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DVSN.AIDVSN AI

AI solutions

AI features and agents, built for production.

DVSN AI designs and delivers AI capabilities that operate inside a business rather than beside it: features embedded in existing applications, retrieval over internal knowledge, extraction pipelines for documents and email, and agents that take bounded actions in your systems. Every deliverable ships with an evaluation suite, guardrails, audit logging and a cost model, and the provider can be changed without rebuilding.

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Agents that act only inside the boundaries you set, with a person in the loop where it matters.

support-agent · run 8f21Completed
  1. C

    I would like a refund for order 48213. The item arrived damaged.

  2. A

    Checking the order and the refund policy before doing anything.

  3. toolorders.lookup{ "id": "48213" }
    result{ "status": "delivered", "total": 184.00, "days_since_delivery": 3 }
  4. toolpolicy.check{ "reason": "damaged", "days": 3 }
    result{ "eligible": true, "approval_required_over": 150 }
  5. A

    This refund is above my approval limit, so I am requesting a review from a manager before issuing it.

  6. Review requested · support manager notifiedPaused · waiting
  7. Manager approved the $184.00 refund.Approved
  8. toolrefunds.create{ "order": "48213", "amount": 184.00 }
    result{ "refund_id": "rf_7f2c", "eta_days": 5 }
  9. A

    Refund issued for $184.00. Confirmation sent to the customer and the ticket closed.

3 tool calls1 human review2,410 tokens6.2 s

What is included

What we build

  • LLM-powered features

    Summarization, drafting, classification and question answering embedded in the applications and workflows staff already use.

  • Knowledge retrieval (RAG)

    Retrieval-augmented answers over internal documents, tickets and policies, limited by the same access controls as the source systems.

  • Classification and extraction

    Pipelines that turn invoices, forms, contracts, tickets and email into structured data, with human review for exceptions.

  • Task-specific agents

    Agents for support triage, operations and back-office processes that use tools against your APIs and SaaS platforms under defined limits.

  • MCP servers and integrations

    Model Context Protocol servers that expose your systems to agents and assistants securely, with scoped permissions and logging.

  • Evaluation, guardrails and cost control

    Test suites for quality and safety, monitoring for drift, and per-feature cost budgets so spend stays predictable.

How it is delivered

How we work

01

Provider and model selection

The model is chosen for the task, the data-handling requirements and the cost, and the system is built so the choice can change.

  • Anthropic, OpenAI, Cloudflare Workers AI, Amazon Bedrock and Google Vertex AI
  • Data residency, retention and training-use terms reviewed and documented per provider
  • Abstraction layer so models and providers are swapped by configuration
  • Cost modelling per feature before build, measured after release
A roadmap being planned on a whiteboard
02

Agents with defined boundaries

Agents are given specific tools, specific permissions and a specific job. Consequential actions wait for a person.

  • Tool use and function calling against internal APIs and SaaS platforms
  • Human-in-the-loop approval for actions that change money, access or records
  • Scoped credentials per agent and per task, never shared service accounts
  • Full audit trail of prompts, tool calls and outcomes
03

Evaluation and monitoring

AI features are tested like software and monitored like infrastructure, so quality is measured rather than assumed.

  • Evaluation suites built from real cases with agreed pass criteria
  • Guardrails for prompt injection, data leakage and unsupported requests
  • Monitoring for quality drift, latency and cost per request
  • Regular review of failure cases with the people who use the feature
Agent runs per hour
Last 24 hours
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843
Runs today
37
Approvals waited
4.8 s
p95 latency
$0.011
Cost per run

Sample data

04

Data handling and access control

AI features inherit the organization's security model. Retrieval returns only what the requesting user could already see.

  • Identity-aware retrieval tied to Microsoft 365, Google Workspace or the application's permissions
  • No client data used to train shared models under the provider agreements selected
  • Encryption, logging and retention aligned with the compliance frameworks in scope
  • Documented data flows for auditors, insurers and customers

What sets DVSN AI apart

01
Production, not demonstration
Every AI feature is delivered with evaluation, monitoring, cost limits and a support agreement, the same way DVSN AI delivers any other system.
02
Integrated with your operations
The team that builds the agent also runs the identity, network and device platform it operates in, so access control and logging are part of the design.
03
Provider-independent
Systems are built so the model or provider can be changed by configuration when capability, cost or terms change.

FAQ

Frequently asked questions

What are you waiting for? Let's get started!

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