AI-assisted process engineering for banks and regulated operations
Assess more improvement opportunities. Move from evidence to action faster.
Leanify turns procedures and case-level operating data into an evidence-linked improvement assessment — baseline performance, priority areas for validation, improvement options and controls — so Process Engineering and Operational Excellence teams can expand their capacity without adding equivalent specialist workload.
Built on DMAIC. Designed for transparent evidence, controlled AI and practitioner-led decisions.
Built from direct requirements input from process-engineering leaders at a major global bank. Working banking demonstrator delivered in seven days. Now opening a small number of peer-bank conversations.
For Process Engineering, Lean Six Sigma, Operational Excellence, transformation, automation and internal-consulting teams.
Expand the impact of your improvement capability
Assess more opportunities
Reduce the manual preparation required to define the problem, establish the baseline and create the initial investigation agenda.
Improve project consistency
Give teams a repeatable evidence-to-action workflow while preserving methodology, practitioner review and accountable decisions.
Strengthen executive decisions
Show the evidence, calculations, assumptions, uncertainties and validation required behind every material conclusion.
From operating evidence to an executive-ready improvement case
Leanify combines a defined procedure, focused business questions and case-level operating data to show what is happening, where performance is being lost, what should be investigated next and how improvement should be controlled.
Synthetic banking demonstration
- SLA attainment
- 76.67%
- Cases breaching the four-hour service level
- 42 of 180
- Two largest defect categories
- 76.19% of observed breaches
- Priority validation area
- Missing or invalid mandatory payment fields
- Proposed first action
- Pre-submission field validation
Why this? In the full assessment, every figure opens its evidence links, calculation basis, assumptions and validation requirements.
Synthetic demonstration outputs. Not results achieved by a bank.
How Leanify works
A controlled path from operating evidence to an improvement decision.
Evidence in
- Focused business questions
- The operational decision, performance problem or improvement opportunity the assessment must address.
- Written procedure
- The intended workflow, responsibilities, controls and expected operating method.
- Case-level operational data
- Recent records in the accepted schema, used to calculate performance and identify observed patterns.
Decision-ready output
- Evidence-backed diagnosis
- A defined problem, operational scope and calculated view of current performance.
- Prioritised validation agenda
- Candidate factors, testable hypotheses, evidence gaps and the next analysis required.
- Improvement options
- Linked interventions, risks, effort, assumptions and an illustrative benefit scenario.
- Control framework
- Proposed measures, ownership, cadence, triggers, responses and evidence traceability.
Structured using Define, Measure, Analyse, Improve and Control.
Designed around the outcomes improvement leaders are accountable for
Process engineering and Lean Six Sigma
Assess more potential projects, improve the quality of the first analytical pass and give experienced practitioners more time for validation, coaching and intervention design.
Operational excellence
Move faster from KPI, SLA, quality or rework problems to a governed corrective-action case with visible evidence and ownership.
Automation and transformation
Build a stronger evidence-backed opportunity pipeline before selecting technology, funding delivery or committing to an assumed solution.
Internal consulting and change
Reach an executive-ready fact base faster while retaining consultant judgement, stakeholder engagement and implementation ownership.
Controlled AI, not a black-box answer
Leanify does not ask users to trust an unexplained AI conclusion. Authoritative baseline figures are calculated from the operational data. Source facts, calculated results, assumptions, observed associations and unconfirmed hypotheses remain distinguishable. AI is used to structure the evidence, connect the analysis and explain what should be investigated or decided next. Every material conclusion can be inspected through its evidence, calculation basis, limitation and validation requirement.
- Calculations come from the data
- Candidate causes remain hypotheses until validated
- Practitioners retain scope, validation and decision authority
See how Leanify could expand your improvement capacity
Explore how the workflow could support your current Process Engineering, Operational Excellence or transformation priorities.
Best fit
Leanify is designed for teams with a defined or reviewable workflow, a written operating procedure and recent case-level data who need a disciplined first assessment of performance and improvement opportunities.
- A bounded operational workflow
- A procedure or agreed operating method
- Recent case-level operational data
Process not yet sufficiently defined? RapidMap by Leania can help convert workshop evidence, notes and procedures into a reviewable current-state process before operational analysis begins.
Explore a complete synthetic banking assessment
See how Leanify analyses a synthetic corporate-payment workflow using a written procedure and 180 synthetic case records. The assessment shows calculated performance, a Pareto of observed SLA breaches, prioritised validation areas, proposed interventions, an illustrative capacity scenario and a control framework. All sample content is clearly labelled synthetic. No client data is used.
Start with one bounded workflow
A Leanify pilot begins with one defined operational problem, agreed inputs, limited runs, guided onboarding and practitioner review. Success measures are agreed before the analysis begins, creating a clear proceed, refine or stop decision.
- One bounded workflow
- Synthetic or appropriately anonymised evidence by agreement
- Agreed operational and adoption measures
- Clear proceed, refine or stop decision
Leanify accelerates the evidence and analytical work around DMAIC. Process engineers retain responsibility for scope, validation, intervention design and decisions.
Security, privacy and data handling
Projects and evidence are private to the authenticated account, and server-side access controls protect stored evidence. Evidence is not shared between accounts. Pilot inputs are agreed before upload, and synthetic or appropriately anonymised evidence can be used for early evaluation.
Frequently asked questions
- Does Leanify find root causes?
- Leanify prioritises candidate factors and frames testable hypotheses using the available evidence. Validating causes remains a practitioner-led activity supported by further data, process investigation and appropriate analysis.
- Does Leanify replace process engineers or Lean Six Sigma practitioners?
- No. It reduces manual evidence preparation and creates a structured first analytical pass. Practitioners remain responsible for scope, validation, intervention design, stakeholder decisions and implementation.
- What inputs are required?
- Two focused business questions, a written procedure for the workflow and recent case-level operational data in the accepted schema.
- Are benefit figures verified savings?
- No. Benefit scenarios are transparent planning models based on visible, editable assumptions. Capacity benefits remain illustrative unless the organisation separately validates and approves a cash-release mechanism.
- When is Leanify not the right starting point?
- Leanify is not the right first step when the workflow is undefined, the available evidence is not sufficiently case-level, or a full validated root-cause study is already required. In those situations, the process should first be defined or the necessary validation work should be completed.
- Can we start with synthetic or anonymised evidence?
- Yes. Early evaluation can use a controlled synthetic case or appropriately anonymised evidence, subject to the agreed pilot boundary and data-handling requirements.