AI data infrastructure
AI data infrastructure for pipelines, training and inference
AI systems inherit every problem in the data underneath them. Duplicate records, missing events, stale tables and inconsistent IDs turn into wrong answers, bad fine-tunes and features that can't be trusted.
- Pipelines and orchestration
- Warehouse modelling
- Event and feature pipelines
- Fine-tuning datasets
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Signs you need this
We build the data layer first. Pipelines with tests and lineage, a warehouse with clean models, event streams for real-time features, curated datasets for fine-tuning, and serving infrastructure that keeps inference fast and its cost visible.
- Models trained or prompted on data nobody has validated
- Pipelines that fail silently and get noticed weeks later
- No consistent customer or product ID across systems
- A fine-tune that made things worse and nobody knows why
- GPU bills with no breakdown by model or feature
- Real-time features built on nightly batch jobs
What you get
What is included
Pipelines and orchestration
Batch and streaming pipelines with Airflow, Dagster or managed tools, tested, monitored and alerting on failure.
Warehouse modelling
Snowflake, BigQuery or Postgres modelled with dbt, with tests on every table and documented lineage.
Event and feature pipelines
Clickstream and product events captured once and served as features for models, personalisation and analytics.
Fine-tuning datasets
Curated, deduplicated and labelled datasets with held-out evals, built from your data with clear provenance.
Model serving
Hosted endpoints or self-hosted inference on vLLM or similar, with autoscaling, batching and latency targets.
Inference cost control
Spend by model, route and customer, with caching, batching and right-sized hardware to bring it down.
How it runs
How we deliver AI data infrastructure
- 01
Data audit
Map sources, quality problems and the data each AI use case actually needs.
- 02
Core pipelines
Build and test the pipelines and warehouse models the first use case depends on.
- 03
Training and serving
Datasets, fine-tunes if warranted, and serving infrastructure with cost and latency monitoring.
- 04
Operate and extend
Alerting, data quality checks and documentation, then extend to the next use case on the same base.
Industries we build this for
AI data infrastructure questions
Something else? Email t@moher.ai and an engineer will answer it.
Do we need to fine-tune a model?
Usually not first. Prompting and retrieval solve most problems. Fine-tuning earns its cost when you need a consistent format or narrow skill at high volume, and you have clean examples to train on.
Which warehouse should we use?
Whichever your team can operate. Postgres covers more than most teams expect. Snowflake and BigQuery make sense at larger scale or with heavy analytics workloads.
Can you cut our inference costs?
Usually. Caching, batching, smaller models for simple tasks and right-sized hardware often reduce spend substantially without a quality drop, measured against your eval suite.
Should we self-host models?
Only when volume, data residency or a fine-tuned model justifies the GPU cost and on-call burden. We model both options before recommending one.
Do you work with our existing data team?
Yes. We work in your repos and tools, follow your conventions, and leave documentation your team can maintain.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.