AI & IT Solutions
Build the platform that makes AI repeatable rather than heroic.
Seaggle builds the data pipelines, retrieval and feature infrastructure, cloud footprint, and deployment path that turn a working prototype into something your team can ship again next quarter.
When this fits
Signals that this is the right conversation
Most stalled AI programmes are blocked by plumbing, not modelling: data that arrives late or malformed, no repeatable path from experiment to production, GPU spend nobody can attribute, and every deployment a bespoke effort. Fixing that is unglamorous and it is usually the highest-value work available.
- Every model deployment is a bespoke project rather than a repeatable path
- Prototypes work locally and stall when they meet real data volumes and permissions
- Nobody can explain the GPU or inference bill by team, model, or feature
- Retrieval and feature infrastructure was stood up for one pilot and never made durable
- Data quality problems are discovered by consumers rather than by tests
What Seaggle delivers
Deliverables, arranged by when they arrive
Each of these is an artifact you receive and can use without us in the room.
Assess
- Architecture review of data flow, storage, retrieval, and serving
- Cost model for compute, inference, and storage with attribution by workload
- Path-to-production audit: what actually blocks a model reaching users
- Governance review — lineage, PII classification, and access boundaries
Build
- Ingestion and transformation pipelines with tests, contracts, and schema-change handling
- Vector and feature infrastructure sized to real query patterns, not benchmarks
- Training and inference infrastructure with GPU scheduling and autoscaling
- CI/CD for models and prompts, with evaluation gates in the pipeline
Operate
- Observability across data freshness, model performance, cost, and latency
- Model registry, versioning, and reproducible environments
- Rollback and canary paths for models and prompts, not just application code
- Runbooks and enablement so your platform team owns it
Solution patterns
Shapes this work commonly takes
These are patterns rather than products. Select one to see how it works, where a person stays in the loop, and what we design against.
Retrieval infrastructure that stays current
A vector store was populated once for a pilot and has been drifting from source ever since.
- How it works
- Incremental indexing tied to source change events, with deletion and permission propagation, re-embedding strategy on model change, and index health monitoring.
- Human checkpoint
- Index freshness and coverage are visible on a dashboard rather than assumed.
- Risks we design against
- Silent staleness and orphaned permissions. Handled with change-data capture, tombstoning, and reconciliation jobs.
Repeatable path to production
Each model reaching users is a one-off effort with a different shape.
- How it works
- A paved road: templated training pipelines, a registry, evaluation gates in CI, and standard deployment targets so shipping the second model costs a fraction of the first.
- Human checkpoint
- Promotion to production is an explicit approval against evaluation results.
- Risks we design against
- A paved road nobody uses. Avoided by building it with the teams who will use it and migrating one real workload onto it first.
Cost attribution and control
Inference and GPU spend is growing and cannot be explained by team or feature.
- How it works
- Tagging and instrumentation per workload, budgets and alerting, caching and routing between model tiers, and rightsizing based on measured usage.
- Human checkpoint
- Budget thresholds notify owners before they are breached, not after.
- Risks we design against
- Optimising cost into a quality regression. Prevented by holding evaluation constant while cost changes are made.
Representative workflow
End to end, with the checkpoints visible
A worked example of how the pieces connect in production. The static sequence below is the whole content — nothing is hidden behind animation.
- 01
Map the current path
From raw source to a decision reaching a user, including where it stalls.
- 02
Fix ingestion first
Contracts, tests, and schema-change handling — everything downstream depends on it.
- 03
Make retrieval durable
Incremental indexing, permission propagation, and health monitoring.
- 04
Pave the deployment road
Templates, registry, and evaluation gates so shipping is repeatable.
- 05
Instrument cost and quality
Attribution by workload, with budgets and alerting.
- 06
Migrate one real workload
Prove the path with production traffic before asking anyone to adopt it.
- 07
Hand over the platform
Runbooks, enablement, and ownership transferred to your team.
Representative example
Evaluation & controls
What separates production work from a demonstration
A demonstration proves something can happen once. These controls are how you know it keeps happening correctly.
Data contracts and tests
Freshness, volume, and schema expectations are asserted in the pipeline, so a producer's change fails loudly instead of silently corrupting a model's inputs.
Reproducible environments
Pinned dependencies, versioned data, and captured configuration, so a result can be reproduced months later by someone who was not there.
Evaluation gates in CI
Models and prompts pass the same discipline as application code — a change that regresses quality does not reach production because a human forgot to check.
Cost attribution
Compute and inference tagged by workload and owner, with budgets and anomaly alerting, so spend is a managed number rather than a monthly surprise.
Least-privilege access
Access to data and models scoped and audited, with secrets handled through your approved mechanism rather than a config file.
Rollback for models
Versioned models and prompts with canary and rollback paths — the same operational safety application code has had for years.
Technology we work with
Named to explain the work rather than to imply endorsement. Tool choices follow the requirement, and we work with what you already run wherever that is sensible.
- AWS / Azure / GCP
- Kubernetes
- Terraform
- Airflow / Dagster
- dbt
- Snowflake / BigQuery / Databricks
- Kafka
- Ray
- MLflow
- OpenTelemetry
Getting started
Where a first engagement begins
A platform assessment ending in a prioritised roadmap, plus one real workload migrated onto the new path so the approach is proven rather than promised.
Discuss platform workQuestions
Do we have to replace our existing stack?
Almost never. We start from the environment you already run and recommend the smallest responsible change that unblocks the work. Replacing a platform is a recommendation we have to justify with arithmetic, not a default.
Can this run entirely in our own cloud?
Yes. Everything is deployed into your accounts under your controls. Where data residency or regulatory boundaries apply, we design to them and document the boundary for your auditor.
How do you keep inference costs under control?
Attribution first — you cannot manage what you cannot see. Then caching, routing between model tiers by task difficulty, batching, and rightsizing, with evaluation held constant so cost reductions do not quietly become quality regressions.
Discuss platform work
Bring the problem, the current environment, and what better should look like. We will tell you what is realistic before anyone signs anything.