AI assistants that
actually ship.
Stop fighting LLM unpredictability. Build, deploy, and govern AI assistants with structured logic — so your team can ship reliable AI without an engineering bottleneck.
Generic AI breaks
where it matters most.
When you try to put LLMs in real workflows, three things keep happening. Sound familiar?
Hallucinations cost real money
Made-up policies, invented references, confidently wrong answers. Each one is a refund, a churn, or a compliance incident.
Behavior changes overnight
Vendor swaps the underlying model. Suddenly your assistant talks differently, follows different rules, breaks integrations.
Every change goes through engineering
Marketing wants to update copy. Product wants a new flow. Compliance needs a guardrail. All blocked behind a sprint.
Structured logic.
Predictable outcomes.
A visual builder where business teams design AI flows like state machines. Validated, versioned, and governed — without writing code.
Everything you need to ship
AI to production. Nothing you don't.
Built by operators who've shipped AI at scale and gotten burned by every shortcut.
Visual flow builder
Design conversation logic as state machines. Drag, drop, connect. Business teams edit live without engineering.
- Version control built-in
- Branch & merge like Git
- Rollback in one click
Guardrails by default
Define what the assistant can and cannot say. Validate every response before it ships to the user.
- PII redaction
- Topic restrictions
- Tone & brand voice enforcement
Native integrations
Plug into your existing helpdesk, CRM, knowledge base, and data warehouse. No middleware tax.
- Zendesk, Intercom, Freshdesk
- Salesforce, HubSpot
- Snowflake, BigQuery, Postgres
Production observability
See every conversation, every decision branch taken, every output validated. Debug like you'd debug code.
- Full traces per session
- Failure mode classification
- A/B test logic variations
Test before deploy
Run thousands of synthetic conversations against new logic before shipping. Find regressions before users do.
- Synthetic user library
- Regression test suites
- Performance benchmarks
Enterprise governance
RBAC, audit logs, data residency. Built for procurement, legal, and security to actually approve.
- SOC 2 Type II in progress
- SSO/SAML
- EU & US data residency
From idea to production
in 14 days, not 6 months.
Our customers ship their first assistant in two weeks. Here's how.
Design your flow
Map your use case as a state machine. We have starter templates for support, sales, HR, compliance, and onboarding.
support_flow.gmz
Connect your data
Plug into your knowledge base, CRM, helpdesk. The assistant retrieves the right context at the right step.
kb.zendesk + crm.hubspot
Test, deploy, iterate
Run synthetic conversations. Ship to a percentage of users. Watch traces. Adjust live — no rebuilds.
deploy --canary 5%
What "structured" actually delivers.
Across customer deployments in the last 12 months.
Operators who shipped AI
that actually works.
A few of the teams who moved from "AI demo" to "AI in production" with GetMindZone.
"We tried building this with GPT-4 directly. After three months and $400K, we still had hallucinations leaking to customers. With GetMindZone, we shipped in 19 days and haven't had a single incident in production."
"What sold us was the visual flow builder. Our product team owns the assistant now — they ship updates daily without involving engineering. That's a structural advantage we didn't think was possible."
"The guardrails are the killer feature. We're in a regulated space — every AI output needs to be defensible. With GetMindZone we have audit trails, version history, and validation built in."
Questions, answered.
Couldn't find what you're looking for? Ask us directly.
How is this different from just using ChatGPT or Claude with prompts?
Prompts are instructions. GetMindZone is infrastructure. We give you a deterministic flow with guardrails, retrieval, validation, and observability around the LLM call — so the LLM does what it's good at (language) while the system enforces what it's bad at (consistency, rules, structure).
Which LLM do you use under the hood?
We're model-agnostic. Customers run on OpenAI, Anthropic, open-source (Llama, Mistral), or self-hosted. The structured layer is the same — you can swap models without rewriting flows.
What does pricing look like?
Two tracks: Platform ($2K–$8K/mo based on usage and seats) for teams that build themselves, and Service (custom SOW with KPIs) where we deliver end-to-end. Most pilots start at $5K/mo for 90 days.
How long does a typical pilot take?
14 days to ship the first assistant, 30 days to measure impact, 90 days to roll out across teams. We've never had a pilot fail to reach production when the customer commits to the methodology.
Do you handle PII / HIPAA / GDPR?
Yes. Built-in PII redaction, role-based access, region-specific data residency. SOC 2 Type II in progress (Q3 2026). HIPAA BAA available on Enterprise plans.
Can my team really update flows without engineers?
That's the core promise. Our visual builder is designed for product managers, ops leads, and customer success teams. Engineers stay involved for integrations and custom logic — not for daily updates.
Ready to ship AI
that doesn't break?
30-minute call. We'll walk through your use case and show you exactly how GetMindZone fits. No slides, no fluff.