AI and data work that went into production.

Reporting ran off a legacy operational store: siloed extracts, a short retention window, and analytics competing with the transactional workload.

End-to-end delivery partner — current-state assessment, target lakehouse architecture, migration execution, reconciliation and cutover, handover and enablement.

Target-state lakehouse architecture on open table formats; ingestion and transformation pipelines off the legacy store; a layered data model from raw through conformed to curated; data-quality, reconciliation and cutover controls; historical backfill; a governed semantic layer for BI; documentation and team handover.

• One governed source of truth — siloed extracts and spreadsheet reconciliation retired.
• Analytics decoupled from operations — reporting no longer competes with the transactional workload, so both got faster.
• Full history retained and queryable, where the legacy store held only a limited window.
• Structured and semi-structured data in one place — BI and ML read the same foundation instead of diverging copies.
• Elastic, decoupled storage and compute — capacity follows demand rather than being provisioned for the peak.
• Governance applied once, in the platform — lineage, access control and auditability, not bolted on per report.
• AI-ready by construction — a curated, documented, permissioned foundation that GenAI and ML work can be grounded on without ad-hoc data pulls.

The forecasting capability could not scale with the portfolio, and quality was hard to hold as coverage grew. Buying more compute was not an acceptable answer.

ML engineering partner — diagnose the bottleneck, re-engineer the training and scoring pipeline, and refine the training regime for accuracy.

Profiling of the existing training and scoring pipeline; re-engineered feature computation; parallelised and vectorised training; scheduling and resource-allocation optimisation; a repeatable retraining and validation regime; accuracy work through feature engineering and model refinement; ongoing monitoring of forecast quality.

• Roughly eight times the modelling capacity on the same compute footprint — the gain came from engineering, not from new infrastructure spend.
• Better forecast accuracy from a refined training regime.
• Retraining became routine rather than a project — models keep pace with the market instead of ageing between refreshes.

Ground zero. No data platform, no pipeline, no operating model — and multiple external sources that each needed onboarding.

Data architecture advisor and build partner from the ground up — we set the architecture and the operating model, then built the first working version of both.

Target data architecture across ingestion, processing and serving; a target operating model for the end-to-end data pipeline — ownership, roles, run process and ways of working, sized for a start-up team; an ingestion strategy for multiple heterogeneous external sources; platform selection and setup; a processing and data-quality framework; GenAI prototyping on the resulting foundation; and a staged roadmap with the skills shape needed to run it.

• New sources onboard against a repeatable pattern instead of bespoke code each time — the marginal cost of the next source drops.
• A platform sized for the stage the company is at, with a defined path to scale rather than an architecture it will outgrow.
• Clear ownership — a small team can actually run the pipeline, because the operating model says who does what when it breaks.
• GenAI evidenced before it was committed to — prototypes on real data showed what was worth building.
• The re-platforming cost avoided — the expensive rebuild that follows a first architecture chosen by accident.

A news platform for children, operated end-to-end by AI with only limited human checkpoints — which puts the whole weight of safety, reading level and factual accuracy on the pipeline design.

AI and data design authority for the platform, legal and compliance advisor, and prototyping and implementation partner.

The end-to-end AI editorial pipeline — source ingestion, story selection, rewriting to an age-appropriate reading level, safety screening, factual and consistency checking, and publication; human-in-the-loop gates at a small number of defined checkpoints rather than across the whole flow; content guardrails calibrated for a child audience; legal and compliance advisory; a working prototype, then the production implementation.

• An editorial operation that runs on AI — a publishing cadence a newsroom of that size could not sustain manually.
• Consistent, age-calibrated output — reading level and safety screening are applied by the system to every item, not by whoever is on shift.
• Compliance designed in, not bolted on — the legal position was set before the pipeline was built, which is materially cheaper than retrofitting it.
• Human effort concentrated where it counts — oversight at the gates that matter, instead of review of everything.

Scams do not operate alone — they run as rings, scattered across public platforms, the dark web and the chain. The signal that exposes them sits across parties that have never been read together.

Core AI and data engineering across collection, knowledge construction and GenAI integration.

Social-media collection at scale; dark-web collection; blockchain and on-chain tracing; ontology design and knowledge-graph construction; entity resolution and network detection; GenAI integrated into the analyst workflow; intelligence output into the client's own process.

• Scattered signals related into networks — analysts see the ring, not the single scam.
• Social, dark-web and on-chain evidence in one picture, where these have historically sat in separate tools and separate teams.
• Analyst throughput raised by GenAI on the parts of the work that are reading and summarising rather than judging.

Two large enterprises — a major mainland telecommunications operator and a major insurance firm — with release cadences their test functions could not keep pace with by writing and running cases by hand.

Product and delivery — ASTRA's Agentic QA capability deployed into the client's test function and run alongside their QA team.

Automated test-case generation from requirements and specifications, produced in the client's own templates; vision-driven automated execution — a vision model reads the application screen the way a tester does, rather than depending on brittle element selectors; automated regression runs; automated test-report generation; and traceability from requirement to test case to result.

• Test design shifts from authoring to reviewing — the QA team approves and corrects generated cases instead of writing them from scratch.
• UI automation that survives front-end change, because the agent reads the screen rather than a selector that breaks on every release.
• Coverage expanded without a matching headcount increase.
• A consistent evidence pack for every release — generated, not assembled.