ERP
Operational and transactional records already maintained.
How HENIOCHOS works
From the data you already have to answers management can trust.
HENIOCHOS works with the business data your existing systems already produce. That data is validated, structured around the business rules your company approves, and then turned into analysis, findings and answers management can act on — without replacing anything you run today.
01
Data
ERP · POS · CRM · Accounting · Spreadsheets · Other sources
02
Validate
Validated data · readiness checks
03
Analyze
Business rules · analysis & findings
04
Understand
Management answers · follow-up questions
Data is connected or uploaded depending on the systems and setup in each business. Not every source supports a direct connection.
Step 1 — Bring the data you already use
Start with the systems already running your business.
Depending on the setup, data is either connected from a source system or uploaded as regular exports. Both routes are normal. What matters is that the data reaching HENIOCHOS is the data the business already relies on.
POS
Sales activity captured at the point of transaction.
CRM
Customer, client or pipeline records where relevant.
Accounting
Financial records used for reporting and reconciliation.
Spreadsheets
Management files still used alongside core systems.
Other operational data
Additional sources agreed during onboarding.
- No operational system is replaced or migrated.
- Onboarding agrees exactly which data is required, and in what form.
- Only data relevant to the agreed analysis is used.
- Source structure differs between businesses, so the setup is agreed case by case.
Step 2 — Validate before analysis
Before HENIOCHOS analyzes the business, it checks the data.
Validation is deliberately placed before analysis. Issues in the underlying data are surfaced first, so a figure is only presented once it is understood what it is built on.
A polished answer built on unreliable data is still a bad answer.
Validation reduces avoidable error and makes data quality visible. It does not claim to catch everything — where something cannot be verified, it is reported as such rather than assumed.
HENIOCHOS · Data readiness
Last 12 monthsRaw business data
Connected or uploaded from existing systems
Validation checks
Structure, completeness, consistency
Approved dataset
Used as the basis for analysis
Required fields presentExpected structure recognised
Missing or unusable values12 rows held for review
Duplicate or conflicting recordsNo duplicates detected
Period and comparison consistencyPeriods align with prior year
Illustrative interface. Checks are configured per business and per data source.
- Expected structure
- Required fields and the shape of each source are checked against what the agreed analysis needs.
- Missing or unusable values
- Gaps and values that cannot be interpreted are identified rather than quietly averaged away.
- Duplicates and inconsistencies
- Repeated or conflicting records are flagged where the data allows them to be detected.
- Period consistency
- Periods and comparisons are checked so that a movement is measured against a comparable base.
- Readiness status
- Each dataset carries a readiness status, so management knows what the figures rest on.
Step 3 — Apply the business rules
The business defines the rules. HENIOCHOS follows them.
Two companies rarely define the same metric the same way. Margin, an active customer, a reporting month or a material change each mean something specific inside a business. The definitions your management team approves are what shape the analysis.
Definitions
- KPI definitions
- Agreed with the business
- Comparison logic
- Period vs prior period
Structure
- Reporting hierarchy
- Organisation → group → item
- Fiscal periods
- Company reporting calendar
Attention & access
- Materiality thresholds
- Set by management
- Access-controlled views
- Scoped per role
The AI does not invent KPI definitions. Approved business rules and analytical logic produce the figures; the AI explains them and helps management interrogate the analysis.
Step 4 — Structure and analyze
Turn validated data into a structured view of performance.
Analysis follows the structure configured for your business: headline KPIs, trends and period comparisons, segments and dimensions, and drill-downs into the level where a movement actually sits.
01
KPI
A headline measure is calculated on the approved dataset.
02
Movement
It is compared against the periods your reporting uses.
03
Segment
The movement is split across the dimensions you configured.
04
Drill-down
The segment is followed to the level where the change sits.
05
Finding
Material movements are summarised for management review.
What is surfaced
Material movements, anomalies against configured thresholds, and priority items ranked so management attention goes to what carries weight.
How it is worded
Where the data shows a movement or an association, it is described as such. Cause is not asserted unless the underlying data supports it.
Step 5 — The answer, with the evidence
Management sees what changed — and can ask what to look at next.
Questions are asked in plain language and answered on the approved dataset. Each answer separates what is established fact, what is inference, and what cannot be answered because the data is not there.
- Fact — figures traced back to the approved analysis.
- Inference — a reading of the movement, labelled as interpretation.
- Unknown — stated plainly when required data is missing.
HENIOCHOS provides analysis and decision support. The decision, and the judgement around it, stays with your management team.
HENIOCHOS · AI Analyst
Last 12 monthsGross profit grew slower than revenue this quarter. What is behind it?
Management answer
Revenue rose faster than gross profit because average value per transaction fell while volume grew. The gap concentrates in two product groups.
Underlying figures · last 12 months
Revenue
€ 4.82M
+6.4% vs prior period
Gross profit
€ 1.29M
+3.1% vs prior period
Average value
€ 37.6
−1.8% vs prior period
Supporting drill-down
- Total revenue
- Channel
- Product group
- Transactions
Fact — figures traced to approved data.
Inference — mix shift as likely driver.
Unknown — supplier cost data not loaded.
Illustrative interface. Figures shown are examples, not customer data.
Each reporting cycle
A repeatable management workflow.
After onboarding, each reporting cycle follows the same agreed path. The analysis is not rebuilt from scratch every period, and the definitions behind the figures stay consistent unless the business changes them.
- 01
New period data arrives
The agreed sources are connected or uploaded for the new period.
- 02
Validation checks run
Structure, completeness and consistency are checked before anything is used.
- 03
Approved rules are applied
Your definitions, periods, hierarchy and thresholds shape the calculation.
- 04
Dashboards and findings refresh
Reporting and prioritized findings update on the approved dataset.
- 05
Management reviews and asks
The team works through priorities and follows up with questions.
Analysis refreshes with each agreed data cycle, at the frequency your reporting requires.
Onboarding & configuration
Configured around the business before it becomes useful.
Onboarding is a structured sequence, run together with your team. Its length depends on the number of sources, the state of the data and how quickly definitions can be agreed.
- 01
Understand the objectives
What management needs to see, decide and follow up on.
- 02
Identify source data
Which systems and files hold the data the analysis requires.
- 03
Agree definitions and structure
KPI definitions, reporting periods and hierarchy are set with the business.
- 04
Validate sample data
A sample is checked so structure and quality issues surface early.
- 05
Configure rules and views
Thresholds, comparisons and access-controlled views are configured.
- 06
Review outputs with management
Findings and dashboards are reviewed and adjusted before rollout.
- 07
Move into recurring use
The agreed cycle becomes the routine management reporting process.
Clarity
What HENIOCHOS does — and does not do.
HENIOCHOS does
- Analyzes approved business data
- Structures management reporting
- Surfaces material movements and priorities
- Provides drill-downs into the underlying detail
- Answers questions grounded in approved data
- Supports management decisions with evidence
HENIOCHOS does not
- Replace your ERP, POS, CRM or accounting system
- Invent data that is missing from the sources
- Make causal claims the data does not support
- Make management decisions autonomously
- Override or ignore configured business rules
Validate before analysis
Data quality is checked before figures are relied on.
Rules before AI
Approved business logic produces the numbers, not the model.
Evidence with material answers
Material statements come with the figures behind them.
Human decision-making in control
Management holds the judgement and the decision.