Software and AI engineering studio
We take prototypes built with AI tools to production, build custom software and AI features, and make them findable in Google and AI answers. You work with the engineers in your own repository, and every milestone ends with software you can run.
78%
less staff time to onboard each customer
B2B SaaS
10x
more concurrent users on the same platform
SaaS platform
95%
of claim documents processed without manual entry
Insurance
2M+
organic impressions a month from programmatic pages
Healthcare marketplace
Client names are withheld. Each number is the headline result of the case study it links to.
What we build with
Prototype to production
AI tools build good first drafts. What they leave out only shows once customers log in with their own data and the usage bill arrives. We close those gaps and keep what your users already rely on. If a rebuild would cost less than a fix, we say so before any work starts.
Works in the demo
Ready for real customers
Works in the demo: Login works, but data access is not checked per customer
Ready for real customers: Access rules tested on every table and endpoint
Works in the demo: Keys and config that can leak, with spend that has no ceiling
Ready for real customers: Secrets on the server, spend capped, alerts on usage spikes
Works in the demo: Agents and developers working straight on production
Ready for real customers: Separate dev and production, backups, reviewed deploys
Works in the demo: Code no one on the team can safely change
Ready for real customers: Consistent structure, tests that guard behaviour, docs
We read the code, run it, and check what a demo never tests: data access, keys, spend and how changes reach production.
Version control, CI, staging and backups, so changes stop going straight to production.
Access rules, managed secrets, spend limits, logging and evals, added without rewriting what already works.
Documentation and a walkthrough, so your team (or ours) can keep shipping with confidence.
Services
Five services, one engineering team. Start with the one that fits today. The others are there as the product grows.
01AI MVP Studio
AI-built prototypes prove an idea fast. We make them ready for real customers without losing the features your users already rely on.
02Software Engineering
Web applications, APIs and backends built by senior engineers in your repository from the first commit, with tests, staging and monitoring in scope, not sold as extras.
03AI Solutions
We add assistants, document processing and workflow automation to the systems you already run. Every model call is logged, tested against real examples and held under a spend limit.
04Staff Augmentation
You interview the engineers who will do the work. They join your standups, your repository and your reviews, and own features from design to deploy.
05SEO & AI Search
We make your site crawlable, indexed and quotable for Google and the AI answer engines your buyers research in, then measure how often you are mentioned and cited.
Case studies
Six projects we can publish. We work with companies in any industry: access rules, scale, integrations and messy documents look much the same everywhere. Client names are withheld.
SaaS
A platform re-architected to hold 10,000+ concurrent users
Logistics
A live operations dashboard handling 500K+ shipment events a day
Financial services
A document assistant that saves each analyst 5+ hours a day
Insurance
Claim documents processed in 2 minutes instead of 30
Healthcare
3,500+ search landing pages built from structured data
Your industry
Not listed? It does not need to be. Tell us what you are building.
We built an agent workflow that collects, checks and provisions each new customer's set-up for a B2B SaaS platform. Staff time per customer fell from 8-10 hours to under 2.
We built a real-time operations dashboard for a logistics company that flags shipments at risk of missing their SLA. Prediction accuracy rose from 60% to 84%.
We built an internal assistant that summarises, classifies and routes incoming financial documents for analysts to check. Time per document fell from 15 minutes to under 2.
In their words
Each quote links to the case study it comes from. Names are withheld; roles are real.
They didn't just fix the immediate problems. They gave us a foundation to scale confidently for the next several years of growth.
CTO at a B2B SaaS company
10x improvement in concurrent capacityWe process claims faster, more accurately, and our team can focus on helping customers instead of data entry.
VP of Claims Operations at an insurance company
95% of claim documents processed without manual entryThis platform gave us visibility we never had before. We're now proactive instead of reactive, and our customers notice the difference.
Director of Operations at a logistics company
84% SLA prediction accuracy, up from 60%How we work
Buyers worry about the same things with any outside team: who owns the code, who does the work, and what you get for the money. These are our answers.
Work happens in your GitHub and your cloud from the first commit. If we stopped tomorrow, you would already hold everything.
The first call is with an engineer, not a sales rep, and you meet the engineers who will do the work before anything is signed.
Each engagement lists what you will receive and what it costs, in writing, before any build work begins.
Every build milestone ends with something running that you can use. A status report is not a deliverable.
If a rebuild is cheaper than a fix, or an off-the-shelf tool does the job, that is what we will tell you.
Insights
15 Nov 2026 · 4 min read
We graded the common AEO and GEO advice by the evidence behind it. A short list survives.
Read article15 Nov 2026 · 4 min read
The research points at ordinary causes, and model choice is not one of them.
Read article15 Nov 2026 · 3 min read
The answers tell you more than the portfolio does. What to ask, and what a good answer sounds like.
Read articleFAQs
Not answered here? Email service@spheretech.dev and an engineer will reply.
Sphere Techlabs is a software and AI engineering studio. We take prototypes built with AI tools to production, build custom web applications and APIs, add AI features such as assistants and document automation, embed senior engineers in client teams, and run technical SEO and AI search optimisation.
Companies of any size, in any industry, that need software or AI built properly. That includes founders with an AI-built prototype and their first paying customers, product and engineering leaders at growing companies, and operations teams with manual work to automate. Our published case studies come from SaaS, logistics, financial services, insurance and healthcare, but none of the work is specific to those sectors.
An engagement with Sphere Techlabs starts with a message, then a call with an engineer. For existing code we start with a fixed-fee review. For new work we start with a short discovery. Either way you receive a written scope with named deliverables and a price before any build work begins.
Clients of Sphere Techlabs own their code from the first commit. The work lives in your repository and your cloud accounts, so there is nothing to hand over at the end and nothing that locks you in to us.
The cost of a project depends on scope, so we do not publish a single number. Reviews are a fixed fee, and builds are quoted as a fixed scope once we have seen the code or the brief. You get the price in writing before work starts.
The six projects in our case studies took two to five months each. You get a timeline in writing together with the scope, before work starts.
Yes. Our engineers use AI coding tools for exploration, refactoring, tests and documentation. Every change is still read and reviewed by an engineer before it is merged, and the checks we run on client prototypes (data access, secrets, environments) apply to our own work too.
Yes. Our engineers can join your standups, repository and code reviews through staff augmentation, or take a defined piece of work and hand it back with documentation.
Contact
We will tell you what is likely to break, what to fix first, and what it would take to get to production. If we are not the right team, we will say so.
What happens next
Prefer email? service@spheretech.dev