# One direction. Made together.

> veridive delivers AI projects in four steps: Align, Validate, Build and Embed. Each engagement starts with a baseline and an evaluation set, keeps a person in control of consequential decisions, passes an eight-point checklist before going live, and ends with training and handover. Work is done by small, senior pods of three to five people.

Your business context. Our strategy and engineering. Shared ownership of what happens next.

## Four stations. One owner at each.

Each step ends with something you can hold: a decision, evidence, a system, a practice.

### 1. Align · Days, not months

Choose a valuable workflow, name its owner, and agree how success will be measured.

- **Activities:** Stakeholder interviews, a walk through the real workflow, baseline measurement, success criteria
- **Output:** A shared starting point.

### 2. Validate · Evidence first

Test on approved data. Understand quality, cost and risk before committing to a larger build.

- **Activities:** An evaluation set from real examples, model and approach comparison, risk and access review
- **Output:** Evidence to move forward — or a clear reason not to.

### 3. Build · Weekly demos

Develop in small increments, connect to your environment, and review working software together.

- **Activities:** Weekly demos, integrations, human-review interfaces, security review
- **Output:** A system ready for acceptance.

### 4. Embed · Handover

Train your people, document the system, and agree who operates and improves it.

- **Activities:** Role-based training, champions, runbooks, handover
- **Output:** A new everyday practice.

## What we hold ourselves to.

1. **Evidence before ambition.** Every project starts with a baseline and an evaluation set. We show results on your examples, not on a demo.
2. **People stay in control.** We design where a person decides, approves or overrides — before we automate anything consequential.
3. **Model-agnostic. Your data stays yours.** We choose models on evidence and deploy where your data needs to live: your cloud, a managed environment, or on-premises.
4. **Small, senior teams.** The people you meet are the people who build. No hand-offs to a bench.
5. **Write it down.** Decisions, prompts, evaluations and runbooks are documented and handed over. Nothing depends on us remembering.
6. **Leave you stronger.** Success is when your team can run and improve the system without us.

## Bold moves. Thoughtful guardrails.

*Room for ambition. A plan for reality.*

### Who can access what?

We map approved data, processing locations and access roles. Integrations respect the permissions already attached to your information, and we align with KVKK and GDPR requirements.

### When does a person decide?

We agree the boundaries of each workflow. Consequential actions need human approval, and there’s always a fallback when the system can’t complete a task reliably.

### How will we know it works?

We define a baseline and acceptance criteria up front, evaluate representative tasks for quality, cost and latency, and repeat those checks whenever models or data change.

### Who owns what comes next?

Ownership, handover documentation, operating responsibilities and support are agreed in writing. Your team knows how to use the system — and what to do when something goes wrong.

## Before anything goes live.

Eight conditions, signed off with your owner. If one is missing, the system waits.

1. Acceptance criteria agreed and written
2. Evaluation set built from real examples
3. Data classified and access mapped
4. Human approval points defined
5. Prompt-injection and data-leak tests passed
6. Cost ceiling and alerts configured
7. Monitoring and regression checks running
8. Runbook, documentation and training delivered

## A pod, not a pyramid.

- **Engagement lead:** Owns outcomes and decisions with you
- **Applied AI engineers ×2:** Build, integrate, evaluate
- **Product designer:** Designs the workflow and the review experience
- **Your domain owner:** The person whose work changes — essential

Most engagements need three to five people from us. Never a pyramid.

## Your systems first.

- **Models:** Anthropic Claude, OpenAI GPT, Google Gemini and open-weight models such as Llama, Mistral and Qwen — chosen per task, on evidence.
- **Hosting:** Microsoft Azure, Google Cloud, AWS, or on-premises for regulated data.
- **Stack:** Your systems first. We add only what’s needed: retrieval, orchestration, evaluation and monitoring.

## Four ways to begin.

- **Executive Build Day** · 1 day
- **Discovery Sprint** · ≈2 weeks
- **Pilot to Production** · ≈6–10 weeks
- **Embedded AI Partnership** · Monthly

[Compare the formats](https://veridive.com/services/#ways-in)

## Frequently asked questions

### How does veridive run an AI project?

In four steps: Align, Validate, Build and Embed. We choose one valuable workflow and its owner, test on approved data to understand quality, cost and risk, build in small increments with weekly demos, and then train your people, document the system and hand over ownership. Each step ends with something you can hold: a decision, evidence, a system, a practice.

### How long does an AI pilot take?

About six to ten weeks for one workflow, as a Pilot to Production engagement. Alignment and validation take days, not months; building runs in weekly increments; the pilot runs next to the current process on live cases; and the last weeks cover training, runbooks and monitoring. A separate production scope follows once the agreed criteria are met.

### How do you keep people in control of AI decisions?

We design where a person decides, approves or overrides before we automate anything consequential. Each workflow has agreed boundaries: actions that cost money, touch customers or can’t be undone need human approval, and there is always a fallback when the system can’t complete a task reliably. Approval points are part of the go-live checklist.

### How do you know an AI system works?

We measure it against a baseline agreed at the start. Before building, we record volume, time per case, error rate and cost, and we build an evaluation set from real examples with expert-approved answers. The system has to meet written acceptance criteria for quality, cost and latency, and those checks are repeated whenever models or data change.

### How do you handle data privacy, KVKK and GDPR?

We map approved data, processing locations and access roles before building, and integrations respect the permissions already attached to your information. We align with KVKK and GDPR requirements and deploy where your data needs to live: your cloud, a managed environment or on-premises. Data is classified and access mapped before anything goes live.

### Who will work on our project?

A small, senior pod, usually three to five people from veridive: an engagement lead, applied AI engineers and a product designer, working with your domain owner, the person whose work changes. The people you meet are the people who build; there is no hand-off to a bench.

### What happens after launch?

Your team owns the system. Ownership, documentation, operating responsibilities and support are agreed in writing before launch. You can run it yourself with the runbook and training we hand over, or continue with an Embedded AI Partnership for monitoring, cost control and regular improvements, with capacity and service levels agreed together.

## Pick one workflow. We’ll walk it with you.

[Start a project](https://veridive.com/contact/)