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Data Engineering & AI

Bad data is why most AI projects fail.

AI projects spend significant time on data, not models.

We build the pipelines, warehouses, and feature stores that your AI actually runs on. Not nice-to-haves - the infrastructure your models depend on to produce reliable predictions in production.

What we build

Ingestion pipelines
Streaming and batch ingestion from any source - databases, APIs, event streams, SaaS tools - with schema validation and error handling built in.
Transformation & modelling
dbt-powered transformation layers, data models that serve both analytics and AI, with full lineage tracking from source to output.
Feature stores
Centralised feature registries with point-in-time correct joins, online/offline consistency, and versioning for reproducible model training.
Data warehousing & analytics
Cloud data warehouse design, query optimisation, and semantic layers that give analysts and AI systems access to the same trusted data.

The data readiness spectrum

1
Raw logs
Nothing - unusable as-is
2
Cleaned data
Basic analytics and reporting
3
Structured data
BI dashboards and SQL queries
4
Feature-ready
Model training and batch inference
5
AI-ready
Real-time inference, streaming ML, and autonomous agents

Our data stack

Ingestion
KafkaFirehoseAirbyte
Transform
dbtSpark
Storage
SnowflakeBigQueryRedshift
Orchestration
AirflowPrefect
Feature store
FeastTecton

Feature store deep dive

Why feature stores matter for AI

Without a feature store, the same feature is computed differently in training and serving - leading to training-serving skew that silently degrades model performance. Feature stores enforce consistency, reduce duplication, and let teams reuse high-value features across multiple models.

They also enable point-in-time correct lookups for backtesting and retraining - ensuring your model was never accidentally trained on future data.

What we build in them
Online store for low-latency real-time inference
Offline store for batch training and backtesting
Point-in-time correct feature retrieval
Feature versioning and deprecation workflows
Monitoring for feature drift and data freshness
SDK integration with your training and serving code

Compliance & governance

Data lineage
End-to-end tracking of where every field comes from and how it has been transformed.
PII handling
Automated detection, masking, tokenisation, and deletion workflows for personal data.
Access control
Column- and row-level security, role-based permissions, and integration with identity providers.
Audit logging
Immutable logs of every read and write operation for compliance and forensic review.
Result
"Built a real-time feature pipeline processing 800M events/day for a fintech AI system"

Architecture

How your data moves through the stack

Sources
  • Databases
  • REST APIs
  • SaaS tools
  • Event streams
  • Files / S3
›
Ingest
  • Kafka topics
  • Airbyte connectors
  • Firehose delivery
  • Schema registry
  • Dead-letter queue
›
Transform
  • dbt models
  • Spark jobs
  • Data quality tests
  • Lineage tracked
  • Incremental loads
›
Store
  • Snowflake / BQ
  • Feature store
  • Vector DB
  • Data lake (S3)
  • Redis cache
›
Consume
  • ML training runs
  • Real-time inference
  • BI dashboards
  • Operational APIs
  • Agent tools

By the numbers

800M+

Events per day processed on our largest fintech pipeline

60%

Of AI project time typically spent on data - we cut this dramatically

<2min

Data freshness latency achieved in our streaming feature stores

99.9%

Pipeline uptime SLA maintained across production deployments

Before & after

What we typically inherit vs. what we leave behind

Before - typical legacy state
# No orchestration - cron jobs in production
SELECT * INTO analytics FROM raw_events;
# 14-day stale features used for ML training
cp data.csv /mnt/nfs/ml_team/today/
# PII in plaintext across 6 S3 buckets
# 'pipeline' = 400-line bash script
After - StartxLabs data platform
# Airflow DAGs with retry + alerting
-- dbt model: user_features_v2
SELECT user_id, agg_spend_30d ...
# Feast online store - <2ms latency
# Point-in-time correct joins for training
# PII masked at ingestion, audit log on

How we engage

Discovery → Design → Build → Deploy → Monitor

Wk 1
Discovery
  • Current state audit
  • Source system inventory
  • Data quality assessment
  • Stakeholder interviews
Wk 2
Design
  • Pipeline architecture
  • Schema design
  • Tool selection
  • SLA definition
Wk 3–8
Build
  • Connectors built
  • dbt models written
  • Tests & contracts added
  • Feature store deployed
Wk 8–10
Deploy
  • Production rollout
  • Backfill execution
  • Alerting configured
  • Runbooks written
Ongoing
Monitor
  • Freshness alerts
  • Drift detection
  • Usage dashboards
  • On-call support

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in TouchSee Our Work

What happens next

1

Send us a message

Tell us what you're building or what's broken.

2

Discovery call (30 min)

We ask hard questions. You get honest answers.

3

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Contact Us

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

  • Agentic AI development and multi-agent systems
  • Generative AI consulting and LLM integration
  • RAG development and custom model deployment
  • Data engineering, MLOps and custom software
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