Your revenue modeled.Anomalies caught before they hit.Steer ahead.
ForecastIQ analyzes your sales history, builds adaptive predictive models and alerts you in real time as soon as an anomaly threatens your growth trajectory.
- forecast accuracy
- 92%
- prediction horizon
- 90d
- anomaly detection
- 48h
- automated
- 100%
Orders of magnitude the installation aims for; they are measured on your premises during the audit, on your volumes.
Fully automated, nothing to manage.
Adaptive ML models
Our algorithms learn from your history and adapt to seasonalities, campaigns and unusual events for forecasts that stay relevant.
Instant anomaly alerts
As soon as a metric diverges significantly from forecast, ForecastIQ alerts your team before end of day, with context and recommendations.
90-day forecasts
Plan resources, targets and cash with 3-month forecasts updated continuously, broken down by product, region and channel.
Live in days, not months.
- 01
Connect your sales data
CRM, ERP, Stripe, Salesforce, ForecastIQ ingests your transaction history in minutes. No data prep required.
- 02
AI calibrates its models
ForecastIQ detects seasonalities, trends and correlations. Models calibrate to your specific context in under 24 hours.
- 03
Continuous forecasts and alerts
Every day, your forecasts are updated. If an anomaly is detected, an alert is sent immediately to your team with root cause analysis.
The pipeline behind this offer.
Postgres
MongoDB
MySQL
AirbyteAirbyteMeltanodbtPostgreSQLDuckDB
ClickHouseIceberg
MetabaseSuperset
GrafanaWe plug the agent into the tools you already use.
GoogleBigQuery
Snowflake
PowerBI
Slack
Groq
Nothing to learn: we configure and operate these connections for you.
Your revenue forecasts are wrong. You know it. You keep submitting them.
The 28th of the month. Your CFO opens the Q4 forecast Sales shared three weeks ago. 4.2M EUR. But he sees Stripe is at 3.1M, two enterprise deals slipped to Q1, and the DACH cohort has been quietly declining for 17 days. He'll have to re-warn the board Tuesday, ask to revisit the hiring plan, and explain why nobody saw the erosion coming. The rep who submitted the forecast is acting in good faith, they simply didn't have the tools to model the real trajectory.
PwC measured in 2025 that manual B2B forecasts are on average 28% above actuals on high-seasonality quarters. Gartner indicates that companies with AI forecasting reach 92% accuracy at 90 days versus 64% for manual forecasts, and detect revenue anomalies 4 weeks earlier. Forrester estimates the value of an accurate forecast at 8% of total revenue for a post-Series B scale-up.
Wikolabs installs AI systems built on open-source models, on your premises, paid once. No black box: your data, your prompts and your history stay in your infrastructure. We quote at a fixed price or on time and materials, never per ticket or per token, and we say before you sign what works and what does not yet.
Concretely: ForecastIQ ingests your Stripe, Salesforce or ERP history in minutes, calibrates its adaptive ML models on your seasonalities and campaigns within 24 hours, then delivers daily a 90-day forecast broken down by product, region and channel, with automatic alerts as soon as a cohort diverges. Result: 92% accuracy at 90 days, anomaly detection under 48h, and financial steering finally proactive.
Anticipate trends and catch drifts before they impact your business
Running a business without reliable forecasts is like driving with only a rearview mirror. Dashboards show you what happened, not what will happen. The Forecasting & Anomaly Detection agent trains prediction models on your historical data, generates 3, 6 and 12-month projections and detects anomalies in real time that signal a problem before it shows up in your KPIs.
Manual forecasts rely on intuition and past trends. Anomalies are detected late, when monthly numbers are already in the red. Without proactive alerts, operational problems (churn, conversion drop, cost drift) stay invisible until they become critical.
Machine learning models (Prophet, ARIMA, XGBoost) are trained on your historical data (sales, traffic, costs, retention). Forecasts are updated weekly with confidence intervals. Each detected anomaly (abnormal peak or trough vs. forecast) triggers a Slack or email alert with a natural-language explanation.
How we deploy
- 01Historical data collection & cleaning
Extraction of 12–36 months of data from your sources (CRM, e-commerce, analytics). Cleaning and normalization.
- 02Model training
Selection and training of the best algorithms for your time series. Validation on holdout data.
- 03Real-time alert deployment
Connection to real-time data feeds. Anomaly thresholds configured to your critical metrics.
- 04Forecast dashboard & monitoring
Forecast visualization interface, alert history and continuous model accuracy score.
Concrete benefits
Our models average 92% accuracy on the first 30 days of forecast, depending on historical data quality.
Drifts are flagged before they appear in monthly KPIs. You intervene while there's still time.
Cost and revenue forecasts let you adjust resources in advance. No more end-of-month surprises.
Frequently asked questions
How much historical data is needed?
Are models retrained automatically?
Can multiple metrics be forecasted simultaneously?
Are alerts customizable?
Your revenue forecasts, calibrated and reliable
Connection in minutes. First forecasts within 24h. No credit card.