AI & Data Engineering
Data pipelines, LLM features and analytics that move your business from gut-feel to evidence-led decisions.
What this service covers
We help teams that have data but can't act on it — and teams that want to bake AI into the product without burning their roadmap. Our engineers design data pipelines, build analytics layers and ship production LLM features that hold up under real-world traffic and cost constraints.
Every AI / data engagement is grounded in evaluation: we measure baseline performance, instrument every prompt and pipeline, and ship features only when they beat the bar in offline and online tests.
The shape of the engagement
Four things every project under this service is built around.
Data pipelines
Reliable ELT pipelines (Airflow, dbt, Prefect) into warehouses tuned for analytics and ML.
LLM features
RAG, agents, structured extraction and content workflows with evals, guardrails and cost controls.
Analytics & BI
Self-serve dashboards, KPI alerts and semantic layers that finance, ops and product all trust.
ML where it earns
Forecasting, recommendation and classification models in production — with monitoring and retraining built in.
What we build it with
Senior teams, modern tooling — no experiments at your expense.
Data
- Python
- dbt
- Airflow
- Prefect
- PostgreSQL
- Snowflake
- BigQuery
AI / ML
- OpenAI
- Anthropic
- LangChain
- LlamaIndex
- Hugging Face
- pgvector
BI / Ops
- Metabase
- Lightdash
- Superset
- Sentry
- Langfuse
- Weights & Biases
A predictable delivery playbook
Same four phases on every project — adapted to your scope, not improvised.
Data audit
Inventory of data sources, quality and gaps. We benchmark what's usable today and what needs investment.
Pipelines & warehouse
Source ingestion, dbt models and a tested semantic layer that becomes the source of truth.
AI / analytics features
LLM-powered features or ML models built and shipped behind evals and guardrails — never blind.
Operate & improve
Monitoring, retraining triggers, prompt regression tests and ongoing cost-per-query optimisation.
What changes for your business
- Cut reporting cycles from days to minutes with a trusted warehouse and semantic layer.
- Ship AI features that beat measured baselines on accuracy, latency and cost-per-query.
- Catch data-quality regressions before they reach dashboards with automated tests.
- Give product teams a self-serve analytics layer that doesn't require a data team to query.
Common questions
If yours isn't here, ask us directly — we reply within one business day.
We have GPT in mind — what should we know?
LLMs are powerful but unpredictable. We build with evals, structured outputs and fallbacks so features are reliable, not magic-tricks.
Can you work with our existing warehouse?
Yes — we extend Snowflake, BigQuery, Redshift or Postgres-based warehouses rather than insisting on a rebuild.
How do you handle data privacy?
PII detection, redaction, regional data residency and customer-managed keys are all options we design in early.
Do you offer ongoing ML / data ops?
Yes — retainer engagements for data ops, model monitoring, retraining and continuous LLM-feature improvement are common.
Often paired with
Most of our engagements combine two or three of these — here are the closest fits.
Ready to talk about ai & data engineering?
Tell us what you're building — we'll come back with a scoped proposal in under 48 hours.