Solution patterns

Practical systems for real operating teams.

Most of the work we are asked for falls into a few recognisable shapes. These patterns show what a first release usually contains and where it tends to grow next. Each one is shaped around your workflows, data sources and stage of growth.

Data platform

Business data operating layer

Connect APIs, databases, spreadsheets and external feeds into a governed reporting layer your team can trust. Definitions live in one place, refreshes are monitored, and every number can be traced to its source.

  • ETL/ELT pipelines and warehouse models in BigQuery, Snowflake, Redshift or PostgreSQL
  • Data quality checks, alerts and recovery paths
  • Power BI, Tableau, Looker Studio or Streamlit views, with exports that match the screen
Data engineering consulting

Web & app

Customer portal or internal app

Ship a clean interface for customers, operators or administrators with forms, user flows, integrations and analytics. Built around one complete user journey first, then extended as the process matures.

  • Responsive UI and API-backed workflows
  • Role-based access, approvals and audit trails
  • Maintainable code and documented handover
Custom software development

AI & automation

Knowledge assistant for teams

Give a team a controlled assistant over documents, structured data and workflow APIs, with answers grounded in source context and actions limited to what has been approved.

  • RAG over documents, tickets and business data
  • MCP servers and custom AI connectors into approved tools, with human approval
  • Access-aware, evaluated and auditable implementation
AI automation, RAG & MCP MCP servers & AI connectors

Migration

Legacy reporting moved to a cloud warehouse

Move reporting off legacy databases, extracts and spreadsheets into Snowflake or BigQuery without losing the numbers people already rely on. Discovery first, then layers, then a cutover justified by evidence rather than a date.

  • Database discovery, source-to-target mapping and dependency analysis
  • Raw, staging, curated and reporting layers with fact and dimension models
  • Stage-by-stage reconciliation against the reports in use today
Data warehouse migration

Data quality

Reconciliation and validation layer

When two systems disagree every month, the fix is a layer that compares them on a schedule, explains the difference and records any adjustment. The source data stays immutable; the corrections sit above it with an audit trail.

  • Record counts, date coverage, nulls, duplicates, totals and metric comparisons
  • Matching and tolerance rules, outlier detection and alerting
  • Manual-override and whitelist layers that leave the base dataset untouched
Workflow automation

On-chain data

Cross-chain transaction ledger

For teams whose data lives on several networks, a warehouse-backed ledger that consolidates activity into consistent, valued and categorised records that finance and operations can query like any other dataset.

  • Transactions, logs and events decoded with contract ABIs and Web3.py
  • FX reference data, token-level valuation and fee proration
  • Transaction categorisation, staking-reward reconciliation and KPI reporting
Blockchain & on-chain analytics

Two more common shapes

Operational automation and an embedded specialist.

Repeated hand-off, automated

A weekly report assembled from four systems, invoices pushed from one tool to another, records reconciled by hand. We automate the repeatable steps, keep review where judgment is needed, and make failures visible with logs and alerts.

Workflow automation

Senior specialist inside your team

When the shape of the work is still emerging, an embedded data, software or AI consultant working in your tools and your hours is often the fastest route. Capacity, responsibilities and overlap are agreed before the start.

IT staff augmentation

How a pattern becomes a project

Start small, prove it, then extend.

  1. 01

    Pick one workflow

    One report, one journey, one hand-off, one knowledge set. Enough to matter, small enough to judge in weeks.

  2. 02

    Agree acceptance

    What "working" means: which records, how fresh, who can see what, what happens on failure.

  3. 03

    Deliver and decide

    A working release with documentation and monitoring, then a conversation about the next useful increment.

Which pattern fits?

Describe your workflow and we will map it to a first release.

Bring the goal, the systems involved and your preferred working hours. Thirty minutes is enough to propose a sensible first milestone.