Turn your enterprise data into GenAI intelligence
SCIKIQ connects every data silo, wraps it in rich context, and lets your teams query it in plain language — so your AI investments actually deliver.
No replatforming · Enterprise-grade security · GDPR compliant
01A Monday morning
Not a dashboard. A decision.
This is what “actually deliver” looks like. Most tools hand you numbers and leave the thinking to you — SCIKIQ starts with the answer: what changed, why it matters, and the one decision worth making today.
You’re on track to exceed quarterly ARR by about 6%.
Growth accelerated because enterprise pipeline expanded while product adoption kept improving. Customer health is stable, though two strategic accounts show early renewal risk.
If only one decision is made today, approving the Healthcare expansion is expected to generate the highest business impact.
- Revenue+12.4%Accelerating
- Customer− 2.1%Stable, 2 at risk
- Innovation+18.0%Adoption climbing
- Delivery− 4.0%EU slip, recoverable
- Financial+6.2%Margin holding
- People+9.0%Capacity improved
- Pipeline +$2.1MHealthcare and Manufacturing entered proposal stage early
- Renewal risk AcmeUsage down 22% since the sponsor left in April
- Product usage +18%Natural-language queries up across 9 of 12 business units
- New prospect Fortune 100Inbound from a tier-1 insurer, routed to the UAE team
- EU delivery 5 daysIntegration slip, recoverable if addressed before Wednesday
- Hiring capacity +4 FTETwo delivery pods freed by a hospitality go-live
- Largest projected ARR contribution of the open set
- Highest probability of closing inside 45 days
- Implementation capacity is already free after the hospitality go-live
- 42 similar opportunities closed 31% faster
Recommendation Approve today.
- Forecast Q3 revenue across 4 scenarios
- Refreshed the board pack with Friday’s actuals
- Reviewed 127 customer accounts for churn signal
- Simulated three pricing scenarios
- Monitored 6 competitors for pricing moves
- Generated this executive briefing
Illustrative product view · sample data
Almost no enterprise can produce that page today. Here is why.
02The problem
Most enterprise AI dies in the data, not the model
AI projects rarely fail because the model is weak. They fail because the data behind it sits in separate systems, carries no business meaning, and nobody quite trusts it. SCIKIQ fixes that first.
- Data sits in 12+ disconnected systems with no common language
- AI models hallucinate when data lacks metadata and lineage
- Data engineers spend most of their time preparing data by hand
- Compliance teams can't audit what the AI is using
- All enterprise data unified in one governed intelligence layer
- Rich metadata and lineage make AI responses accurate and auditable
- Automated pipelines free engineers to build, not babysit data
- Policy enforcement and access controls at every query
Fixing it takes four layers, not one more tool.
03The stack
Four layers. Each answers a harder question.
Enterprise 360 tells you what happened. The knowledge graph tells you why. The copilot explains it in plain language. The agent factory does something about it.
- ERPSAP, Oracle, Dynamics
- CRMSalesforce, Dynamics
- Cloud dataSnowflake, Databricks
- Files & APIsExcel, REST, OData
- Streams & IoTKafka, MQTT, OPC UA
- DocumentsPDFs, email, scans
- 01Enterprise 360What is happening?
- 02Knowledge GraphWhy is it happening?
- 03AI CopilotTell me, in plain language
- 04Agent FactoryFix it, with a full log
- One live number everyone trusts
- The root cause, traced to its source
- Answers in plain language
- Actions written back to your systems
What is happening?
Unify customer, operational, and transactional data across ERP, CRM, and line-of-business systems into one real-time view — so every team works from the same numbers instead of reconciling them by hand.
Why is it happening?
Trace the relationships between customers, products, suppliers, and events to find the root cause behind a number that moved — and show exactly which sources the answer came from.
Tell me, in plain language
Let any team ask about performance, risk, or churn in their own words — answered from governed data with full lineage, not dashboard averages.
Don't just tell me — fix it
Turn answers into action. Agents reconcile, rebalance, flag anomalies, and trigger workflows on their own — with every action logged and auditable.
04The platform
Start where it hurts. Grow into the rest.
The four layers run on six modules. Adopt the whole platform, or start with the module that unblocks you first.
Data Fabric
One architecture and semantic layer across every source — the knowledge graph the copilot and agents run on.
Data Governance
Metadata management, lineage, stewardship, and policy enforcement at enterprise scale.
Digital Transformation
A governed 360 view in 60 to 90 days — built over the systems you run, not a two-year replatform.
Data Product Factory
Convert governed datasets into publishable, reusable data products — via simple prompts.
None of it matters if it cannot reach the systems you already run.
Customers
Not 12–18 Months
Point it at what you already run.
No-code connectors for databases and warehouses, SAP and business applications, files and documents, conversations and recordings, streaming and industrial OT, healthcare and payment standards, and AI models. You keep what works.
Databases & warehouses
53- Relational databases19
- Data warehouses & lakehouses15
- Document stores2
- Key-value & cache1
- Vector & search6
- Big data & query engines8
- Data catalogs2
e.g. Oracle, SQL Server, Snowflake, Databricks, BigQuery
Business applications & BI
72- SAP & ERP19
- SaaS & business apps30
- BI & analytics23
e.g. SAP S/4HANA, Salesforce, Workday, ServiceNow, Power BI
Files, documents & conversations
51- Cloud storage3
- Files2
- Document libraries & web9
- Unstructured documents12
- Email2
- Collaboration & messaging6
- Audio, voice & video17
e.g. SharePoint, PDFs & scans, Slack, Zoom recordings
Streaming, IoT & industrial OT
48- Streaming & IoT9
- Industrial IoT & OT39
e.g. Confluent (Kafka), OPC UA, Siemens S7, MQTT Sparkplug
Industry standards
20- Healthcare12
- Finance & payments8
e.g. FHIR & HL7, Epic, ISO 20022, SWIFT MT
APIs & AI models
24- APIs3
- AI & ML services21
e.g. REST & GraphQL, Claude, OpenAI, AWS Bedrock
See all 268 data sources by type
Databases & warehouses53
Business applications & BI72
Files, documents & conversations51
Streaming, IoT & industrial OT48
Industry standards20
APIs & AI models24
The connector catalog. Anything not listed connects through the generic REST, GraphQL or OpenAPI connectors.
06Where it pays off
Every leader sees their own number
A category, a supply chain, a channel, a vendor book — whatever you are accountable for, you get one live view of it, built on the same four layers. Shown here for a consumer-goods business; every industry below runs the same pattern.
Category 360
See, diagnose, and act on category performance in real time. Unify revenue, margin, and share across channels, trace the root cause of a dip, and trigger corrective action.
SKU 360
End-to-end visibility and resilience. Monitor inventory, supply events, and vendor status for every line, and resolve bottlenecks before they reach sales.
Channel 360
Omnichannel insight across modern trade, e-commerce, and distributors. Integrate sales, stock, and campaign data, then optimise allocation and ROI.
Vendor 360
Monitor risk, quality, and regulatory status. Track vendor performance and incidents, and automate early warning and remediation.
Promo 360
Link spend, uplift, and supply for every campaign. Connect investment to real sales and inventory outcomes, and stop ROI erosion before it starts.
This is not a roadmap. Sixteen enterprises run it in production today.
07Proof
Recognition
In the news
08The board question
“Can we trust it?”
Boards ask it last. Data teams answer it first. Lineage, access, explainability, quality and compliance are part of the foundation — not features added later.
Lineage
Every insight and every agent action is traceable end to end, across each source system, with one-click drill-down.
Security & access
Enterprise-grade controls, role-based access, and audit logs that align with your existing global standards.
Explainability
Every copilot answer is grounded in real data, with causal links and business context. No black box.
Data quality
Automated profiling, deduplication, and anomaly detection across every 360.
Compliance
Regulatory and packaging obligations monitored in real time, with early warning before an incident is recorded.
Still have questions? So did every team on that wall.
The things every data team asks us first.
Not sure SCIKIQ fits? We work with enterprises who have tried building this before and hit the same walls. Happy to walk through your situation.
Ask us directlyWhat is SCIKIQ?
SCIKIQ is an AI-ready enterprise data platform. It connects every data silo, wraps it in a governed knowledge graph, and lets teams ask questions in plain language and act on the answers — so your AI investments actually deliver.
What does the SCIKIQ platform include?
Six modules — Data Hub, SAP Integration, Data Fabric, Data Governance, Digital Transformation and the Data Product Factory — under four layers: Enterprise 360, Knowledge Graph, AI Copilot and Agent Factory.
Do we need to replace our existing data infrastructure?
No. SCIKIQ is built to work alongside your existing stack, not replace it. Whether you are running Oracle, SAP, Snowflake, SQL Server, or a mix of legacy and cloud systems, SCIKIQ connects to all of them via no-code connectors. You keep what works and eliminate what does not.
How long does deployment actually take?
Most customers are live within 60 to 90 days — against traditional data platform projects that routinely run 12 to 18 months. The no-code architecture and pre-built connectors remove the heavy lifting that usually slows these projects down.
We already have a data lake or warehouse. Why would we need SCIKIQ?
A data lake stores data. SCIKIQ governs, unifies and makes it usable. Most enterprises with existing warehouses still struggle with inconsistent definitions, broken pipelines and siloed access. SCIKIQ adds the intelligence layer that turns stored data into trusted, AI-ready intelligence — without replacing what you have already built.
Is SCIKIQ suitable for regulated industries?
Yes. Built-in data lineage tracking, role-based access control and audit logging mean every piece of data can be traced to its source, access is governed at a granular level, and compliance teams get the audit trails they need without building separate tooling.
Ready for a different Monday morning?
See SCIKIQ on your own systems: one governed view, the root cause behind it, and agents that act on it.
Typical deployment: 60–90 days, not 12–18 months · No replatforming · No migration risk
Across every part of the business
On top of the systems you already run
Explore all of SCIKIQ
Platform, 360 views, industries, roles, company types, comparisons and guides
Platform
The modules that make enterprise data AI-ready
- Enterprise data hubConnect, govern and unify every source
- Data GovernanceAI-powered metadata, quality and stewardship
- Data Product FactoryTurn governed data into marketable products
- Data FabricA unified architecture for modern data
- SAP IntegrationNo-code, non-invasive SAP extraction
- Digital TransformationA 360 view in 60 days, not two years
360 views
Governed, entity-level views your teams act on
- Customer 360Golden record, churn and lifetime value
- Supplier 360Spend, risk and reliability in one view
- Finance 360Entities, P&L and variance analysis
- HR & Employee 360Cost, attrition and performance
- Supply Chain 360Cost, disruption and service levels
- Factory 360Output, downtime and OEE
- Office 360Cost, utilisation and occupancy
- Facility 360Cost, failure risk and uptime
Industries
How the four layers apply in your sector
- ManufacturingOEE, quality and supply chain
- Banking & FinanceRisk, compliance and 360 views
- HealthcarePatient 360 and compliance
- TelecomNetwork, churn and experience
- RetailInventory, demand and loyalty
- InsuranceUnderwriting, claims and retention
- AirlinesFares, disruption and revenue
By role
The 360 view each business owner runs on
- CFOFinance 360, variance and close
- COOOutput, cost and service levels
- CMOCustomer 360, churn and LTV
- Supply ChainLanded cost and disruption risk
- CHROWorkforce cost and attrition
By company type
What ownership structure does to your data estate
- Startups & scale-upsGoverned metrics, no platform team
- Privately heldDecades of systems, one view
- PE-backedPortfolio reporting and exit readiness
- Publicly tradedAudit-ready, traceable numbers
Compare
How SCIKIQ differs from the tools you may be evaluating
- SCIKIQ vs CollibraGovernance programme vs governed platform
- SCIKIQ vs InformaticaBroad suite vs one governed layer
- SCIKIQ vs AlationCatalogue vs knowledge graph
- SCIKIQ vs AtlanActive metadata vs semantic layer
- SCIKIQ vs TalendPipelines vs governed activation
- Cloudera Data Hub alternativeManaged compute vs a governed hub
- SCIKIQ vs FivetranMoving data vs giving it meaning
Guides & company
Choose the right pattern, then see who builds it
- Hub vs fabric vs lakehouseWhat each pattern actually solves
- Data hub implementationSix stages, and what goes wrong in each
- About SCIKIQWho we are and what we believe
- Partner communityWho we build and deliver with