AI consulting for companies
AI that shows up in your operations, not just in a slide deck.
We automate processes with AI agents, get your data in order, and train your team. We start with one concrete process, measure the result, and leave it running.
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Same working hours as the US Your data stays under your control Measured results
We pick the tool for the job, not for a partnership
- OpenAI
- Anthropic Claude
- Google Gemini
- Microsoft Copilot
- Open models
The problem
Almost every company has tried AI. Few have it running.
The pattern repeats: a pilot that dazzles in the demo and never reaches production, data scattered across five systems, and a team using ChatGPT on their own with no clear rules.
Pilots that stay pilots
A proof of concept is not a process. If nobody connects it to your systems and measures its effect, it lives and dies in a slide deck.
Data everywhere
Spreadsheets, the ERP, the CRM, inboxes. Before a model can answer anything, the data has to be in one place and trustworthy.
Use without rules
Your team is probably already pasting customer data into public tools. It is not bad intent: there is no policy and no safe alternative.
Services
Three ways we help
Each works on its own, and they reinforce each other: clean data makes better agents, and a trained team adopts them faster.
Automation and AI agents
Agents and workflows that take on repetitive work: answering customers, reading documents, updating systems. A person approves what matters.
- Customer service on WhatsApp and web
- Invoice and document processing
- Internal assistants over your own knowledge
- Integrations with your ERP and CRM
Data and analytics
Your numbers in one place, dashboards that get read, and predictive models where the data supports them. AI without clean data is guesswork.
- Data audit and consolidation
- Dashboards and automated reports
- Demand, churn and collections forecasting
- Ask your data questions in plain language
Training and AI governance
Your team already uses AI, with or without rules. We train them to use it well and write the rules that keep your data safe.
- Hands-on workshops by role
- Sessions for leadership
- AI acceptable use policy
- Tool and vendor evaluation
Where to start
Processes where AI tends to pay for itself first
Projects that make sense for mid-sized companies. Yours may be different; that is what the diagnostic is for.
Customer service
A WhatsApp agent that answers, quotes and books
Handles common questions, checks stock and availability, and hands the conversation to a person when needed, with the full context.
Finance
Supplier invoices that file themselves
Reads the PDFs and e-invoices that arrive by email, matches them to the purchase order and posts them to the ERP. Accounting only reviews exceptions.
Operations
An assistant that knows your manuals
Answers from your own procedures, policies and contracts, and cites the document behind every answer.
Sales
Monday's report, ready without touching a spreadsheet
Sales, margin and receivables by region and rep, generated and annotated automatically, in your inbox before the meeting.
Collections
Know who to call first
A model that estimates which invoices are most likely to go late, so the collections team spends its time where it counts.
Internal service
Emails and tickets that sort themselves
Reads what lands in the support or purchasing inbox, pulls out the data, classifies it and routes it to the right person.
These are examples of the kind of work we do, not client case studies.
How we work
From idea to a running process, in short phases
Every phase has a deliverable and a quote agreed before it starts. If a phase shows it is not worth continuing, we tell you and stop there.
- 01
Conversation
Tell us what is costing you time or money. We will tell you straight whether AI is the right tool.
- 02
Diagnostic
We review processes, data and systems, and deliver use cases ranked by impact and effort, with an estimated return.
- 03
Pilot
We build the first use case on your real systems and measure it against today: hours, errors, response times.
- 04
Production and adoption
We put it into operation, train the people who use it and monitor it. You decide whether we stay on or your team takes it over.
Why StatPulse
Engineering first, enthusiasm second
We build, not just advise
We come from writing software and leading engineering teams. What we propose, we know how to put into production.
No vendor lock-in
We do not resell licenses. We choose between OpenAI, Anthropic, Google or open models on cost, quality and where your data needs to live.
Measured before and after
Every project starts with a baseline, so the result shows up in numbers rather than impressions.
Nearshore, not offshore
Costa Rica is within two hours of every mainland US time zone. We are in your meetings, not asleep during them, in English or Spanish.
If AI is not the answer to your problem, we will tell you in the first conversation.
Which process eats the most hours every week?
Tell us in 30 minutes. If we can help, we will propose a diagnostic with a defined scope. If we cannot, we will say so.