Inference cost is outrunning revenue
Per-token pricing is fine in beta. At scale it quietly turns a 80% gross margin into a 40% one.
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SaaS
Ship the AI feature without the cost curve that follows it.
AI features for software products, built so that gross margin survives adoption. We size the serving stack for your tenth thousand user, not your demo.
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.
Enterprise buyers are blocking the deal
Their security questionnaire asks where the data goes, and 'a third-party API' is the wrong answer.
The feature has been two sprints away for six months
The demo worked. Making it reliable for every tenant is a different engineering problem.
The demo is the easy 20%.
Turning a working prototype into a feature that every tenant can use, that support can debug and that finance can forecast is where most AI roadmaps stall. That is the part we do.
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Predictable unit economics.
We model cost per active user before the build and pick the serving approach that holds at your projected scale.
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Tenant isolation that survives a security review.
Per-tenant data boundaries, retention controls and the documentation your buyers ask for.
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Self-hosted where it pays.
Open-weight models on dedicated GPUs often beat API pricing past a threshold. We tell you where your threshold is.
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Built into your stack.
Your repo, your CI, your observability. We leave behind code your team owns, not a black box.
Where we plug in
Different teams, different first project. The platform underneath is the same.
AI feature teams
From prototype to a feature every tenant can switch on, with evaluation and guardrails.
Platform teams
Serving infrastructure, autoscaling, caching and the cost dashboard to go with it.
Security and compliance
The architecture answers for SOC 2, ISO 27001 and enterprise questionnaires.
Product leadership
An honest read on which AI ideas on the roadmap are worth funding this year.
What we actually deliver
Four things, in this order. Each one is useful on its own.
AI Discovery for product teams
Two weeks to rank the roadmap by value, effort and true running cost.
Feature build
Retrieval, agents or fine-tuned models, shipped inside your product and your release process.
Serving infrastructure
Dedicated GPU capacity with autoscaling, so inference cost tracks usage instead of leading it.
Evaluation harness
Regression tests for model behaviour, so the next model upgrade is a decision, not a gamble.
How a SaaS engagement runs
No surprises about sequence, and no invoice before there is something to look at.
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01
Roadmap and cost review
We take the AI items on your roadmap and put a real running cost against each one.
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02
Architecture
Serving approach, tenancy model, data boundaries and the cost ceiling, agreed in writing.
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03
Build inside your stack
Our engineers work in your repo and your sprint cadence, with your team reviewing.
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04
Hand over or keep running
Your team takes it, or we run the serving layer under an SLA. Both are fine.
What changes after we ship
- Flat
- Cost per user as adoption grows
- 100%
- Tenant-isolated inference paths
- 4–8 wk
- Prototype to production feature
- Yours
- Code, in your repo, on day one
The SaaS 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.
SaaS questions we get asked first
Short answers. Longer ones are a conversation.
Should we self-host a model or keep using an API?
It depends on your volume and how much variance you can tolerate. Below a certain steady-state throughput, an API is cheaper and simpler. Above it, dedicated GPUs win — often substantially. We run the numbers on your actual traffic during discovery and show you the crossover point rather than pushing a preference.
Will you work inside our codebase?
Yes, that is the default. Our engineers work in your repository, your branching model and your review process. You own everything we write.
How do you handle multi-tenancy?
Data boundaries are designed before anything is built: per-tenant retrieval scopes, isolated storage, and controls that prevent one tenant's content reaching another's context window. This is the part enterprise buyers probe hardest, so it gets documented properly.
Can you help us answer security questionnaires?
We provide the architecture documentation, data-flow diagrams and control descriptions that those questionnaires ask for. Your team still owns the response, but they are not writing it from scratch.
What if we already have a prototype?
Good — that shortens discovery. We assess what is there, tell you what carries over to production and what needs rebuilding, and price from that.
Tell us what the roadmap promised.
Send us the feature and your rough traffic numbers. We will come back with an approach and a running cost.
- 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.