How we work

How we turn a workflow
into an AI system

We start by listening.

Our first step is an introductory meeting to understand your business, your workflows, and where your team believes an AI application could create value. We ask questions, pressure-test the use case, and look for practical opportunities where AI can improve visibility, reduce manual work, or support better execution.

If there is a fit, we move into a structured discovery and planning phase. Then we build around your workflows, connect with the tools you already use, and stay involved after launch through managed services and continuous improvement.

How engagements work

Two ways to work with us

Most clients follow one path, in order: a conversation first, then discovery and planning, then the build, then the relationship that keeps it running. We rarely build or run a system we have not mapped first.

The standard path

  1. 1

    Introductory Meeting

    Where we start

    We meet to understand your business, your workflows, and where your team believes an AI application could create value. We ask questions and pressure-test the use case before anyone talks about a project.

  2. 2

    Discovery & Planning

    Scoped to your project

    If there is a fit, a structured discovery maps your systems, identifies the highest-value problem, and delivers a phase-1 statement of work. You keep the plan whether or not we build together.

  3. 3

    AI System Build

    Phased milestones

    A production system built around your actual workflows. Fixed scope, milestone gates, and deliverables you can inspect at every step. No hourly billing, no open tabs.

  4. 4

    Managed Services

    Annual partnership

    We stay with the system after launch: monitoring, improvements, new use cases, and model upgrades. One predictable number, a partnership that compounds.

Every build is scoped precisely in the discovery statement of work, so nothing starts before the plan is agreed.

Governed agentic workflows

AI systems that do more than answer questions

Most AI tools answer questions. Integritas Labs builds AI systems that help move work forward. Our systems can monitor business signals, identify exceptions, pull context from connected systems, draft recommendations, route tasks, prepare approvals, and escalate blocked items, while keeping people in control through review queues, audit trails, and governance.

Human approval

People decide where judgment, risk, or sign-off matters

Audit trails

Every action and decision is recorded and reviewable

Scoped data access

Systems see only the data their workflow requires

Least privilege

Tools and integrations get the minimum access needed

Managed services

Monitored, governed, and improved after launch

The difference

What you walk away with

When organizations want AI built, they have traditionally had two options: hire a strategy consultancy, or hire a custom dev shop. Both leave you short of a running system. Integritas Labs is the new way: an engagement that ends with a working, governed system in production. Here is how they compare on the axes that matter.

What you get at the end

The new way · Integritas Labs

A production system, monitored and governed
Legacy Option 1 · Strategy consultancy:
A deck and a roadmap
Legacy Option 2 · Custom dev shop:
Code you must now operate

Your existing systems

The new way · Integritas Labs

We build around the tools you already use
Legacy Option 1 · Strategy consultancy:
A roadmap to buy more
Legacy Option 2 · Custom dev shop:
Often a rip-and-replace

Billing model

The new way · Integritas Labs

Fixed scope, milestone-gated
Legacy Option 1 · Strategy consultancy:
Retainer, scope drifts
Legacy Option 2 · Custom dev shop:
Hourly, risk sits with you

Who has sat in your seat

The new way · Integritas Labs

An operator on every project
Legacy Option 1 · Strategy consultancy:
Analysts, sometimes
Legacy Option 2 · Custom dev shop:
Engineers, rarely

After v1.0 ships

The new way · Integritas Labs

Multi-year managed partnership
Legacy Option 1 · Strategy consultancy:
Engagement ends
Legacy Option 2 · Custom dev shop:
Support contract, best effort

When the AI is unsure

The new way · Integritas Labs

Flags for a human, never guesses
Legacy Option 1 · Strategy consultancy:
Not their problem
Legacy Option 2 · Custom dev shop:
Depends who built it

Before you call

The questions every intro call starts with

Asked and answered here so the call can start further down the road.

What is delivered in discovery?

A written record you own: an inventory of your systems and data, a prioritized map of the highest-value problems, a recommended technical approach, and a phased statement of work with scope and timeline. If we build, it becomes the blueprint. If we do not, it is still a clear plan you can hand to anyone.

How does discovery work?

Discovery is sized to the project: typically weeks rather than quarters, working closely with your team. We audit the systems and data you already have, then spend real time on the use cases and the user personas who will use the system day to day, so we get the requirements right before any code is written. It is how you avoid the most expensive mistake in this work: building the wrong thing well.

How long until something is built?

Discovery runs as long as the project needs, typically weeks rather than quarters. From there, a phased build puts working software in front of you at every milestone gate rather than one reveal at the end, with a shipped first version typically inside a few months. You watch the system take shape as it is built, and nothing ships that has not been through discovery first.

Do you build AI agents?

We advise on them, and when a bespoke agent earns its keep, we build it. Often the honest answer is an existing agent platform: in that case we scope the use case, source the right provider through our partner network, and manage the rollout so it lands connected to your tools and owned by your team. Either way, you get one accountable partner for the outcome.

What if we are not a fit?

Discovery tells you that early, instead of after a failed build. If the map says off-the-shelf tools are the honest answer, the statement of work says so, and you keep the audit either way. We focus on organizations with real operational complexity, where a custom system earns its keep more than off-the-shelf tools do.

Start with a conversation

We begin with an introductory meeting to understand your business, your workflows, and where your team believes an AI system could help. If there is a clear fit, we move into structured discovery and planning, then build around the tools and processes you already use.