We take AI from the pilot stage into daily operations: agents that answer and qualify customers, chatbots wired into your CRM and workflows that strip out repetitive work. Every use case starts with a metric and a review date.
An AI automation agency designs and deploys AI inside a company's workflows: agents and chatbots that serve and qualify customers, automations that connect systems, and generative AI for content. DataLab does it from Colombia, in a time zone that overlaps the US workday, with measurable use cases, CRM integration and its own AI platform, Krea.ia.
The problem
Problems we solve
✕ 01
Your team does robot work
Copying data between systems, building reports by hand and answering the same questions every day eats hours that should go to selling or solving complex cases.
✕ 02
Customers left waiting
WhatsApp and website messages arrive after hours or in spikes your team can't keep up with. A customer who waits is a customer who buys elsewhere.
✕ 03
AI pilots stuck in demo mode
Someone tried a tool, the meeting loved it and it stopped there: no company data, no integration with your systems and no metric to justify scaling it.
✕ 04
Content demand outpaces your designers
Every promotion, season and channel needs new assets, and the design team becomes marketing's bottleneck.
What's included
What you get with AI automation
Assessment and use-case map
We review processes, data and systems, then rank use cases by impact, effort and risk. You know where to start and what to expect.
AI agents for business
Agents that look up your information, qualify leads, book meetings or draft documents, following the rules your team sets.
Business chatbots for web and WhatsApp
Automated answers grounded in your content, hand-off to a human when needed and every conversation logged in your CRM.
Business process automation
Workflows that connect your CRM, ERP, email, forms and spreadsheets through APIs, so data moves on its own with no manual re-entry.
Generative AI content with Krea.ia
Our platform, trained on your brand's visual library, produces assets for promotions, banners and carousels in minutes.
Guardrails and human review
We define which data the AI can use, which decisions need human approval and how every action is logged for audit.
Results dashboard
We track hours saved, requests resolved, qualified leads and conversions against the baseline set during the assessment.
Training and documentation
Your team learns to run, tune and supervise every automation, with playbooks and hands-on sessions.
How we work
How we work: AI automation, step by step
Weeks 1–2
Assessment
We map the process, measure how much time and money it costs today and check which data exists and where it lives.
Week 3
Use-case design
We define the workflow, data sources, rules, the exact point where a human steps in and the success metric.
Weeks 4–8
Controlled pilot
The agent or automation goes live on a single channel or with a limited group of users, measured against the baseline.
End of pilot
Review and scale
With data in hand, we decide together: scale to more channels or teams, adjust, or switch it off. Nothing grows until it has proven its value.
Monthly
Operations and continuous improvement
We monitor answers, errors and model usage costs, update the agent's knowledge and propose the next use case.
Stage timings are estimates: your project's schedule is set in the proposal.
Confidential clientDataLab built the custom software a confidential insurance agency uses to manage more than 25,000 policies, calculate commissions for 300 sales agents and handle renewals. The three processes that drive its revenue now run in one tool, built around how the agency operates.Tovar ArquitecturaIn 12 months, DataLab built the construction supervision and technical oversight SaaS platform for Tovar Arquitectura, a Colombian architecture and construction company. With every process recorded in one system, the result is full control and traceability across processes and faster delivery of technical reports.StarkapitalIn 10 months, DataLab built the SaaS core for Starkapital, a Colombian fintech that finances taxis and other public transport vehicles. The platform covers every loan from origination to portfolio management and now handles more than COP 1 billion a month in loan processing.
Krea.ia, our AI-powered design production platform, is trained on each brand's visual library. We don't sell theory: we use AI every day.
Software and data under one roof
We build custom platforms and lending cores, and implement CRMs like HubSpot, ActiveCampaign and RD Station. Wiring AI into your stack doesn't depend on a third party.
Critical systems in production
We built the software an insurance agency uses to manage 25,000+ policies and calculate commissions for 300 sales agents, Starkapital's lending core that handles more than COP 1 billion a month in lending operations, and Tovar Arquitectura's construction oversight platform.
Nearshore, in your time zone
Bogotá keeps UTC−5 all year: on New York time in winter, an hour behind it in summer and two to three hours ahead of California. Pilot reviews, fixes and readouts happen live during your business day.
Platforms and tools
Krea.ia
Large language models (LLMs)
WhatsApp Business
HubSpot
ActiveCampaign
RD Station
Python
Node.js
REST APIs & webhooks
AWS
Google Analytics 4
Engagement models
Ways to engage and how pricing works
Fixed-scope pilot
One use case, one channel and one metric, with a set price and timeline. The lowest-risk way to start.
Implementation and monthly operations
We integrate, run and improve your agents and automations for a monthly fee, with a regular results review.
Krea.ia subscription
Access to Krea.ia, trained on your brand's visual library to create promotional assets, banners and carousels in minutes; terms are set in the proposal.
What drives the cost?
Every proposal is built around your goals, scope and timeline. We send you a detailed quote at no cost.
Number of use cases, channels (web, WhatsApp, email) and teams involved.
Integrations with your CRM, ERP, databases or in-house systems.
Volume of conversations, documents or images, which drives AI model usage costs.
Quality and structure of your data, and whether it needs cleanup first.
Security, privacy and human-approval requirements at each step.
How much ongoing operation and improvement you need after the pilot.
AI agents, chatbots or automations: what your business actually needs
An automation follows fixed rules: when a form comes in, it creates the contact in your CRM and alerts a sales rep. A chatbot holds a conversation and answers from a defined knowledge base. An AI agent goes one step further: it understands the request, checks several sources, picks the right tool and completes tasks within the limits your company sets.
You don't always need an agent. Often the biggest savings come from a simple automation that eliminates manual re-entry between two systems. That's why DataLab's assessment starts with the process, not the technology: we measure what the task costs today, choose the simplest solution that solves it and make clear which part stays with a person. A rule of thumb: if the task always follows the same steps, a plain automation is enough; if it requires interpreting language or documents, that's where AI earns its place. And if the bottleneck is a spreadsheet the whole team shares, you may not need an agent at all but a small platform: our AI MVP development service turns a shared Excel file into a multi-user app.
How to choose an AI automation agency that ships
A polished demo impresses in any meeting. The hard part is making AI work with your data, inside your systems and under your business rules. Before you hire, ask whether the agency has built software that runs in production, how it connects AI to your CRM or ERP, how it measures results and what happens when the model gets something wrong. If an agency can't show you a before-and-after dashboard, what you're buying is an experiment, not an implementation.
DataLab answers with a track record. We build custom software such as SaaS platforms and fintech cores, run Krea.ia as our own product and work with Scrum and an ISO 9001:2015-certified quality management system. Our AI automation services start with a baseline and close each cycle with a data readout: hours saved, requests resolved or leads qualified, depending on the use case.
Krea.ia: generative AI trained on your brand
Krea.ia is DataLab's AI-powered design production platform. It is trained on each brand's visual library and, starting from a product photo, generates new assets that keep the product intact while changing the context: a season, a promotion or a holiday. Assets are exported lightweight for the web, and your reference images, generated pieces and history live in dedicated storage.
Design, marketing and retail teams create banners, stories and carousels in minutes, with quick resizing for web, social and ads, and Canva-compatible exports for fine-tuning copy and logos. It runs on a monthly platform fee that covers technical support, storage, unlimited users and model training, plus image packs purchased as you use them. Your designers stop producing endless variations and focus on campaign ideas instead.
Frequently asked questions
AI automation: frequently asked questions
01
What's the difference between a chatbot and an AI agent?
A chatbot holds a conversation and answers questions from a defined knowledge base. An AI agent can also check multiple sources, use tools and complete tasks, like creating a CRM record or booking a meeting, always within the permissions your company grants. DataLab recommends one or the other based on the process, not the hype.
02
How much do AI automation services cost?
It depends on the number of use cases, integrations, conversation or document volume and the quality of your data. DataLab doesn't publish fixed rates. We recommend starting with a fixed-scope pilot, with a set price and timeline, and scaling only once the data shows a return on the investment.
03
What happens to our data and privacy?
Before connecting any model, we agree with your team on which data the AI may use, what gets anonymized and where it is stored. We sign an NDA, handle personal data under Colombia's data protection law (Law 1581 of 2012) and give each integration only the permissions it needs to work.
04
Will AI replace my team?
The goal is to take repetitive work off your team's plate, not to replace their judgment. In every workflow we define what the AI resolves on its own, what it proposes for a person to approve and which cases go straight to a human. Your team gets more time to sell, handle complex cases and improve service.
05
Can it connect to WhatsApp and our CRM?
Yes. We integrate chatbots and agents with WhatsApp Business, web forms and email, and log every conversation in your CRM, whether HubSpot, ActiveCampaign, RD Station or any other system with an API. When the agent qualifies a lead, the sales rep gets the full context and doesn't have to ask the same questions again.
06
How soon will we see results?
A well-scoped pilot goes live in weeks, not months, because it targets a single process and a single channel. The metric, baseline and review date are set before work begins. With that readout, we decide together whether to scale, adjust or switch off the automation.
07
What if the AI gets something wrong?
Any AI model can make mistakes, so we design with guardrails. The agent only accesses authorized information, sensitive actions require human approval and every response is logged for review. When we catch an error, we adjust the instructions or knowledge base and test the fix before it goes back to production.
08
Why work with a nearshore AI automation agency in Colombia?
Because AI projects need fast feedback loops. DataLab's team in Bogotá works on UTC−5, which overlaps most of the US business day, so pilot reviews, prompt adjustments and urgent fixes happen live. You get a partner that also builds custom software and runs marketing, with the working language agreed in your proposal.
Which task would you take off your team's plate today?
Tell us about the process that eats the most time. We'll review it with you, estimate the potential savings and propose a pilot with a clear metric, scope and timeline.
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