KPI Partners builds generative and agentic AI, data science and ML on Google Cloud, and grounds all of it in governed enterprise data. Domain accelerators from IQ Foundry give you working agents in weeks. The Enterprise Context Engine makes sure they answer from your business, not from a guess.
Enterprise AI
Most enterprises do not need a bigger model. They need the model connected to the right data, with the right controls, doing a job someone in the business actually asked for. That is how we scope every Google Cloud engagement.
Assistants and content workflows built on Gemini, grounded in your own documents and data rather than open-web recall.
Agents that take action inside a business process, with the tool access, memory and audit trail an enterprise can sign off on.
Forecasting, segmentation, anomaly detection and propensity models built to run in production, not to sit in a notebook.
IQ Foundry by KPI Partners
IQ Foundry is our suite of GenAI and agentic accelerators built around business functions rather than industries. Each one arrives with the domain logic already modelled, so procurement, supply chain, HR, sales and finance teams see working agents on their own data in weeks.
Sourcing, supplier and contract intelligence agents across the purchase lifecycle.
Rebate eligibility, accrual tracking and claim leakage detection.
Inventory position, fulfilment exceptions and supplier risk, surfaced as actions.
Proposal and response generation grounded in your approved content library.
Employee 360, policy answers and talent analytics for HR and people teams.
Peer and internal benchmarking with the source figures always traceable.
Claims triage, adjudication support and exception handling for insurers.
Marketing, legal and IT accelerators are in the roadmap. Tell us the function and we will scope building it with you.
Enterprise Context Engine by KPI Partners
Auto-context is not governed context.
Google Cloud will infer a lot about your data on its own. What it cannot infer is which definition of margin your CFO signs off on, which rows a regional manager is allowed to see, or which answers have been validated against the system of record. That gap is where AI programmes lose trust.
The Enterprise Context Engine is not a product you buy from us. It is the delivery discipline that turns platform-native capability into governed, secured, evaluated context on the platform you already run. On Google Cloud that means the Looker semantic model, Gemini in BigQuery, Conversational Analytics and Dataplex, wired together and then hardened.
Assess the data estate, semantic maturity and governance posture. Output is a gap list and a sequenced plan.
One real subject area, modelled and grounded end to end, tested for answer accuracy against known results.
Gold-layer modelling, access and PII masking, verified answers, evaluation harness, human review paths.
Context library maintenance, accuracy monitoring, adoption tracking and continuous cost governance.
The foundation underneath
The hard part is rarely BigQuery. It is the Oracle, SAP, Salesforce and Workday estate feeding it, twenty years of business logic buried in reports nobody documented, and a governance model that has to survive an audit. That is the work KPI Partners has done for two decades.
Landing zone through to semantic layer, designed for the workloads you actually run.
A governed LookML model that gives finance, supply chain and sales one version of every number.
Catalogue, lineage and access control set up as part of the build, not retrofitted after the first audit finding.
Clean, joinable SAP and Oracle data in BigQuery is the single biggest unlock in most enterprise estates.
The transactional side of the estate, sized and tuned for the applications that depend on it.
Follow-the-sun run and support so the platform keeps performing after the build team rolls off.
Migration plays
Every migration starts with an automated assessment of the existing estate: what runs, what is used, what can be retired. The output is a fixed-scope plan with a wave sequence, not an open-ended discovery.
Enterprise Analytics Accelerators
Our Enterprise Analytics Accelerators cover 32 pre-built analytics packages across 11 enterprise source systems. Each ships with extraction logic, a conformed data model and a working set of metrics, so the first business-visible output lands in weeks rather than quarters. They are also the fastest route to the governed semantic layer your AI agents will depend on.
Request the accelerator catalogueFinance, procurement and order-to-cash subject areas for SAP, Oracle and NetSuite, landed in BigQuery.
Pipeline, retention and service metrics from Salesforce and adjacent systems, modelled in Looker.
Headcount, attrition and talent acquisition views sourced from Workday and SuccessFactors.
Inventory position, fulfilment performance and supplier risk across ERP and WMS sources.
Vertical extensions for life sciences, insurance, manufacturing, retail media and energy.
Case studies
Two engagements that show the range: real-time ML on operational data, and a full analytics platform transformation.
Detecting fraudulent payroll activity as it happens rather than in a retrospective audit, using streaming pipelines and anomaly detection models running against live payroll data.
Moving a renewable energy business off a constrained reporting estate onto a modern cloud data warehouse, with SAP as the core source and a governed model built for the whole enterprise.
How we engage
We work in fixed-scope stages so you can stop, redirect or scale after any one of them. Google Cloud migration funding may be available for qualifying workloads, and we will help you assemble the submission.
Automated inventory of the current estate, usage analysis, target architecture and a costed wave plan.
One real subject area or agent built end to end on Google Cloud, in production, proving the pattern with your data.
Wave-based delivery of remaining sources, reports and agents, with parallel run and formal reconciliation.
Managed services, context maintenance, cost governance, and an enablement track so your team owns the platform.
Industries
Deep domain models matter more than generic architecture. These are the industries our accelerators and delivery teams know best.
Start the conversation
Send us the shape of the problem and we will come back with a point of view, not a capability deck. If an assessment or a pilot makes sense, we will scope it with a fixed price and a fixed timeline.