How we work

Foundation first. Always in this order.

Fixed-scope engagements, presented in the sequence we recommend. You cannot build reliable intelligence on an unreliable foundation — so we fix the foundation first.

Where most companies are

—Data scattered across 8–15 disconnected systems
—No single source of truth for any key metric
—Pipelines built by one person who has since left
—Data quality unknown — teams don't trust the numbers
—60–70% of time spent finding and cleaning data
—AI projects start — and stall — repeatedly

What becomes possible

✓All data unified, trusted, and under your control
✓AI-powered insights grounded in real, clean data
✓Custom LLM applications built exactly for your needs
✓Engineers shipping AI features in days, not months
✓AI agents handling routine ops — humans only when needed
✓Compounding productivity gains across every team

Data Foundation Audit

The first thing every company should do before any AI investment.

Timeline3–4 weeks

Best forFunded startups & mid-market companies

A structured diagnostic of your entire data landscape. We map where your data lives, how it flows, where it breaks, and exactly what needs to happen before AI can be reliably built on top of it.

Final deliverables

→Data landscape map — every source, system, API, and pipeline documented
→Data quality scorecard — completeness, consistency, freshness, and trust rated per source
→Pipeline fragility report — what breaks, how often, and who owns it
→AI-readiness score — a frank, numbered assessment of where you actually stand
→Use case opportunity report — which AI applications are feasible now, which require foundation work first
→Prioritised remediation roadmap — what to fix, in what order, with rough effort estimates
→Leadership readout call — 60-minute walkthrough of findings and recommendations

Data Mobilisation & Unification

Our core offering. The foundation that makes everything else possible.

Timeline6–12 weeks

Best forCompanies scaling data infrastructure

We consolidate your fragmented data landscape into a single trusted platform you own and control. Everything unified, quality-checked, documented, and ready for AI to be built on top of.

This package is the prerequisite for all AI work. We strongly recommend completing it — or validating the equivalent is in place — before any LLM or ML project begins.

What is included

→Data source inventory — every system, database, API, and file store mapped
→Pipeline architecture design — ingestion, transformation, storage, serving
→Implementation — ETL/ELT pipelines from all identified sources
→Data quality framework — validation rules, monitoring, and alerting
→Data catalogue — every asset documented with owner, lineage, and usage
→Single data platform — lakehouse, warehouse, or hybrid
→Observability dashboards showing pipeline health and data freshness
→Full documentation and knowledge transfer to your engineering team

AI-Powered BI & Insights

Turn your unified data into intelligence your business can actually use.

Timeline4–8 weeks

Best forData-ready companies needing an intelligence layer

With a solid data foundation in place, the first AI application that delivers immediate value is a custom intelligence layer: dashboards your team can trust, natural-language interfaces that answer real business questions, and automated insight generation tailored to how your organisation actually works.

What is included

→BI architecture design on your unified data platform
→Custom dashboards and reporting — live, trusted, and decision-ready
→Natural-language query interface — ask questions, get answers from your real data
→Automated insight generation — surface anomalies, trends, and opportunities
→Cross-domain analysis — connect data across departments that never spoke before
→Evaluation framework — how do we know the insights are accurate?
→Integration with your existing tools and workflows

Intelligent Application Development

From your data foundation to applications that think, decide, and act.

Timeline4–8 weeks

Best forAI-first companies with clean data

We do not start from a technology and work backwards. We start from your business problems — identify which ones AI can solve, design the right application architecture, and build it. This could be an LLM-powered knowledge system, a predictive analytics engine, an NLP document processor, or an autonomous agent workflow. The technology follows the use case.

Requires Package 2 or equivalent. An intelligent application built on poor data will underperform regardless of the technology.

What is included

→Application scoping and architecture design
→LLM selection — vendor neutral (OpenAI, Mistral, Llama, or open-source)
→RAG pipeline connecting the application to your unified data foundation
→Evaluation framework with baseline metrics and quality monitoring
→Guardrails, monitoring, and production observability
→Full deployment, documentation, and handover
→2 × knowledge transfer sessions with your engineering team

What this enables

→Internal knowledge systems that answer questions from your own documents and data
→Support tooling that genuinely understands your product
→Automated reporting and narrative generation on top of your BI layer
→Agentic workflows that act on your data and systems reliably

Runs alongside all packages

AI Developer Velocity

The fastest-growing companies are not just building AI products. They are using AI to make their own engineering teams dramatically more productive.

Most mid-sized companies know this is happening at the big tech firms — but have not yet built the infrastructure to do it themselves. The gap is not capability. It is time, expertise, and having someone who has done this in production.

What this looks like in practice

Automated PR creation from issue descriptions

AI-assisted code review and suggestion agents

JIRA / Linear ticket triage and auto-resolution for known patterns

AI first-responder on-call — diagnoses, notifies humans only when needed

Automated monitoring with intelligent alerting and root-cause suggestions

Deployment pipeline agents that catch regressions before they ship

Documentation generation agents keeping docs in sync with code

Test generation agents building coverage automatically

MCP connectors linking your entire toolchain — Slack, GitHub, Jira, PagerDuty, Datadog

Custom MCP servers for tools and internal APIs not yet covered

Shared internal agent platform — so every team can build without starting from zero

Skills and tools library: reusable building blocks for all future agents

Our approach — discovery first, then build

Phase 1

AI Velocity Audit & Roadmap

3–4 weeks

Map your current toolchain, developer workflows, and existing automation. Identify the 5–10 highest-impact automation opportunities. Produce a prioritised build plan with effort and ROI estimates for each.

Phase 2

Platform & First Agents

4–8 weeks

Set up the agent deployment platform. Build and deploy the first 3–5 agents from the roadmap. Establish MCP connections across your toolchain. Prove the value before scaling.

Phase 3

Scale & Handover

Ongoing

Expand the agent library. Build custom MCPs for gaps. Document the platform and train your team to build their own agents. Leave you fully self-sufficient.

Not sure where to start?

Every engagement starts with a free 30-minute conversation. No deck. No pitch. We listen.

Start with a conversation