AI & Automation
Data & AI Engineering
The pipelines and platforms AI actually depends on
- Batch and streaming pipelines with real tests
- Vector and relational stores designed together
- Lineage and quality gates, not hope
- 99.5%
- Pipeline success rate
- < 15 min
- Data freshness for AI features
- 40%
- Warehouse spend reduction
Target SLO with alerting on freshness and volume anomalies
Streaming path; batch domains run hourly or daily by design
Typical after partitioning, clustering and query review
AI projects fail on data more than on models
The model is a commodity you rent by the token. The data is yours, and it is where the difficulty lives: three systems that disagree about a customer, a pipeline that silently dropped a fortnight of records, a field that changed meaning in 2023 and nobody documented it.
An automation reasoning over that data will be confidently wrong, and it will be blamed on the AI.
What we build
Ingestion from databases, SaaS APIs, event streams, SFTP drops and the occasional emailed spreadsheet. Idempotent and replayable, so recovering from a bad day is a re-run rather than an incident.
Transformation in dbt, with tests that fail the build rather than warn a channel nobody reads. Documented, so an analyst can find out what a column means without asking the person who wrote it.
Storage modelled for both analytics and retrieval. These are usually treated as separate projects and then diverge; designing them together avoids the fun of an AI feature and a board report disagreeing about revenue.
The retrieval layer. Embedding jobs that keep up with change, chunk-level lineage back to source records, incremental reindexing rather than nightly rebuilds, and permission filters that hold under audit.
Quality gates that mean something
Freshness, volume, schema and business-rule assertions run on every load. When one fails, the pipeline stops and someone is paged — it does not publish a quietly wrong number and carry on.
We also attribute cost per domain. Warehouse spend has a way of tripling without anyone owning the increase; making it visible per team is usually enough to fix it.
How it runs
What the engagement looks like
Phases, not a proposal. Each one has an output you can see.
- 1
Model the domain, not the dashboard
Weeks 1-2We start from the entities your business reasons about. Pipelines built backwards from one dashboard become unmaintainable the moment a second consumer appears.
- 2
Build the ingestion spine
Weeks 2-4Idempotent, replayable loads with schema-change handling. A pipeline that cannot be safely re-run is a pipeline you will be afraid of at 2am.
- 3
Transform with tests
Weeks 4-6dbt models with assertions on uniqueness, referential integrity, freshness and business rules, running in CI on every change.
- 4
Wire the AI path
Weeks 6-8Embedding jobs, chunk lineage back to source rows, incremental reindexing, and permission filters applied at query time rather than bolted on.
- 5
Instrument and hand over
Week 8Freshness dashboards, anomaly alerts, cost attribution per domain, and documentation your analysts can actually use.
Proof
Where we have done this
Freight forwarder and 3PL
Recovering 4.1% of freight spend by auditing every invoice
A mid-sized forwarder audited freight invoices by sampling roughly 5% of them, because auditing the rest by hand was uneconomic — so systematic small overcharges passed unnoticed, and shipment exceptions were routinely discovered when the customer rang to complain.
- 4.1%
- Freight spend recovered in year one
- 100%
- Invoices audited, from a 5% sample
- 74%
- Track-and-trace enquiries auto-resolved
Auto components manufacturer
Cutting quality documentation from 6 hours a shift to 90 minutes
Across three plants, quality engineers spent most of a shift writing non-conformance and inspection documentation by hand, while production reporting arrived a morning late because the MES, the historian and the ERP disagreed about what each machine was called.
- 6 hrs → 90 min
- Quality documentation time per shift
- 71%
- Non-conformance reports drafted automatically
- 1 shift
- Production reporting latency, from next morning
FAQ
Questions we get asked
Do we need a warehouse before doing anything with AI?
Not always. A focused retrieval system over documents can ship without one. But if your automation needs to reason over transactional data, you need a modelled, tested source of truth first — otherwise the automation inherits every inconsistency you have.
Should embeddings live in Postgres or a dedicated vector database?
For most workloads under a few million chunks, pgvector alongside your relational data is simpler, cheaper and easier to keep consistent. Dedicated vector databases earn their operational cost at higher scale or with specialised filtering needs. We will tell you which side of that line you are on.
How do you handle permissions on retrieved data?
Filters are applied at query time from the signed-in user's real entitlements, and the chunk index carries source-row identity so those filters can be enforced. Retrofitting this is painful, which is why it goes in from the start.
Can you work with our existing stack?
Usually, yes — and that is normally the right call. We would rather fix the tests and modelling in the warehouse you already pay for than sell you a migration you do not need.
Talk to someone who does data & ai engineering
Thirty minutes with an engineer who has delivered this, not an account manager. You will get a straight answer on feasibility, rough cost and where it would fail.
Or email [email protected] · we reply within 1 business day