Compute spend has no owner
One line on the bill. Nobody can attribute it.
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Technology & AI
You build the model. We build what it stands on.
Training and serving infrastructure, evaluation harnesses and the security posture enterprise buyers demand.
- Serving economics, modelled
- Evals wired into CI
- Enterprise-ready day one
The starting point
What 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
Shipped on vibes, reverted on complaints.
Enterprise deals need answers
SOC 2, residency, isolation — all blocking.
Our approach
The unglamorous layer decides whether you scale.
Your model is the product. The pipelines, serving tier, eval harness and security posture are what let you sell it to someone serious.
How we work →-
Serving that scales down.
Autoscaling, batching, caching.
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Evaluation you can ship against.
Upgrades become a reviewable diff.
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Cost attributed per customer.
Per tenant, feature and experiment.
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Enterprise-ready from the start.
Designed in, not retrofitted in procurement.
Proof
How we have solved it in Technology & AI.
Challenge, solution, outcome. Names withheld, numbers not inflated.
AI product company
- Challenge
- Compute was one line on the bill with no owner.
- Solution
- Per-tenant and per-feature cost attribution on every request.
- Outcome
- Pricing and roadmap decisions got a real number behind them.
ML platform team
- Challenge
- Training jobs queued behind each other on shared capacity.
- Solution
- Scheduled clusters with autoscaling and proper observability.
- Outcome
- Experiment throughput rose without buying more hardware.
Series-A startup
- Challenge
- Enterprise procurement blocked on isolation and audit logging.
- Solution
- Tenancy boundaries, audit trails and a residency option designed in.
- Outcome
- The enterprise tier shipped without a rebuild.
Teams we work with
Who we build for
Different teams, different first project. The platform underneath is the same.
AI product companies
Serving, evaluation and the reliability layer.
ML platform teams
Training clusters, pipelines, experiment infrastructure.
Security leads
The evidence enterprise procurement asks for.
Founders and CTOs
What to build, what to buy, what to delay.
Scope
What we deliver
Four things, in this order. Each one is useful on its own.
Training and serving
Clusters, scheduling, autoscaling, observability.
Evaluation harness
Behavioural regression tests wired into CI.
Data pipelines
Ingestion, labelling, versioning and lineage.
Enterprise readiness
Isolation, audit logging, residency, the doc pack.
Engagement
How the engagement runs
No surprises about sequence, and no invoice before there is something to look at.
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Architecture review
Two weeks inside your stack
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Fix the bottleneck
The one that is actually binding
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Harden for enterprise
Mapped to the deals you are chasing
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Run or hand over
Under SLA, or with runbooks
Under the hood
What it is built on.
Your existing systems stay. We add the layers that are missing and run them.
Your stack
- Kubernetes and Terraform
- Your CI and registries
- Feature and vector stores
- Your observability tooling
AI layer
- Training and fine-tuning pipelines
- Inference serving and routing
- Behavioural regression tests
- Data lineage and versioning
Infrastructure
- Bare-metal GPU clusters
- Autoscaling and spot scheduling
- Per-tenant cost attribution
- Audit logging and residency
Private cloud
Bare metal when the maths says so.
We model your sustained utilisation and tell you where dedicated capacity beats on-demand. If that is where it lands, we will run it for you.
Explore private cloud- Bare-metal GPU clusters
- Autoscaling to zero
- Per-tenant cost attribution
- Managed under SLA
What changes
- Per-tenant
- Cost on every request
- In CI
- Regressions caught pre-release
- Autoscaled
- Capacity follows load
Worked examples
The write-ups behind this work.
Full situation, architecture and results on each one.
Infrastructure modernisation
Modernising an estate nobody had a complete map of
400 Hosts mapped, none by hand
Cybersecurity
A SOC drowning in alerts that were almost all noise
11k → 40 Reaching an analyst weekly
Legacy modernisation
Changing a system nobody was willing to touch
11,400 Cases pinning behaviour
Free, no call required
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, including the ones that are not us.
Get the checklist- Which bottleneck is actually binding
- What your evals have to catch before release
- Which enterprise controls block deals today
- Where dedicated capacity beats on-demand
Questions
Asked first, every time
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 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 weight toward infrastructure and evaluation. If the modelling work is your core differentiation, 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.
Vocabulary
Terms worth knowing before the first call.
The words that come up most in Technology & AI conversations, in plain English.
What is limiting your scale right now?
Cost, reliability or a procurement blocker. Tell us which.
- 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.
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