Senior leadership gap
You need senior data leadership in the room before the architecture decisions are made — and right now you don't have it. Projects start without it.
Solutions
How we help organisations build data, AI, and decisioning systems they can trust.
Four capability pillars for regulated and data-intensive organisations. Senior-led, CDO-built delivery. Vendor-neutral by design.
The four pillars below are sequenced the way engagements typically run — advisory aligns ownership before delivery, foundations enable everything downstream, decisioning turns data into operational outcomes, and production AI extends what's possible once foundations are real. Each has a flagship offer; most engagements combine several.
Data Advisory
Senior advisory that aligns priorities, ownership, and capability so delivery actually lands.
We sit in the room where the architecture decisions get made. Fractional CDO leadership, transformation strategy, vendor selection (vendor-neutral), programme oversight, team build and capability development. Where engagements typically start — because data and AI work that doesn't have senior alignment from day one usually rebuilds itself within twelve months.
The work that prevents the expensive rework. Buyers who skip senior advisory often spend three months building the wrong thing because nobody senior was in the room when the architecture decisions got made.
The problem Senior alignment is missing where it matters most.
Senior alignment is missing where it matters most.
Data and AI work without senior alignment from day one usually rebuilds itself within twelve months — the rework is already starting.
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Stalled programme, unclear root cause
Your AI or data programme is stalling. You can't tell whether the problem is strategy, capability, execution, or the vendor stack.
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Major investment without an outside read
You're about to commit to a major platform or vendor decision and want a senior outside read before you sign.
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Architecture decisions in the wrong room
Tech teams are shipping data and AI work without business, risk, or compliance in the room. The rework starts within months.
What we build What we build for senior alignment.
What we build for senior alignment.
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01 Fractional CDO leadership
Senior data leadership 8–12 days a month — the role with the seat at the table, vendor-neutral, without the full-time hire.
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02 Transformation strategy & roadmap
Multi-year data and AI roadmap with prioritised capability sequencing. The plan that survives the first vendor change.
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03 Vendor selection — genuinely vendor-neutral
Platform and tooling decisions evaluated against your actual operating model. We don't get paid by the vendor; we don't push the vendor.
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04 Independent programme oversight
Senior independent review of in-flight data and AI programmes. The outside read before the steering-committee meeting.
This is for you if…
- Your organisation needs a CDO right now but isn't ready or sized for a permanent appointment.
- Your AI or data programme is stalling and you're not sure whether the problem is strategy, capability, or execution.
- You're going into a major data investment and want a senior outside read before you commit.
- You're between CDOs and need senior coverage during the search.
Data Foundations
Modern data platforms, governance, and quality at scale. The capability that makes everything else possible.
Modern data platforms (dbt, ClickHouse, Snowflake, Databricks, BigQuery), governance frameworks, data quality programmes, BI semantic layers built on the regulated-industry stack. Foundations land so your team can ship analytics, AI, and decisioning without rebuilding the basics each time. Where workloads need to be both transactional and analytical, we've built the dbt + ClickHouse patterns that handle real-time ingestion alongside reporting and ML feature serving.
200+ data quality rules in production at national scale, aligned to BCBS 239 expectations. 95.6% data quality index achieved. The kind of foundation that survives staff turnover, platform migrations, and regulator scrutiny — because it was designed that way from day one.
The problem Data chaos is costing you more than you think.
Data chaos is costing you more than you think.
Many organisations struggle with a chaotic data landscape that actively hinders growth and creates risk.
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Disconnected systems
Critical data trapped in silos (ERP, CRM, core banking), making a single view of the business impossible.
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Lack of trust
Business teams rely on manual, conflicting Excel reports because they don't trust the underlying data.
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Manual reporting overload
Your most skilled analysts spend their days exporting CSVs instead of generating value-adding insights.
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Inability to scale
Your current infrastructure can't support the advanced analytics, AI/ML, or real-time capabilities needed to compete.
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Hidden compliance gaps
Without clear lineage and governance, you can't satisfy regulators or prove the integrity of your data.
What we build What we build for modern data platforms.
What we build for modern data platforms.
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01 A single source of truth
Scalable enterprise data warehouses using dbt, ClickHouse, Snowflake, Databricks — a reliable foundation for all your analytics.
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02 Real-time data pipelines
Robust, event-driven pipelines that move beyond slow, overnight batch processing to enable real-time operational intelligence.
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03 Specialist data integration
Proven expertise integrating complex sources — on-chain blockchain data, unstructured documents, real-time event streams.
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04 Self-service analytics that works
Well-governed Power BI and Metabase solutions with clean semantic layers — business users answer their own questions without creating new silos.
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05 Embedded data governance
Frameworks covering quality, classification, lineage, and privacy — so your data is compliant and trustworthy from day one.
This is for you if…
- Your dashboards have stopped being trusted because numbers don't reconcile.
- Your data quality programme produces more dashboard tiles than decisions.
- Your modern data stack is in flux and you want senior architecture input before the next vendor commitment.
- You're operating multi-jurisdictionally and need privacy-by-design data architecture.
Decision Intelligence & Risk Systems
Credit risk analytics solutions, decisioning systems, IFRS modelling, regulatory reporting. Systems your CFO and your regulator both trust.
Credit risk analytics solutions — modelling, scorecards, decisioning systems with champion-challenger built in, executive BI suites that reconcile, IFRS 9/17 ECL modelling, regulatory reporting infrastructure (BCBS 239, SR 11-7, APRA, SAMA). The systems regulated organisations actually run on — built on production-grade infrastructure, not Excel and goodwill.
Production ML credit scorecard with Gini 0.85, governed under an SR 11-7-aligned model risk framework. IFRS 9 ECL models that reconcile and survive auditor scrutiny. Champion-challenger frameworks built in from day one. Validation evidence packaged for the regulator before they ask.
The problem Outdated risk practices are holding you back.
Outdated risk practices are holding you back.
In the high-stakes world of lending and finance, legacy approaches to risk management create significant challenges.
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High decline rates
You are forced to reject profitable customers because your current models are too conservative or cannot handle thin-file applicants.
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Slow, manual decisions
You're losing customers to faster, digital-native competitors because your decisioning processes take days, not seconds.
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Rising default rates
Despite conservative lending, you see portfolio losses rising — your models are not capturing modern risk signals.
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Inconsistent policy
High rates of manual overrides and a lack of clear, data-driven rules lead to inconsistent and biased credit decisions.
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Hidden compliance gaps
Without clear lineage and governance, you can't satisfy regulators or prove the integrity of your data.
What we build What we build for credit risk.
What we build for credit risk.
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01 Advanced credit models
A full spectrum, from highly explainable scorecards for regulatory compliance to high-performance ML models (XGBoost, Neural Networks) that deliver superior predictive power.
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02 Modern decisioning platforms
Real-time scoring APIs, feature stores with hundreds of curated credit-specific variables, and robust A/B testing frameworks for speed and consistency.
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03 Comprehensive risk governance
Fairness and bias testing (disparate impact analysis), performance monitoring, and model risk frameworks aligned to SR 11-7.
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04 IFRS 9 & ECL solutions
Specialised expertise in building and validating Expected Credit Loss models to meet accounting and regulatory requirements.
This is for you if…
- Your IFRS 9 ECL numbers move month-on-month for reasons nobody can fully reconstruct.
- Your scorecards work but live in Excel and need productionising.
- Your regulator is asking sharper questions about model risk and data quality.
- Your AML/KYC entity resolution is manual and false positives are eating analyst capacity.
Production AI & ML
Models that actually live in production with the governance evidence regulated industries require.
End-to-end ML lifecycle, MLOps, feature stores, GenAI agents with guardrails, drift monitoring, regulated-industry model risk frameworks (SR 11-7), AI governance for enterprise sales (EU AI Act, NIST AI RMF, ISO 42001). The work that turns prototypes into production systems.
Five years of production ML at national scale, three years of recent agentic AI and event-sourced analytics. Including the specific work most ML consultants haven't done: shipping models that survive monthly regulatory review, building drift monitoring tied to portfolio risk appetite, packaging validation evidence for an actual regulator before they ask.
The problem Your models are stuck in the lab.
Your models are stuck in the lab.
Many businesses invest heavily in data science but fail to see a return. Promising models built in notebooks rarely make it into production — the "model graveyard" effect.
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High failure rate
You're struggling to deploy models, with high decline rates on new applicants or rising defaults despite conservative strategies.
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Slow & inconsistent decisions
Manual overrides are common because the business doesn't trust the models, leading to slow, inconsistent, and biased decisions.
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Fear of the unknown (AI)
Pressure to adopt GenAI, but fears of hallucinations, data leakage, and unclear ROI cause innovation paralysis.
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Skills gap
Your team builds models but lacks the MLOps and software engineering expertise to deploy, monitor, and maintain them.
What we build What we build for data science & ML.
What we build for data science & ML.
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01 Custom ML models
From interpretable scorecards (logistic regression) for regulatory compliance to high-performance models (XGBoost, Neural Networks) for competitive advantage.
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02 Generative AI & LLM applications
Secure, enterprise-grade GenAI — RAG systems for accurate responses, AI agents for intelligent automation, robust safety frameworks against hallucinations and data leakage.
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03 End-to-end MLOps
Feature stores, real-time scoring APIs, model registries with versioning, A/B testing frameworks — the infrastructure that makes production possible.
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04 Comprehensive AI governance
Model documentation for regulators, fairness and bias testing, real-time performance monitoring — transparency and trust built in.
This is for you if…
- Your ML models are stuck in notebooks and the path to production keeps stalling.
- You're going to enterprise customers in regulated industries and need AI/ML governance evidence ready.
- Your team has GenAI demos but no production guardrails (hallucination control, audit trails, PII handling).
- Your model risk framework needs SR 11-7 alignment your regulator will accept.
Free assessments
Browse assessments
Three free assessments. Under 10 minutes each.
Benchmark your maturity against the frameworks your regulator uses — Credit Risk Data Governance, AI/ML Governance, Data Privacy. Each produces a scored gap analysis you can share with your team.
Who we work with
Who we work with
Three industries, one common problem: the leap from a roadmap on a slide to a system in production. The work looks different in each; the discipline is the same.
- 01 / Vertical
Financial Services
Banks, fintechs, credit bureaus, regulated lenders, insurers. Bayan-built. Equifax-trained. Spectral-shipped.
- 02 / Vertical
Technology Startups
Series A–C startups going to enterprise. Production ML, fractional senior data leadership, governance for enterprise sales.
- 03 / Vertical
Data-Intensive Organisations
Healthcare, energy, public sector, mid-market. Cross-sector financial-modelling depth applied to data-intensive operations.
Ready to talk?
Thirty minutes, no obligation.
We'll give you an initial read on your problem and an honest answer on whether we're the right fit.