. JieGou is built around these specific concerns — not against generic AI transformation pitch decks.
"AI vendors quote 60% deflection. What's your real number after 6 months at scale?"
Real production at 1,500-2,200 employee orgs: 20-40%. Vendors keep quoting 55-70%. The gap is the most consistent complaint on CIO peer forums.
Our answer
We measure honestly against your baseline. Phase 1 pilot ends with a measurable success-metric review and a walk-or-stay decision. No vendor-defined "auto-resolved" gaming.
"Who owns AI when it breaks at 3am?"
Vendors point to the customer-success queue. Consultants walked away after framework delivery. In-house teams can't hire AI ops engineers at mid-market budgets.
Our answer
Operations Partner identity exists precisely for this gap. Named human accountable; founder operationally responsible in early pilots; senior ops team as we scale.
"Every vendor says they understand our business. Most don't even understand their own software."
CIOs run 6-vendor pilots and find vendors can't answer "how do you define auto-resolved" — four different definitions across six vendors.
Our answer
We don't ask you to trust us. We ask you to review the Reference Architecture — every component named, every failure mode defined, every architectural exclusion explicit.
"What AI use cases are actually material enough to get approved?"
Productivity-tier AI (Copilots, drafting assistants) creates time savings that get absorbed into low-impact work. Process-level AI (workflow redesign) shows up in metrics leadership cares about.
Our answer
We operate production-grade workflows where AI changes the process boundary — invoice extraction, customer-support drafting, compliance evidence — not individual-productivity wrappers.
"What's your security review actually catching that vendors don't disclose?"
OAuth scope sprawl. Vector store retention disclosed asymmetrically from prompt retention. Downstream model providers buried in subprocessor lists. Indemnification for hallucinated outputs.
Our answer
The Reference Architecture answers all five named concerns explicitly. The 10-Layer Self-Assessment lets you baseline your estate before signing any vendor contract.
Watch — the platform-engineering ceiling at mid-market
Three identities by replacement cost
Vendor / Consultant / Operations Partner. Two are easy to replace.
Replacement cost is the operator-grade lens. Trust and fit are subjective; replacement cost shows up in the spreadsheet.
Tools and frameworks don't run anything at 3am. Someone has to.
Architect-peer transparency
Your engineering lead reviews the architecture before procurement gets involved.
We don't ask for trust. We ask for review. Named stack, not abstractions. The level of detail you'd expect from a peer engineering team — not a marketing deck.
- ✓ 7-component decomposition (Intake / Extraction / Metadata / Structuring / Handoff / Audit / Review)
- ✓ 3 trust boundaries with data classification + encryption + retention per crossing
- ✓ 10 named failure modes with detection + behavior
- ✓ 3 deployment options — managed cloud / VPC hybrid / fully self-hosted
- ✓ 7 explicit architectural exclusions ("what this does NOT do")
- ✓ NO training on customer data — Anthropic + OpenAI + Google DPAs available
Free self-assessment
10-Layer AI Governance Maturity Assessment.
Median score across mid-market shops: 35/100. Top decile: 75+. Most are below the threshold for what auditors are starting to expect.
Run it on your AI estate before signing your next vendor contract. Same framework we use internally to assess our own platform. Free; no email gate.
Run the assessment →Pricing transparency
Phase 1 pilot: free for 30 days.
Production engagement: starts at $50K/year.
Engagement fee + annual ops support, not per-seat. BYOK for LLM (no markup). Multi-pipeline expansion economics that scale down. Published in writing — not 'contact us for a custom quote.'
See full pricing →Prêt à voir des résultats ?
Dites-nous ce dont vous avez besoin. Nous vous montrerons comment les opérations gérées offrent de meilleurs résultats à une fraction du coût.