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dhino Trust: Reliable AI answers from real enterprise data

Finally, Copilot and custom agents work with your real data. Pre-defined templates ensure agents deliver exact results, no hallucinations, no guessing.

What dhino Trust gives you

Templates carry business logic. AI asks questions. dhino returns governed, accurate results.

Deterministic answers

AI doesn't generate SQL or interpret schemas. Templates define the logic. Same question, same answer, every time.

Built-in governance

AI only sees what it's allowed to see. Role-based access, audit trails, and policy enforcement by default.

AI-agnostic

Works with Copilot today. Compatible with any MCP-enabled AI. No vendor lock-in.

Lower cost, better performance

Fewer tokens wasted on context. Queries routed to deterministic logic. Predictable performance at scale.

Usage Scenarios

Copilot with real pipeline data

The sales director asks Copilot: "What's our Q4 pipeline?" Instead of guessing from context, Copilot calls dhino's "Pipeline Summary" template.

  • • Exact numbers: $4.2M weighted, 127 opportunities
  • • 23 closing this month
  • • Same answer, every time

Result: Executive decisions based on real numbers, not AI estimates.

Explore the full pipeline data scenario →

Customer service agent with account data

An internal support chatbot needs to answer "What's the customer's contract renewal date?" dhino provides the answer from CRM with proper access controls.

  • • Agent sees only authorized data
  • • Deterministic: same question, same answer
  • • Proper governance enforced

Result: Faster support, no data leakage.

Explore the full customer service scenario →

Consistent executive dashboards

Multiple AI-powered reporting tools query the same metrics. Without dhino, each tool might calculate "active customers" differently.

  • • All tools use the same template
  • • CFO and CMO see the same numbers
  • • No conflicting reports

Result: One version of truth across all AI tools.

Explore the full cross-department scenario →

Why AI gets your data wrong

  • ✕ Enterprise data is complex. AI guesses instead of calculating
  • ✕ Business definitions live in people's heads, not schemas
  • ✕ Databases weren't designed for AI interpretation
  • ✕ Same question, different answers. 45% cite inaccuracy as #1 AI barrier
  • ✕ Direct database access is risky and uncontrolled

How dhino makes AI reliable

Instead of letting AI "figure out" your data, dhino tells AI exactly how your data works.

Stage 1: Understanding

AI interprets the user's intent and selects the right template.

Stage 2: Execution

dhino executes pre-defined, deterministic logic. No guessing.

Built on MCP

dhino uses the Model Context Protocol (MCP), the emerging open standard for AI-data connections. Compatible with Microsoft Copilot, custom agents, and the broader AI ecosystem.

MCP is the standard for giving AI controlled access to tools and data. dhino is MCP-native.

Who benefits from dhino Trust

Copilot Users

Make Microsoft Copilot actually work with your enterprise data

Custom Agent Builders

Build internal chatbots and assistants that don't hallucinate

AI Platform Owners

Deploy AI at scale with proper governance and auditability

See governed AI at scale →

IT & Security Teams

Enable AI adoption without compromising data security

Frequently asked questions about dhino Trust

How does dhino prevent AI hallucinations on enterprise data?
dhino uses pre-defined templates that carry business logic, relationships, and rules. AI does not generate SQL, interpret schemas, or invent definitions. Instead, AI selects the right template, and dhino executes deterministic logic. Same question, same answer, every time. This separation of understanding from execution eliminates hallucinations on structured data queries that run through dhino templates.
What is Model Context Protocol and how does dhino use it?
Model Context Protocol (MCP) is the emerging open standard for connecting AI to tools and data sources. dhino is built on MCP, so any MCP-enabled AI agent can connect to dhino and access enterprise data through governed templates. This includes Microsoft Copilot, custom agents, and other AI tools in the MCP ecosystem.
Does dhino work with Microsoft Copilot?
Yes. dhino works with Microsoft Copilot today. When Copilot needs enterprise data, it calls dhino templates instead of guessing from context. Copilot returns accurate, governed results based on your actual business data and definitions.
Can dhino work with AI models other than Copilot?
Yes. dhino is AI-agnostic by design. It is built on Model Context Protocol (MCP), an open standard, so it is compatible with any MCP-enabled AI agent or assistant. There is no vendor lock-in. You can use one data layer for all your AI tools.
What is the difference between dhino and direct AI-to-database access?
Direct AI-to-database access is non-deterministic and high-risk. The AI guesses schema structure and business definitions. dhino is the opposite: deterministic execution through pre-defined templates, role-based governance, and business logic defined once by the people who know the data. The AI asks, dhino answers with exact, governed results.
How does dhino handle data governance for AI agents?
Governance is built into the platform, not added on top. Every AI request goes through the same governance controls: role-based access, audit trails, and policy enforcement. AI only sees data it is authorized to see. Sensitive data stays protected automatically with every request.
What are dhino templates and why do they matter for AI?
Templates are pre-defined, tested data operations that carry business logic, relationships, guardrails, and performance constraints. They are created once by IT or data teams and reused by every consumer, including AI agents. When an AI agent needs data, it calls a template instead of generating its own query. This is why dhino returns correct, consistent results instead of AI guesses.
How does dhino reduce AI operational costs?
dhino reduces costs in two ways. First, fewer tokens are wasted on context because templates carry the business logic, so AI does not need to process large schema descriptions. Second, queries are routed to deterministic logic instead of expensive AI inference on structured data tasks. The result is faster answers, lower token usage, and predictable performance at scale.

Ready to make AI actually work?

See how dhino AI turns your data from a risk into a reliable interface for enterprise AI. Deterministic answers, every time.