---
title: "AI Solutions & Agents | DVSN AI"
description: "Production features built on large language models, from document retrieval and extraction to task-specific agents with tool use, human approval and cost controls."
canonical: https://dvsn.ai/solutions/ai-solutions-agents
---

# AI Solutions & Agents

> 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.

## 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 we work

### 1. 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

### 2. 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

### 3. 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

### 4. 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

- **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.
- **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.
- **Provider-independent.** Systems are built so the model or provider can be changed by configuration when capability, cost or terms change.

## Outcomes

- AI features embedded in existing systems with measured quality and cost
- Documents, tickets and email converted to structured data with exceptions routed to people
- Agents that act within defined limits and leave a complete audit trail
- A provider strategy that can change without a rebuild

## Frequently asked questions

### Which AI providers do you work with?

Anthropic, OpenAI, Cloudflare Workers AI, Amazon Bedrock and Google Vertex AI. Provider selection is based on the task, data-handling requirements and cost, and systems are built so the provider can be changed.

### How is client data handled in AI features?

Data is processed under the provider agreements the client approves, retrieval is limited by the same access controls as the source systems, and no client data is used to train shared models.

### What is an AI agent in practice?

A bounded program that uses a language model to decide which of a fixed set of tools to call, such as looking up an order, drafting a reply or creating a ticket, within permissions the client sets. Actions with consequences require a person's approval.

### How do you measure whether an AI feature works?

An evaluation suite built from real cases with agreed pass criteria runs before release and on every change, and quality, latency and cost are monitored in production.

### What does an AI engagement cost?

Discovery and evaluation design are fixed price. Build is phased or delivered under an engineering retainer, and model usage is budgeted per feature with alerts, so operating cost is visible from the first release.

## Related

- [Custom Software Development](https://dvsn.ai/solutions/custom-software-development)
- [Integrations & APIs](https://dvsn.ai/solutions/integrations-apis)
- [Business Automation](https://dvsn.ai/solutions/business-automation)
- [Cloud Security](https://dvsn.ai/services/cloud-security)

## Contact

Book an introduction discovery call today. Contact: https://dvsn.ai/contact