Understand the business before anything is automated

Onboarding that asks only what it needs

• A step-by-step profile of your business: industry and model, products and services, customers, locations, departments, goals, the software and channels you already use, and how much you want automated.
• Only the automation level is required; everything else can be filled in later, and the rest of the product adapts to what is known.

In Skofa: Onboarding

A discovery report you can audit

• A deterministic rules engine turns your profile and live business data into findings: department and workflow maps, technology and data-source maps, revenue opportunities, bottlenecks, automation opportunities, risks and recommended AI employees.
• An additional AI analysis pass may propose more findings, but each must cite evidence from your own data; it is shown in its own section and labelled as an AI assumption unless everything it cites is verified.
• Every finding carries its provenance — known fact, user-provided, inferred, AI assumption or unverified — and you accept or dismiss each one.
• If the AI pass is unavailable (no model configured, budget reached, invalid output), the report is still produced from the rules engine and says so.

In Skofa: Discovery

A radar for what changed

• Your dashboard surfaces real diagnostics without waiting for a report: revenue decline from your filed reports, elevated AI cost, stalled goals, workflow failure rates and statistical anomalies.
• Any of those findings can be turned into a goal for an AI employee in one step, by an owner or admin.

In Skofa: Dashboard

Evidence before conclusions

• Experiments record a real baseline (revenue, AI cost, Skofa spend, workflow failure rate and more), run a mission, then measure the outcome — and are reported as inconclusive when the sample is too small.
• External market intelligence is retrieved with its source, checked for conflicting or stale claims, and combined with your own data — never invented.
• A what-if simulator labels every line as fact, assumption, estimate or prediction.
• The executive view brings together experiment conclusions, patterns learned from past outcomes, and board-style reviews in which several AI perspectives deliberate on a question.

In Skofa: Discovery → Experiments, Market intelligence, Simulation, Executive

Targets measured by Skofa, not guessed

• Give the business an objective — sales, customers served, enquiries, new people who contacted you, money invoiced in your connected software, or posts published — with a target, a deadline and an AI budget.
• Your CEO plans which AI employees do what; nothing starts until you approve, and every week it reviews progress against numbers Skofa measured itself, with how complete each number is.

In Skofa: Objectives

Numbers that come from your own records

• Performance and financial views are built from the daily reports you file and the invoices you have actually paid; AI spend is shown in US dollars, exactly as model providers bill it, and is never added to cedi amounts.
• AI cost is tracked per model, per AI employee and per goal, against the monthly budget you set.
• With at least a week of daily reports, the dashboard shows where your sales trend is heading — a straight-line projection with its confidence stated, not a guess dressed up as a forecast.

In Skofa: Performance, Financials, Analytics, Dashboard

What is not there yet

• Sales projections are straight-line trends from your daily reports; they do not account for seasons, promotions or events you have not recorded.
• Objectives can only be measured on what Skofa records: your daily reports, Inbox, posts and the invoices your connected software syncs. Anything else is tracked as work done, not as a number.