IntelStack
Proof

The work, not the pitch.
Delivered. In production.

Named engagements where permitted, anonymised engagement profiles where not. Sector, scale, product areas, and outcomes are real. Named references available under NDA.

Engagements · reported outcomes

Selected engagements.

Anonymised engagement profiles. Sector, scale, product areas, and outcomes are real; identifying details are not.

  • 01 Technology partner · Global SI

    Unity Catalog + MLflow operations, inside SI delivery

    Product areas · Delta Lake· Unity Catalog· MLflow· Lakeflow / DLT
    Sprint velocity inside SI engagement
    Sprint 01 Δ Steady cadence Sprint 15
    Sprints
    15
    Velocity Δ
    +5.5×

    IntelStack engineers embedded under partner brand on a complex Databricks engagement, taking ownership of the Unity Catalog rollout and MLflow production registry while the partner led client engagement.

    “The end client never saw a seam. The specialists were ours from the outside in.”
    Delivery Partner · Technology partner · Global SI
    Specialist roles covered
    4
    Client-side seams
    0
    Delivery posture
    Embedded
  • 02 Global retail · FTSE 250

    Lakehouse migration + AI/BI Genie rollout, mid-market retail

    Product areas · Delta Lake· Unity Catalog· Databricks SQL· AI/BI Genie
    Hours to decision data, before and after
    Prior estate 62h
    On lakehouse 8h
    Δ −54h

    A multi-format retailer modernised its fragmented reporting estate onto a single lakehouse with semantic-layer governance. The work moved decision data from week-old extracts to live operational reporting.

    “The platform finally matches how the business actually runs. Decisions land in hours, not weeks.”
    Director of Data Strategy · Global retail · FTSE 250
    Reporting TTM
    −72%

    vs prior estate

    Monthly extract jobs retired
    140+
    Business unit adoption
    9 / 9
  • 03 Financial services · Series C, ~400 staff

    Mosaic AI endpoint + MLflow registry, production decisioning

    Product areas · MLflow· Mosaic AI· Feature Store· Unity Catalog
    Production traffic ramp, week over week
    Pilot Δ 11× Production

    An applied AI initiative had landed in pilot purgatory. IntelStack rebuilt it around the workflow it was meant to serve, with an evaluation harness and a clear measure of working.

    “We had an interesting model and no system. IntelStack built the system around it.”
    Head of AI Engineering · Financial services · Series C, ~400 staff
    From pilot to prod
    1 engagement
    Decision latency
    < 200ms
    Evaluation harness
    Built + wired
  • 04 Energy · UK mid-market

    Delta Lake + System tables hardening, platform reliability

    Product areas · Delta Lake· Unity Catalog· System tables· Lakeflow / DLT
    Concurrent workloads by quarter
    Δ 3× absorbed

    An existing platform was hitting cost and reliability ceilings under growing adoption. IntelStack stabilised the workload pattern, reworked governance, and added the headroom for the next wave of use cases.

    “We needed specialists who could see the whole platform, not another pair of hands on one corner of it.”
    Platform Engineering Lead · Energy · UK mid-market
    Workload growth absorbed
    3×
    Sev-1 incidents
    → 0
    Cost / run
    −28%
  • 05 Commercial bank · Card fraud

    Real-time card-fraud detection with explainable scoring

    Product areas · Anomaly detection· Fraud scoring· Explainable AI· Streaming
    False-positive rate, before and after (indexed)
    Prior rules engine 100
    Adaptive model 55
    Δ −45%

    A commercial bank needed to catch fraudulent card transactions in real time without drowning review teams in false positives. IntelStack trained unsupervised models on behaviour and transaction patterns, wired a fraud-scoring engine into transaction monitoring, and built trend and root-cause dashboards — with explainable scores the review teams and auditors could trust.

    “Transparent decisioning is what moved the auditors. The latency is what moved the floor.”
    Head of Financial Crime · Commercial bank · Card fraud
    False positives
    −45%
    Scoring latency
    < 2s
    Auditor confidence
    Explainable
  • 06 Regional digital bank · Treasury

    AI multi-currency reconciliation + liquidity forecasting

    Product areas · Databricks· Azure ML· Semantic matching· Power BI
    Reconciliation cycle time, before and after (indexed)
    Manual process 100
    AI-enabled engine 40
    Δ −60%

    A regional digital bank was reconciling multi-currency ledgers by hand and reacting to liquidity needs after the fact. IntelStack deployed ML models for fuzzy and semantic matching across cross-currency transactions, added predictive analytics for FX liquidity planning, and built reconciliation and exception dashboards on Databricks — giving Treasury real-time accuracy across Nostro accounts.

    “We went from chasing breaks to planning liquidity. The reconciliation just runs.”
    Head of Treasury Operations · Regional digital bank · Treasury
    Reconciliation time
    −60%
    Liquidity posture
    Proactive
    Multi-currency accuracy
    Improved
  • 07 Fintech lender · Credit decisioning

    Explainable AI credit-risk modelling in production

    Product areas · Databricks· XGBoost· SHAP explainability· Power BI
    Decision throughput ramp, indexed
    Manual underwriting Δ 10× AI decisioning

    A fintech lending platform needed faster, defensible credit decisions. IntelStack built ML models reading income stability, repayment history, and alternate credit signals, layered SHAP-based explainability over every approval and rejection, and wired portfolio-risk dashboards — real-time, transparent decisioning aligned to regulatory expectations on Databricks.

    “Ten times faster, and every decision has a reason we can show a regulator.”
    Chief Credit Officer · Fintech lender · Credit decisioning
    Loan approval time
    10× faster
    Default prediction
    +35%

    accuracy

    Decisioning
    Explainable
  • 08 Tier-1 bank · AML

    Graph link-analysis for AML investigation

    Product areas · Neo4j· Graph analytics· NetworkX· Power BI
    High-risk counterparty clusters surfaced, by quarter
    Δ Hidden links surfaced

    A Tier-1 bank needed to surface hidden relationships and layered fund flows its rules engine missed. IntelStack built graph-based link-analysis models for pattern recognition, profiled counterparty risk from historical and real-time data, and delivered AML dashboards with explainable risk heatmaps — strengthening detection of circular and layered laundering.

    “The graph showed us the structures the rules never could. Investigations got sharper and faster.”
    AML Investigations Lead · Tier-1 bank · AML
    Investigation cycles
    −30%
    High-risk detection
    Improved
    Audit readiness
    Strengthened
  • 09 Digital bank · Core operations

    Explainable AI dashboard for core-banking operations

    Product areas · Azure OpenAI· SHAP explainability· NLP· Power BI
    Operational-risk review time, before and after (indexed)
    Opaque models 100
    Explainable layer 70
    Δ −30%

    A digital bank wanted transparency into AI-led core-banking operations and risk models. IntelStack built a SHAP-based explainability layer over AI decisions, added NLP-driven sentiment analytics on customer interactions, and wired real-time visualisation for operational-risk monitoring across FX, reconciliation, and internal transfers.

    “Explainability turned a black box into something compliance and the floor could both stand behind.”
    COO, Digital Bank · Digital bank · Core operations
    Operational risk reviews
    30% faster
    Regulatory compliance
    Improved
    Decision transparency
    End-to-end
Named engagements · Banking & Financial Services

Named, with permission.

Banking and financial-services engagements we can name — across lending, BaaS, trade finance, and consumer banking.

01 Ongoing
Amplifi Capital
UK consumer & SME lending · Credit union

Salesforce CRM modernisation across the lending lifecycle

Amplifi Capital is a UK specialist lender to underserved segments. As it scales, it is modernising its Salesforce CRM to unify visibility across sales, credit, and collections — improving lifecycle data flow from origination to collections.

Scope of work
  • —Audited the existing Salesforce estate and digital processes
  • —Redesigned the customer lifecycle: origination, underwriting, servicing, collections
  • —Integrated third-party APIs for credit scoring, document verification, and payments
  • —Built modular workflows automating underwriter decisioning and client communication
Stack · Salesforce· AppExchange· AWS· Experian APIs· UK payment gateway
02
EDB · APEX Group
Institutional banking · Luxembourg · BaaS

Banking-as-a-Service platform build for a global offering

European Depository Bank (EDB), a Luxembourg-based institutional bank, was building a white-labelled global BaaS offering in partnership with a major US bank. IntelStack ran discovery across both institutions and built the platform foundations.

Scope of work
  • —Full discovery of EDB and partner-bank capabilities
  • —Designed onboarding, application-tracking, KYC, and FX-liquidity journeys
  • —Integrated SEPA / SWIFT / ACH rails, account opening, ledgering, and screening
  • —Deployed to cloud with resilience, documentation, and data-protection controls
Outcome
  • →Institutional-onboarding MVP shipped
  • →API-led core banking enabling fast partner expansion
Stack · AWS· Mambu· ComplyAdvantage· IDnow· CurrencyCloud· GreshamTech· SaltEdge
03
Traydstream · Citi
Trade finance automation · Global banks

Trade-finance automation, integrated into Citi

Traydstream's trade-finance automation platform partnered with IntelStack to deliver implementation work for Citi and other global banks — document checking, validation, and compliance at global-infrastructure scale.

Scope of work
  • —Deployed document-parsing engines and trade-validation rules
  • —Integrated workflows with Citi's compliance and legacy systems
  • —Aligned security architecture for global infrastructure rollout
Outcome
  • →Automated trade verification
  • →Reduced manual processing for compliance teams
Stack · Python· Azure· Kubernetes· OCR / NLP· REST APIs
04
Shoal · SC Ventures
ESG consumer banking · Standard Chartered venture

ESG savings & investment platform engineering

Shoal, a fintech under Standard Chartered's SC Ventures, set out to build an ESG-led consumer banking platform. IntelStack contributed the savings module, then was appointed core product-engineering partner for the investment module — through to a clean handover on Shoal's acquisition.

Scope of work
  • —Built the customer-facing mobile savings module
  • —Stood up a 10-person squad: tech lead, BA, scrum master, UX/UI, full-stack, QA
  • —Led product architecture and stakeholder alignment for investment workflows
  • —Provided ongoing technical support and documentation
Outcome
  • →Extended savings features on Shoal's mobile app
  • →Appointed product-engineering partner for the investment module
  • →Knowledge transfer and codebase handed over post-acquisition
Stack · Dart / Flutter· .NET· AWS· GraphQL· Appium· CI/CD
05
UK commercial bank
SME business banking · UK

Digital-first SME current-account platform

A mid-sized commercial bank launched a digital-first business banking platform for SMEs, built around cash flow, KYC, and payments. IntelStack defined the operating model and engineered the platform for scale.

Scope of work
  • —Defined the SME operating model
  • —High-throughput APIs for onboarding and KYC
  • —AML checks, secure access control, and scalable infrastructure
Outcome
  • →Strong SME adoption
  • →Operational savings via automation
  • →Modular base enabling ongoing enhancements
Stack · Spring Boot· Kafka· Azure· MongoDB
Under NDA

More named references available under NDA.

Tier-1 banks, challenger banks, and fintech lenders across the UK and EU.

Request references
Leadership Track Record

The pedigree behind the practice.

Programmes delivered by IntelStack leaders in prior roles, before the firm was formed.

01
Lloyds Banking Group
Commercial bank modernisation · UK
Founder experience — prior to IntelStack

Commercial bank modernisation — Bank-in-a-Box rollout

Lloyds launched a multi-year modernisation to address fragmented technology stacks across its commercial banking arm. An IntelStack leader served as Systems Integrator on a modular Bank-in-a-Box rollout — re-platforming credit origination, onboarding, and relationship-management tooling into an API-first architecture.

Scope of work
  • —CRM integration, rules-based product engine, and workflow orchestration
  • —Phased rollout across business units using agile delivery pods
  • —Post-launch optimisation and regulatory assurance
Outcome
  • →Reduced onboarding times for mid-market clients
  • →Improved transparency and RM experience
  • →Laid groundwork for a modular digital layer still in use today
Stack · Salesforce· Mulesoft· Java microservices· Docker· Jenkins CI/CD· AngularJS
02
Yorkshire Building Society
Savings & mortgage modernisation · UK mutual
Co-founder experience — prior to IntelStack

Savings & mortgage platform modernisation

YBS, a mutual lender, set out to modernise its savings and mortgage systems while preserving member-first values and keeping change risk low. An IntelStack co-founder led an API-led re-architecture of onboarding, account servicing, and workflows.

Scope of work
  • —API-led re-architecture of onboarding, account servicing, and workflows
  • —Integration with CRM, orchestration, and channel systems
  • —Embedded low-code controls and phased delivery
Outcome
  • →Enabled secure self-serve and reduced operational friction
  • →Strengthened resilience and internal cross-team alignment
Stack · Mulesoft· Salesforce· Java· BPM tools· ReactJS
Discipline
Production-grade
Built for what runs, not what demos
Reach
UK · EU · India
Three regions, one delivery standard
Anchor
Databricks
Lakehouse · Unity Catalog · MLflow · in production
Compliance
ISO 27001
Certified — information security management
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