Applab
Applied artificial intelligence

AI that solves real work.

We have spent ten years designing and building digital products. Today we apply that discipline to artificial intelligence: use cases with clear return, measurable pilots and systems your team can operate.

Input
Your data, documents and systems
CRMERPPDFEmail
Model + rules + tools
The AI reasons with your context

It searches, classifies, drafts or decides within limits you define.

Human oversight
Your team validates what matters

Approvals, escalation and traceability of every action.

Output
Results measured against your baseline
−42% response time
Where it applies

Start with the area where the most time is lost today

Customer service

24/7 assistants with your policies

They answer with your information and hand off to a person when the case requires it.

Sales

Qualified leads and proposals in minutes

Automatic qualification, RFP answers and proposal drafts.

Operations

Documents that process themselves

Reading and classifying invoices, contracts and emails with human validation.

Product

AI features inside your app

Search, recommendations and summaries designed with UX, not a chat bolted on.

Data

Ask your business in plain language

Reports and answers about your metrics without waiting for the data team.

Internal team

Copilots that know your company

Assistants connected to your documents and processes for the team’s daily work.

AI services

From idea to a system in operation

Six services you can hire separately or as one journey.

Strategy and discovery

Workshop, use‑case map and roadmap with estimated return per initiative.

Agents and assistants

Conversational or autonomous, with access to your tools and escalation to people.

Intelligent automation

AI flows with human validation at critical steps, integrated with your systems.

AI in your product

AI features designed from the user experience, inside your app or platform.

Data and knowledge

We connect the AI to your documents and systems so it answers with your information, not the internet’s.

Evaluation, safety and operations

Testing, behavior limits, quality monitoring and cost control in production.

AI Sprint · Entry product

A working pilot in four weeks

No long‑term commitment. You finish the sprint with a pilot measured against your baseline and a clear decision: scale, adjust or stop.

1
Week 1

Diagnosis

Processes, available data, risks and the use case with the best return.

Deliverable: pilot plan with a success metric.

2
Weeks 2 to 4

Pilot

Build, test with real users and adjust weekly.

Deliverable: working pilot and measurement against the baseline.

3
After

Scale

Integration with your systems, security, operations and continuous improvement.

Under the model you choose: project, dedicated team or on demand.

How we do it

Five principles we don’t negotiate

Design first

The experience of the AI flow is designed before choosing the model.

Your data under your control

Enterprise providers, configured so your information does not train third‑party models.

Human oversight

Critical decisions go through a person; everything is traceable.

Measurement from day one

Every pilot has a success metric and a baseline before it starts.

No vendor lock‑in

We choose the model by cost, quality and privacy for each case, and switch when a better one appears.

What we work with
OpenAIAnthropicGoogle GeminiVercel AI SDKLangChainpgvectorNext.jsNode.jsAWSGoogle Cloud

We integrate with your CRM, ERP, email, WhatsApp and the tools your team already uses.

FAQ

What we get asked before starting

No. The first week’s diagnosis exists precisely to learn which data we have and pick a use case that works with what you already have.

Which process would you take off your team’s plate tomorrow?

Let’s start there. Thirty minutes to understand the case and tell you whether a pilot makes sense.