GPU spend has no owner
Training and serving costs are one line on the cloud bill and nobody can attribute them.
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Technology & AI
You build the model. We build everything it stands on.
For AI-native companies: training and serving infrastructure, evaluation harnesses, data pipelines and the security posture your enterprise customers will demand.
The three things we hear in the first meeting.
If none of these sound like you, we are probably not the right call yet — and we will say so.
Model upgrades are a gamble
There is no regression suite, so every new checkpoint is shipped on vibes and reverted on complaints.
Enterprise deals need answers you do not have yet
SOC 2, data residency, tenant isolation — all solvable, all currently blocking a contract.
The unglamorous layer decides whether you scale.
Your model is the product. The pipelines, the serving tier, the eval harness and the security posture are what let you sell it to someone serious.
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Serving infrastructure that scales down.
Autoscaling, batching and caching, so idle capacity is not the biggest line on the bill.
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Evaluation you can ship against.
A regression suite for model behaviour, so upgrades are measured rather than hoped for.
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Cost attributed per customer.
Know what each tenant, feature and experiment actually costs to run.
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Enterprise-ready from the start.
Isolation, logging and residency designed in, not retrofitted during a procurement cycle.
Who we build for
Different teams, different first project. The platform underneath is the same.
AI product companies
Serving, evaluation and the reliability layer around your model.
ML platform teams
Training clusters, data pipelines and experiment infrastructure.
Security and compliance leads
The architecture and evidence enterprise procurement asks for.
Founders and CTOs
An honest read on what to build, what to buy and what to delay.
What we actually deliver
Four things, in this order. Each one is useful on its own.
Training and serving stack
GPU clusters, scheduling, autoscaling and the observability around them.
Evaluation harness
Behavioural regression tests wired into CI, so a model change is a reviewable diff.
Data pipelines
Ingestion, labelling workflows, versioning and lineage for training data.
Enterprise readiness
Tenant isolation, audit logging, residency options and the documentation pack.
How a technology engagement runs
No surprises about sequence, and no invoice before there is something to look at.
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01
Architecture review
Two weeks inside your stack. We come back with the three things limiting scale.
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02
Fix the bottleneck
Usually serving cost, eval coverage or data pipeline reliability. We fix the one that is binding.
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03
Harden for enterprise
Isolation, logging and residency work, mapped to the deals you are trying to close.
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04
Run or hand over
We operate the platform under SLA, or hand it to your team with the runbooks.
What good looks like
- Per-tenant
- Cost attribution on every request
- In CI
- Model regressions caught before release
- Autoscaled
- Capacity follows load, both directions
- Documented
- Security answers ready before procurement
The Technology & AI AI readiness checklist
The questions we work through before recommending anything — data readiness, hosting constraints, review process and the running cost at year two. Use it with any vendor.
Technology questions we get asked first
Short answers. Longer ones are a conversation.
We already have a platform team. What do you add?
Usually depth in one area they have not had time for — serving economics, evaluation infrastructure or the enterprise security work. We work alongside your team, not instead of it, and we are explicit about where we are not needed.
Can you help us move off a hyperscaler?
Yes, and we will also tell you when not to. Migration makes sense at certain sustained utilisation levels and is a distraction below them. We model your actual usage before recommending a direction.
Do you do model training or just infrastructure?
Both, though most engagements are weighted toward infrastructure and evaluation. If the modelling work is your core differentiation, you should keep it in-house and we will build around it.
How do you charge?
Fixed-scope for discovery and defined builds, monthly retainer for managed infrastructure. Both quoted before work starts.
What is limiting your scale right now?
Cost, reliability or a procurement blocker. Tell us which and we will tell you how we would attack it.
- A reply within one working day, from an engineer rather than an account manager.
- An honest read on whether this is worth doing now, or in a year.
- No marketing list. Your details reach the solutions team and stop there.