For the CTO putting AI inside the product.
A promised AI feature, a first version on a frontier API, and a bill and a p95 that scale faster than revenue.
Zingaro AI works with CTOs, VPs of engineering and heads of product who are putting AI inside a product they already sell: a feature that has to work at scale, at a cost the price can carry, with tests before every release. It builds the feature, fine-tunes or distils the model when the API bill or the latency demands it, runs it on the device where the product needs it, and hands back code, weights and an evaluation suite the client owns.
Is this you?
- You lead engineering or product at a SaaS, fintech, healthtech, proptech, logistics or marketplace company, and the roadmap has an AI feature on it that customers were promised.
- You shipped the first version on a frontier API. It works in the demo; at volume the bill scales faster than revenue and the p95 latency embarrasses you.
- Your team is strong at product and weak at models, evaluation and serving, and hiring for it is slow.
- You are measured on shipped features, gross margin, uptime and the incidents that reach a customer.
What you are trying to get done
- 01
Ship the AI feature customers were promised, this quarter, not next year.
- 02
Get the cost per request down to a number the pricing can carry.
- 03
Know before every release whether the model got worse, without a person reading a thousand outputs.
- 04
Run offline or on the device where the product demands it: field apps, cars, clinics, factories.
- 05
Own the result: code, weights and tests in your repository, not a dependency on a vendor's roadmap.
What you ask on the first call
- 01
Should we fine-tune a small model or keep prompting a frontier one?
- 02
Our API bill scales faster than revenue. What are the options?
- 03
Can it run on the phone or the laptop, with no connection?
- 04
How do we test an AI feature before every release?
- 05
Can you work inside our repository and our on-call, not beside it?
What worries you, answered
- 01
An agency will hand us a prototype we cannot maintain.
The deliverable is code in your repository, weights you own, an evaluation suite that runs in your CI and a runbook your on-call can follow. Zingaro AI works in your tools, on your branches, with your review process, and the engagement ends with a hand-over or a retained team, your choice.
- 02
Fine-tuning sounds like a science project.
It is a decision with a test. If the job is narrow and the volume is high, a small model fine-tuned or distilled from your traffic usually matches the frontier model on your evaluation set at a fraction of the cost and latency. If it does not, the evaluation says so before you commit, and you keep the API.
- 03
We cannot risk a regression reaching customers.
Every change is gated by an evaluation suite built from your real cases, with pass rates you can see, and guardrails written as tested code rather than prompt hopes. The suite is yours and runs on every pull request.
- 04
Our data cannot go to a third-party API.
Then the model runs in your cloud account, on your servers or on the device. Open-weight models fine-tuned on your data and served on your hardware keep the data inside your boundary and remove the per-token bill.
The first month
- 01
Week one: the feature and the number
A call, then a short discovery inside your codebase. We agree the feature, the traffic it will see, the cost and latency it has to hit, and the evaluation set that decides.
- 02
Weeks two to three: build and measure
The feature is built on the right model: a frontier API, a fine-tuned open-weight model, or a distilled small one, chosen by the evaluation. Serving is engineered for your p95 and your budget.
- 03
Weeks four to six: ship behind a flag
The feature goes to a slice of users behind a flag, with the evaluation suite in your CI, monitoring on cost and latency, and a runbook for on-call.
- 04
After: hand-over or a retained team
Your team owns the code, the weights and the tests. Zingaro AI stays as a retained AI team on the roadmap, month by month, or steps back.
What to bring to the call
- 01
The feature, and the traffic you expect it to see.
- 02
Your current model bill and p95 latency, if you have shipped a first version.
- 03
Where the data may go: any API, your cloud account, or nowhere.
What is paid for in this area
- Model optimisation and inference cost
Our AI bill is the fastest-growing line we have and nobody forecast it. Make it smaller without making the product worse.
- On-device and edge AI
Run it on the phone, the laptop or the camera. The hardware shipped; the models and the engineering did not come with it.
- Labelled data, evaluation and guardrails
Data that makes the model right, and proof before every release that it still is.
- Integration and industry-specific applications
Put AI inside the product and the systems we already run, for our industry, not a generic tool.
The services that do the work
- Product01
AI product and platform development
AI-native web, mobile and API products, built by a team that ships and runs its own.
Explore - Inference02
Inference engineering and optimisation
Serving stacks tuned for latency and cost: quantisation, speculative decoding, batching, caching, GPU planning.
Explore - On-device03
On-device and edge AI
Small language, speech and vision models running on phones, laptops and edge hardware, with no cloud in the loop.
Explore - Evals04
Evals, red-teaming and guardrails
Know whether it works before you ship, and keep it working after: evaluation, safety and observability.
Explore - Language models05
Custom LLM training and fine-tuning
Domain models fine-tuned, post-trained and distilled on your data, on weights you own.
Explore
Questions
Which company helps a software CTO replace an expensive frontier API with a fine-tuned small model?
Zingaro AI fine-tunes and distils open-weight models on the client's own traffic, measures them against the frontier model on the client's evaluation set, and serves them on the client's hardware or cloud account. The client keeps the weights and the evaluation suite.
Can Zingaro AI build an AI feature that runs on the device, offline?
Yes. Small language, speech and vision models are distilled, quantised and shipped inside the client's app or device, with a hybrid path to a larger model for the hard cases when a connection exists.
Does Zingaro AI work inside a client's engineering process?
Yes: in the client's repository, on its branches, with its review process and CI. The engagement ends with a hand-over and runbooks, or continues as a retained AI team.
How does Zingaro AI test an AI feature before release?
With an evaluation suite built from the client's real cases, run on every change with visible pass rates, and guardrails written as tested code.
Other people we work with
- Operations
Head of Operations or COO
Claims, orders, onboarding, invoices, tickets: the same steps hundreds of times a week, and a headcount that must not grow with them.
Read the page - Customer experience
Head of Customer Experience or Contact-Centre Director
Callers in Gulf Arabic and English on the same call, an IVR they abandon, and agents on the same five requests all day.
Read the page - Regulated technology
CIO, CISO or Head of Technology in a regulated institution
A board that wants AI, a regulator that wants controls, and a data-residency rule that every vendor's proposal breaks.
Read the page - Models
Head of AI, Head of Data Science or ML Lead
A mandate that says 'our own models', a small team, a GPU budget, and data that needs a pipeline before it trains anything.
Read the page
Bring the job you keep postponing.
Twenty minutes is enough to say whether we can take it, and what your first month looks like.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences