Claude will reason well over anything you put in front of it. Deciding what to put in front of it, from a twenty-year-old ERP estate, is where enterprise AI programmes stall. KPI Partners builds the retrieval, the governance and the evaluation that turn a capable model into a system your business will actually rely on.
Enterprise AI
The pattern we see repeatedly: a promising pilot, an impressive demo, then a stall at the point where someone in finance or compliance asks where the number came from. We build for that question from the first sprint.
Assistants and content workflows grounded in your own documents and data, with citations back to the source.
Agents that take action inside a business process, with the tool access, memory and audit trail an enterprise can sign off on.
Classical models where classical models win, and a clear-eyed view of when a language model is the wrong tool.
IQ Foundry by KPI Partners
IQ Foundry is our suite of GenAI and agentic accelerators built around business functions rather than industries. Each arrives with the domain logic, tool definitions and evaluation set already modelled, so a team sees a working agent on their own data in weeks rather than spending a quarter learning what to prompt.
Sourcing, supplier and contract intelligence 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.
A frontier model will infer a great deal from a schema. 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, and no amount of model capability closes it.
The Enterprise Context Engine is not a product you buy from us. It is the delivery discipline that turns raw platform capability into governed, secured, evaluated context. With Claude that means designing the retrieval layer and tool surface deliberately, writing instructions that encode real business rules, and building the evaluation set before the demo rather than after the complaint.
Assess the data estate, semantic maturity and governance posture. Output is a gap list and a sequenced plan.
One real use case, 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.
What we build
None of this is model research. It is the unglamorous work of connecting a capable model to an enterprise that was not designed with one in mind.
Deciding what the agent owns, what stays with a person, and where the process should stop and ask.
Building the connectors that let an agent read and act across the systems your business actually runs on.
The part most programmes skip, and the reason most of them cannot get past a pilot.
Most enterprise knowledge is in documents nobody has structured. That is a pipeline problem before it is an AI problem.
Getting your own engineers productive with AI tooling, with the review discipline that keeps quality up.
Agents are not a project you finish. Prompts drift, data changes, models improve.
Deployment paths
You do not have to move data to a new vendor to use a frontier model. Which path fits usually comes down to where your data already sits, what your security review will accept, and which contract is easiest to sign.
Running the largest model on every step of a workflow is the most common way to make an agent uneconomic. We size each step to the job it does.
The hardest reasoning steps: multi-step planning, ambiguous documents, decisions where being wrong is expensive.
The workhorse for most production paths, where quality and cost both matter and neither can dominate.
High-frequency, well-bounded steps: classification, routing, extraction, the parts that run thousands of times a day.
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. On an AI programme their real value is upstream: they are the fastest route to the governed semantic layer an agent needs before it can answer anything reliably.
Request the accelerator catalogueFinance, procurement and order-to-cash subject areas for SAP, Oracle and NetSuite.
Pipeline, retention and service metrics from Salesforce and adjacent systems.
Headcount, attrition and talent acquisition views 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 ERP 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.
Use case qualification, data readiness review, deployment path selection and a costed plan.
One real agent built end to end, in production, with an evaluation set the business signed off on.
Additional use cases on the same tooling and context library, with regression testing across each release.
Accuracy and cost monitoring, model version migration, context maintenance, and enablement so your team owns it.
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.