Daisy Intelligence
AI decision engine that tells retailers and insurers which actions will maximize sales and reduce risk
By Daisy Intelligence Corporation · HQ Toronto, Canada · 4.0/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Grocery and supermarket chains wanting AI-ranked promotion, pricing and assortment actions
- Insurers with high claim volumes needing fraud, risk and abuse detection plus claims automation
- Retail and insurance teams with large historical transaction or claims data
- Planning and merchant teams replacing manual spreadsheets with ranked recommendations
Ideal size: Enterprise (500+ employees) people · Established retailer or insurer with clean multi-year transaction data
Not for
- Small or single-site businesses without enough transaction history to model
- Buyers wanting self-serve software deployable without vendor or data-science involvement
- Companies seeking general-purpose AI, chatbots or document tools
- Teams unwilling to share transaction and claims data with a third-party AI vendor
Value metrics scorecard
Time-to-Value
3-6 months
~90 days to first production value
Total Cost of Ownership
On request
Custom enterprise pricing; quotes arranged through the sales team, no public list price
Implementation Friction
3/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not listed
Add-on costs
- None
Company & support
Who is behind Daisy Intelligence, and how your team gets help once it is live.
Company
- Founded
- Not recorded
- Headquarters
- Toronto, Canada
How you get support
We haven’t recorded support channels for Daisy Intelligence yet. Nothing here means unverified — not absent.
Market position
Where Daisy Intelligence sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- Daisy Intelligence
- Smartly
- Adyen
- NewStore
- Finix
- Versapay
Add or change companies
Up to 10 companies including Daisy Intelligence. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Daisy Intelligence ships in AI, and what it asks of your ecosystem.
AI features shipped
The vendor describes an engine built on reinforcement learning and other AI methods that outputs specific, ranked, actionable recommendations. Retail use cases are promotion, pricing, demand, assortment and space planning; insurance use cases are fraud detection, claims automation and underwriting. No model providers or model-key policy are named on the pages reviewed.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Daisy Intelligence is a Canadian AI company whose decision engine uses reinforcement learning to recommend specific, ranked actions. For retailers, mainly grocers and supermarkets, it optimizes promotions, pricing, demand planning, assortment and space planning, with the vendor citing an average 2.9% lift in retail sales. For insurers it identifies fraud, risk and abuse and automates high-volume claims processing and underwriting. Clients include regional grocers and Green Shield Canada, with quoted returns above 10x. Pricing is quote-based and not published.
Frequently asked questions
What does Daisy Intelligence actually do?
It is a decision-optimization AI: the engine analyses a customer's historical retail or claims data and returns specific, ranked actions - which promotions to run, what price to set, how much demand to plan for, which claims look fraudulent, and similar decisions - rather than dashboards or reports for analysts to interpret.
Which teams is Daisy built for?
Two verticals. Retail, primarily grocery and supermarket chains, where it supports promotion planning, pricing, demand, assortment and space planning. And insurance, where it supports fraud, risk and abuse detection, high-volume claims automation and underwriting. Both are enterprise deployments, not self-serve tools.
How is Daisy priced?
The vendor does not publish pricing or seat tiers; the site routes buyers to a sales form. Expect a custom enterprise quote scoped to data volumes and use cases, so budget for a negotiated annual contract rather than a published list price.
How long does implementation take?
No implementation timeline is published. Deployments depend on integrating multi-year transaction or claims history and validating model output with planning or claims teams, so plan for a multi-month onboarding rather than a same-week go-live.
Does Daisy use customer data to train its models?
The public pages reviewed do not state a training or data-use policy, so this cannot be confirmed either way. Ask for the data processing terms and model-training language during diligence before sharing transaction or claims data.
What compliance certifications does Daisy hold?
No security, trust or compliance page is among the sources reviewed, and no SOC 2, ISO 27001, HIPAA, FedRAMP or ISO 42001 certification is claimed publicly. Request current security documentation, penetration test results and a data processing agreement during vendor diligence.