The systems
serious institutions
run their
decisions on.
We build the data and AI systems institutions rely on when the cost of being wrong is high, in regulated industries, in mission-critical operations, and inside the constraints the work actually runs under.
By appointment · NDA available on first contact
- 0%
- Senior practitioner ratio
- Every one
- Engagements led end-to-end by partners
- 0 days
- Time to first production artifact
- Four
- Disciplines under one practice
A focused practice.
End to end.
Analytics engineering, machine learning and modern product practice under one roof. We ship, and we stay with what we shipped.
All servicesData & Analytics
Dashboards that compress what is happening into what someone should do about it. Built around the decisions they exist to inform, not the metrics they happen to display.
Explore Data & AnalyticsMachine Learning
Forecasting, segmentation and decision-support models, designed for the conditions they will meet in production, evaluated against the metric that matters, documented for the reviewer in the room.
Explore Machine LearningApplied AI
LLM workflows and document intelligence, deployed where they remove real friction, instrumented, evaluated and constrained, never as a chat window bolted onto a process that already worked.
Explore Applied AIData Engineering
Modern, governed data platforms that turn one-off reports into a durable, auditable asset for the whole organization.
Explore Data Engineering
How the practice conducts itself.
Six tenets the work is held to. They are unglamorous on purpose. The most consequential systems we have ever shipped were built inside them.
- 01
The work itself
Selling is not the work. The work is the system that runs after we leave. Everything else is overhead.
- 02
Decision over decoration
Every artifact is judged by the decision it changes. A dashboard nobody acts on is a defect, not a deliverable.
- 03
Production is the deliverable
A prototype is a sketch of a system. The system is what we are paid to build, and the only thing the institution will rely on.
- 04
Evidence-grade
Documentation, lineage, evaluation and runbooks ship with the build. When an oversight function asks, the answer is already on the shelf.
- 05
Selectivity over scale
We turn down more work than we take. The shape of an engagement is the strongest signal of how it will end.
- 06
Quiet by default
Engagement details — clients, sectors, named outcomes — are confidential by default. The work speaks where it can; we do not.
Where the
work lands.
Four sectors where engineering discipline is the difference. Same craft, different operating context.
All industries
Public-Sector Operations
Mission-critical operations and oversight programs, program analytics, document intelligence and operating cockpits built inside the controls the program already lives under.
Financial Services
Risk, forecasting and decision systems for investment, banking and insurance teams, designed against SR 11-7 and the regulator in the room.
Healthcare
Clinical-adjacent analytics, population health insight and operations intelligence, HIPAA / HITECH from day one, not as a follow-up project.
Enterprise
Modernizing analytics, embedding AI in the workflow, and replatforming legacy reporting estates, without the business going dark.
Frameworks the work runs inside.
Every engagement ships with the evidence an oversight function actually asks for. Model cards, evaluation reports, data lineage, decision provenance, runbooks. Assurance is part of the build, on day one.
- SOC 2 Type II
- ISO 27001 aligned
- HIPAA / HITECH
- GLBA
- SR 11-7 model risk
- NIST AI RMF
- NIST CSF 2.0
- PCI DSS 4.0
- GDPR · CCPA
- EU AI Act readiness
- Customer-tenant deployment
- Evidence-grade documentation
Selected
engagements.
A short list of the engagements we’re built for. Specifics, names, sectors, numbers, shared under written agreement.
All work- Financial Services
A multi-strategy asset manager
Productionized analyst-built signals through a shared feature store and walk-forward harness, with SR 11-7-style documentation. Time-to-production reduced from quarters to weeks.
Read more - Public-Sector Operations
An institutional oversight program
Consolidated program telemetry from twelve sources into a single operating cockpit, retiring the weekly briefing deck. Decisions now move at the cadence the data does.
Read more - Insurance
A 50-state insurance carrier
Replatformed regulatory reporting onto a governed warehouse with rebuild-from-source lineage. Audit cycle shortened by a factor of five; analysts moved off reconciliation work.
Read more - Healthcare Operations
An academic medical system
Document-AI pipeline for prior-authorization extraction with confidence scoring and a human review queue. Manual review volume reduced by approximately 70%.
Read more
Diagnose. Build.
Steward.
Every engagement enters through Diagnostic, ships through Build, and stays with the team that built it. Stewardship is the deliverable that matters longest.
- 01
Diagnose
The decision worth getting right, the data that can inform it, an honest read on whether the work is set up to succeed. Two to four weeks.
- 02
Prototype
A working prototype on your data, in weeks not quarters, so stakeholders can see, test and refine the shape of the system before it goes anywhere.
- 03
Harden
The prototype becomes a production system, instrumented for the metrics that matter, hardened against the conditions it will actually meet, documented for the reviewer in the room.
- 04
Steward
The team that built the system stays with it. Drift, retraining, eval regressions, runbooks, handled where the system lives, not in a follow-up project.
A modern,
opinionated stack.
Boring, dependable tools where they fit. Leading-edge ones where they unlock genuine new capability.
Languages & ML
- Python
- R
- SQL
- scikit-learn
- PyTorch
- TensorFlow
- LangChain
Data & Cloud
- Azure
- AWS
- Snowflake
- Databricks
- dbt
- Airflow
- Postgres
Visualization & Apps
- Tableau
- Power BI
- Plotly
- Next.js
- TypeScript
- FastAPI
Writing from the
work itself.
Written by the people who do the work. Read once, useful for a year.
All insights
Dashboards as decision systems, not decoration
Most executive dashboards fail not because the data is wrong, but because they answer the wrong question. A short tour of what changes when you start with the decision.
Read · 6 min
Where AI actually fits, and where it doesn’t (yet)
A practical lens for distinguishing the AI use cases that compound from the ones that quietly burn cycles. Drawn from work across regulated industries.
Read · 8 min
The shape of modern data platforms
A high-level blueprint for the data platform we keep recommending to mid-sized organizations, and the trade-offs behind each layer.
Read · 10 min
Semantic layers in practice: dbt, Cube, LookML
A side-by-side look at the three semantic-layer options we keep choosing between, and the constraints that push us toward each.
Read · 9 minDesigning evaluation harnesses for LLM workflows
How we structure regression suites for AI workflows so quality is measurable, not vibes-based. With concrete examples from regulated environments.
Read · 9 min
PII handling patterns for analytics platforms
A practical pattern library for tagging, masking, segregating and auditing PII across modern data stacks. The defaults we set, and why.
Read · 8 minThe data-product operating model, end to end
Roles, intake, SLAs, on-call. The operating model that survives the analyst attrition the firm forgot to plan for.
Read · 11 minShipping ML into regulated environments
What it actually takes to move a model from notebook to production inside SR 11-7, HIPAA or analog institutional controls. The artifacts, the reviewers, the order.
Read · 10 minHave a workload worth getting right?
If you’re scoping a system someone will rely on, replacing a reporting estate that no longer holds up, or evaluating where AI genuinely belongs in your operations, we’d like to hear about it.