Platform · AI Agents · EU-hosted by architecture
AI agents the way regulators and boards accept them.
AI grounded in a brand knowledge graph. Every decision scored on five governance dimensions, logged on a Merkle-chain audit trail, EU-hosted by architecture.
150k+
Lines of open engineering
GraQle, the team's separate open-core SDK — 440 public test files (~7,700 test functions), public CI on Python 3.10–3.12
Lower cost
Model routing
Frontier models called only when routed cheaper models cannot meet the governance threshold
140+
PyPI releases
GraQle open-core SDK, shipped continuously since March 2026 — reproducible MultiGov-30 benchmark harness included
EP 26162901.8
Patent
Brand knowledge graph reasoning method, Quantamix Solutions B.V.
What we build
Generative AI got fast. The teams shipping agentic systems at scale also need them defensible. Generic agents guess; governed agents cite. The difference is the layer that sits between the model and the business decision: a brand knowledge graph, a scoring function, and an audit trail.
Our position is the opposite of most agent stacks. Where Copilot and similar runtimes optimise for speed of a single agent answering a single question, we optimise for the reliability of a governed workflow answering a compounded business question — one a board, a regulator, or a procurement committee will ask about six months later.
Below are the six capabilities that separate a governed AI workflow from an ungoverned one.
Six capabilities that make AI defensible
Multi-level reasoning
Reasoning flows up levels rather than down chains. A first-level agent answers; a second-level agent scores the answer against the brand knowledge graph; a third-level agent decides whether to publish, revise, or escalate to human review. Every level is inspectable and every decision carries a score.
Grounded in the brand knowledge graph
Every agent answers from the customer's own foundation documents — persona research, voice rules, positioning, case studies, interview transcripts — held as an interconnected graph. Generic agents answer from the public internet. Governed agents cite the brand's own source. The difference reads in every output.
Governance scoring on every decision
Transparency, Reasoning, Auditability, Compliance, Explainability — each scored 0-100 for every agent decision. Low scores block publication; high scores publish with a full audit trail. This five-dimension score is what makes agentic AI defensible to legal, procurement, and the board.
Substantially lower cost than single-model incumbents
Grounding + scoring + routing means the expensive model is called only when cheaper models cannot meet the governance threshold. Most agent decisions resolve on routed cheaper models with the knowledge graph doing the heavy lifting — which is what drives the aggregate cost advantage over single-model stacks on identical workloads.
Merkle-chain audit trail
Every agent decision writes to a cryptographic audit trail — model, prompt, grounding documents, score, compliance tier. The trail is tamper-evident and exportable to a regulator on demand. This is what turns agentic AI from an operational capability into a governance asset.
EU-hosted by architecture, not by policy
The entire stack is EU-hosted. Data residency, processing jurisdiction, and audit logs all stay in the European Union. For regulated EU procurement this is a gate — platforms that cannot demonstrate EU-native architecture are excluded before the technical evaluation begins.
Where governed agents ship first
Four use cases that carry the governance model naturally. Each links to the pillar where the pattern is documented in detail.
Market research agents
Agents grounded in customer interviews, competitor reviews, and community discussion produce research deliverables a board will accept. The flagship at /blog/market-research-using-generative-ai walks the full loop.
Brand-safe content generation
Multi-model routing plus SCORCH visual audit plus BRAND Score text scoring means every piece of content that ships is grounded, scored, and publishable. Text governance and visual governance run in the same pipeline.
Compliance and governance agents
Agents that read GDPR Article 30 records, EU AI Act Article 10 data governance pipelines, and BCBS 239 lineage artefacts and score them against policy. The agent that catches a non-compliant change before it ships is the one that pays for the programme.
Salesforce agentic training
Our corporate training programme covers governing Agentforce, Einstein, and Data Cloud: grounding sales responses in the brand's own case studies and positioning, and keeping decisions reviewable by a compliance officer. This is training and advisory work, not a packaged CrawlQ integration.
One intelligence layer, not another model
Content, compliance, and AI adoption are three problems that every enterprise is solving in parallel. We believe they are one problem wearing three coats — the problem of grounding, scoring, and auditing AI output so the business can defend it. One knowledge graph, one scoring function, one audit trail answer all three.
That position is documented across four pillars on this site — Brand governance for AI content, Brand intelligence for market research, Brand-safe content generation, and EU AI Act for marketing compliance. The same grounding, scoring, and audit approach runs through all four. The pillars are the answer by buyer question; this page is the answer by platform primitive.
Evaluate the pattern on your stack
A scoping call with the team that shipped the pattern.
No sales intermediary. We confirm use case, regulatory scope, and existing AI stack on the call. From there the typical path is a pilot Campaign grounded in your foundation documents, running alongside your current system for a measurable comparison.
Frequently asked questions
What does multi-level reasoning mean in practice?
Multi-level reasoning means an agent's answer is itself the input to a scoring agent, whose verdict is itself the input to a publishing agent. Level 1 answers. Level 2 scores. Level 3 decides. Each level is inspectable, each decision carries a score, and the system can retract weak claims or escalate to human review when confidence drops. Single-level agents cannot reason about their own conclusions; multi-level agents can.
How do your AI agents differ from Agentforce, Copilot, or LangChain agents?
All three are runtimes for building individual agents. CrawlQ Studio is not an agent runtime: it is a hosted workspace that grounds AI-assisted work in a brand knowledge graph, scores it, and keeps a tamper-evident audit trail. The difference is that governance is part of the workspace rather than something a team has to build from scratch.
Is this open source?
Partly. GraQle, a separate open-source SDK from the same team, is on PyPI under Apache-2.0: 150k+ lines of Python, 440 public test files (~7,700 test functions), public CI across Python 3.10–3.12, 140+ releases. Verify yourself at github.com/quantamixsol/graqle. CrawlQ itself does not run on GraQle; it includes a small tamper-evidence module adapted from it. The commercial layer is the brand-knowledge-graph grounding, governance scoring, and managed audit trail. The pattern is published; the data model, integration work, and regulatory alignment are what customers pay for.
Does CrawlQ plug into agent frameworks like LangChain or Agentforce?
CrawlQ Studio is a hosted workspace, not an agent framework, and we do not list packaged adapters for agent runtimes such as LangChain, LangGraph, AWS Bedrock Agents, Azure AI Foundry, or Agentforce. If your project depends on one, raise it on a scoping call and we will tell you plainly what is and is not available.
How do I verify the engineering behind these agents?
Everything verifiable is public. The GraQle repo carries 150k+ lines of Python and 440 test files (~7,700 test functions) run in public CI on Python 3.10–3.12, and it ships MultiGov-30 — a reproducible three-tier, multi-regulation governance benchmark harness (EU AI Act, GDPR, cross-regulation reasoning). Clone github.com/quantamixsol/graqle, run the suite and the harness, and read your own results. We publish the apparatus rather than a headline accuracy number, so you can measure instead of trusting.
How do I evaluate this for my organisation?
Start with a scoping call on Calendly at https://calendly.com/crawlq-ai-demo/book-a-demo-call. We confirm use case, regulatory scope, and existing stack. From there the typical path is a pilot Campaign grounded in your foundation documents, running alongside your current system for a measurable comparison. Scoping call first; commercial discussion second.
Related reading
- Best agentic AI — governed by design — the comparison buyer question
- How it works — use cases end-to-end — the workflow view
- Corporate AI training — graph-of-agents and multi-level reasoning taught as a programme
- Brand governance for AI content — the scoring pillar this page operationalises
- Quantamix Solutions — the parent organisation and the full enterprise AI portfolio