AI & Data
Put your operational datato work before yourcompetitors do
Forecasting, document intelligence, predictive maintenance and agentic automation — engineered into the systems your teams already use, governed for audit, and measured against a business outcome.
- Outcome first
- Every use case starts with a bottleneck and a measurable target.
- In production
- AI features running live inside Kaidova platforms.
- Embedded
- Predictions land in your ERP, POS or dispatch screens — not a separate dashboard.
- Governed
- Human review where it matters, audit trails everywhere.
- 31%
- Less food waste
- 8×
- Faster invoicing
- 42%
- Fewer breakdowns
- 11 hrs
- Saved per person weekly
Overview
Most AI projects fail becausethey start with the model
They begin with a technology and hunt for a use case. We start at the other end — with the bottleneck that costs you money every week — and only then decide whether machine learning is the right instrument.
Sometimes a rules engine and a cleaner data pipeline solve it for a tenth of the cost. We will tell you that. When a model is the right call, we build it into the system your team already uses, with data lineage, human review points and an audit trail. Nobody has to remember to open a separate dashboard for the AI to be useful, and nobody has to guess why it made a decision.
Who this is for
- Operations leaders with a recurring, expensive bottleneck — waste, downtime, manual processing
- CIOs asked for an 'AI strategy' who want something that ships
- Companies with data in an ERP or POS that has never been used to predict anything
- 4
- AI features live in production platforms
- 6–10 weeks
- From discovery to first measurable result
What's included
What you get from thisengagement
Working software and a documented reason for each decision — not a slide deck of possibilities.
Data readiness audit
An honest assessment of whether your history can support a model yet — and exactly what is missing if it cannot.
Demand & capacity forecasting
Per-branch, per-SKU or per-route prediction that retrains on a schedule rather than ageing quietly.
Document intelligence
Invoice, PO and contract extraction that posts straight into your ERP, with exceptions routed to a human.
Predictive maintenance
Component failure risk scored on a rolling window, with the workshop slot booked before the failure happens.
Agentic workflows
Multi-step automations that plan, check against your rules, execute across systems and log every decision for audit.
Monitoring & retraining
Drift detection, accuracy reporting and scheduled retraining, so month twelve resembles week one.
Our approach
From bottleneck toproduction, in threephases
Each phase ends with something you can review and a decision point where you can stop.
Start with phase one- Map the bottleneck and what it costs per week
- Audit data volume, quality, access and ownership
- Pick one use case with a measurable target
- Decide honestly whether ML is the right instrument
You end up with: A written recommendation with a target and an estimate — including 'not yet' if that is the answer.
- Data pipeline and feature definitions built and documented
- Model or agent developed and validated against historical data
- Embedded into the operational screen or workflow with human review points
- Shadow run against real operations before it influences decisions
You end up with: A working AI feature in the hands of the people who make the decision, with its accuracy visible.
- Drift monitoring and scheduled retraining
- Outcome tracked against the phase-one target
- Governance review: decisions, exceptions and audit logs
- Extension to the next use case, or handover to your team
You end up with: Measured, governed AI in production — and a documented path to the next bottleneck.
AI governance
AI governance you canshow an auditor
Enterprise AI has to be explainable to the people accountable for the outcome.
Data lineage
Every feature traceable to its source system and transformation.
Human in the loop
Approval points wherever judgement or accountability is required.
Decision logging
Inputs, outputs and confidence recorded for every automated action.
Access & privacy
Personal data minimised, access controlled, retention defined.
Not sure which service you actually need?
Describe the problem in thirty minutes and a solution architect will tell you — including when the answer is that you do not need us for this.
Tools we use
Boring technology, chosen deliberately
Proven tooling your team can maintain. Novelty is a cost you pay at handover.
Modelling
- Python ML stack(scikit-learn, XGBoost, Prophet or similar)
LLM & agents
- OpenAI / Anthropic APIs
- Orchestration framework
Data
- PostgreSQL
- MySQL
- Pipelines
Runtime
- TypeScript
- Node.js
- Docker
- AWS
- Azure
Automation
- n8nfor orchestration where low-code fits
Why us
Why partner with Kaidova on AI
AI features already run in our own platforms with real users. You are buying something we operate, not a first attempt.
It already runs in our own products
Forecasting, predictive maintenance and document extraction live inside Kaidova platforms — with the operating lessons that come from that.
We will talk you out of it
If a rules engine and a cleaner pipeline solve the problem for a tenth of the cost, that is the recommendation you get.
A number before you commit
Phase one ends with a measurable target. If we cannot name one, the project is not ready.
Wired into the tools people already open
Predictions and actions land in the screens your team uses daily.
Built to be handed over
Architecture, feature definitions and retraining runbooks are documented so your team can take over.
Our work
Results from systems in production
How disciplined engineering translates into operational outcomes. Every figure shown is measured on a live system and approved by the client.
View all case studies
01LieferfairMarketplaces & LogisticsRebuilding a delivery marketplace to survive its own growth
A fast-growing delivery platform was hitting the limits of its original architecture. We re-engineered the core system for scale, reliability and real-time operations.
Read case study3.2×
Increase in order throughput
02Construction contractor (on Oplyra)Construction & TradingOne ledger for a contractor running six disconnected systems
Read case study40%
Less time closing the month
03FastestVPNConsumer SoftwareScaling a consumer VPN to millions of sessions a day
Read case study99.9%
Uptime across regions
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Find out what your data is already capable of
Thirty minutes with the engineers who built AI into our own platforms, walked through your operation. No obligation, no slide deck.