Readiness to organize your data
0 % · 0 of 6 controls that applyFirst missing: report chosen and source access
Not yet: the basics for working with real data are missing.
Higher-risk controls weigh more in the score.
Data consultancy · Ecuador
Data consultancy in Ecuador: what to organize first
Start with the cross-check that takes the most hours to build by hand: ERP sales against SRI-authorized receipts and each area’s spreadsheets, in a database that updates itself. Defining each figure and deciding with it stays with your management.
Start now if
- Each area shows up with its own number
- Reports are built by pasting spreadsheets into Excel
- The ERP lets you export or read its data
Not yet, if
- Nobody can decide which figure is official
- Key data only exists on paper
Real process · example timings
Monthly close: ERP, SRI receipts and spreadsheets
Today
- 1Export ERP sales and download the SRI receipts45 min
- 2Unify customer and product codes by hand2 h
- 3Ask about figures that do not add up and wait+2 days
- 4Cross the sources and build the report2 h 15 min
With AI
- 1ERP, receipts and spreadsheets load nightlyauto
- 2Checks flag duplicates and sales without a receiptauto
- 3AI explains each alert and the analyst reviews only those45 min
- 4The report comes from the database with agreed definitions15 min
Today≈ 5 h of work + 2 days waiting
With AI≈ 1 h
Ready for a pilot?
Tick what you already have; heavier items stall a pilot more. Nothing is sent.
What it costs and when it pays back
Hours freed by AI minus what it costs to run. Examples are marked; public prices link to their source.
AI model
Estimated floor: the 2026 SBU (USD 482, Ministry of Labor) ÷ 173.33 h (40 h × 52 weeks ÷ 12) ≈ USD 2.78. Typical real cost (example): ×2.5, a salary of two basic wages plus employer contributions and bonuses. Source
Today250 h/month
Automated50 h/month
- Value of hours freed per month200 h × USD 2.78
- USD 556
- AI model public price50 reports × USD 0.0040 (example: 30,000 input + 2,000 output tokens; gpt-6-luna at USD 0.10 / 0.50 per million) · OpenAI · API pricing
- −USD 0.20
- Platform, monitoring and support exampleTypical monthly range
- −USD 100 to −USD 250
- Net savings per month
- USD 306 to USD 456
- Source connections, organized database, checks and first dashboard typical range · exampleOne-off, up front
- USD 2,500 to USD 6,000
- Months to pay back the investment
- 5.5 to 20 months
Tokens: the unit AI providers bill for the text the model reads (input) and writes (output). OpenAI puts 100 tokens at about 75 English words.
Range: the lower figure uses low costs, the higher one high costs. Public prices checked on 11 Oct 2026.
estimated4-week pilot
A typical plan; real timing depends on your systems.
Pilot · weeks 1 to 4
- Week 1Pick and measureOne report that crosses ERP, receipts and spreadsheets, with its current hours.KPI: Signed baseline
- Week 2Sources and definitionsAccess to the sources and a written definition of each key figure.KPI: Definitions approved
- Week 3Database and checksDaily load into the organized database and alerts when something does not add up.KPI: Figures that match the ERP
- Week 4Real useManagement uses the new report and it is compared to the baseline.KPI: Hours per report vs. baseline
Before you start
Frequently asked questions
What exactly does a data consultancy do?
It connects a company’s scattered data to the decisions made with it: it links the sources (ERP, invoicing, CRM, spreadsheets), builds reliable pipelines, delivers dashboards and metrics that answer business questions and, when the case justifies it, adds AI agents that use that data with scoped permissions. The deliverable is not a report: it is a system that keeps running.
How much does a data project cost in Ecuador?
It depends on scope: a dashboard on a single source is not the same as integrating several systems with an AI agent on top. That is why we do not publish a single price. The starting point is a free 30-minute assessment where we review your sources, your team and the highest-return use case, and you leave with a concrete estimate.
Do you work with companies outside Quito and Guayaquil?
Yes. Data work is remote by nature: scoped access to your systems, weekly meetings and versioned deliverables. The company’s city does not change the method.
Do we need clean data before we start?
No. Almost no company starts with clean data: part of the assessment is measuring the real state of your sources and deciding what to fix first. A scoped pilot with imperfect but useful data is worth more than a year of cleanup that ships nothing.
How do you protect the confidentiality of our information?
With least-privilege access from day one: read-only access to what is strictly needed, a log of every query, credentials in secret managers and confidentiality agreements signed before any data is touched. When an AI agent uses your documents, it works on indexed copies with access control, not on your live systems.