Sollyn Labs is building the platform company of the AI age: the new-age AI products that companies, customers, governments and startups will run on — designed, engineered and delivered by one lab.
Most AI studios build for one country. We build for the world: the United States, Europe, the Middle East and Asia.
Every era gets one company that delivers its defining technology to everyone. Software had its. Our mission is to be that company for AI — putting new-age AI products into the hands of companies, customers, governments and startups, owned by the people who run on them, in every country on Earth.
Microsoft did not win the software era by building one good program. It won by becoming the company that delivered software itself — to every company, every government, every desk and every home on the planet. That seat is empty in the AI era. We are building to fill it.
One lab that designs, builds and delivers the AI products the world runs on — for companies, for customers, for governments, for startups. For everyone, everywhere.
The pattern of every era. Steam had its engine builders. Electricity had its utilities. Software had one company whose products became the default layer the world worked on — not the loudest, not the first, but the one that showed up for every kind of customer, in every country, at a standard nobody else could match consistently.
AI is at that same moment now. The models exist. What does not exist yet is the company that turns them into finished products the whole world can rely on — a ministry in the Gulf, a fund in New York, a factory in Europe, a two-person startup in Asia, all running on products from one lab, built to one standard.
That is the company Sollyn Labs is becoming. Not an agency that rents out hours. Not a wrapper around someone else’s model. A product company and research lab in one: we train and fine-tune the models, design the products people actually use, engineer them to production grade, and deploy them where they belong — inside our clients’ own walls, under their own control.
The bar is set accordingly: if a product carries the Sollyn name, it should be the product its category is measured against. Anything less is a prototype, and prototypes don’t change eras.
Agencies finish engagements. Platform companies ship products that compound. Every build at Sollyn becomes a product, a pattern or a platform capability the next client starts from — the portfolio grows, and so does the distance between us and everyone else.
A citizen checking a scheme status, a partner reviewing a contract, a founder shipping her first AI feature, a ministry serving fifty million people — the same lab serves all of them. Greatness in this era means range without dilution.
We were global before it was comfortable: four regions, every major data regime, models in the languages our clients’ people actually speak. Distribution is not a department here — it is the strategy.
One senior team takes your product from first sketch to a system running in production. No hand-offs, no bench of juniors, no 200-slide decks.
End-to-end product design and engineering: the model, the application around it, and the interface your people or customers use every day.
AI that runs inside your perimeter — your data centre, your sovereign cloud, your offices. Built for regulated sectors and national programmes.
We distil and fine-tune open-weight models for one domain, and train small–medium language models from scratch when ownership matters.
Agents that do real work across your systems — reading, drafting, checking and filing — with a human approving anything that changes a record.
Ask questions of your files, contracts, records and databases in plain language — every answer traced back to the source it came from.
Camera and sensor intelligence that runs on small hardware at the site — factories, infrastructure, field operations — no cloud round-trip.
We map where AI saves you the most time or money, what data it needs, and what to build first — including where a ready-made tool is the smarter buy.
Each product starts as a real problem we hear from buyers, then becomes a pilot you can test on your own data. Customise it, white-label it, or take the pattern and build your own version with us.
Problem: Teams drown in PDFs, contracts and records they can’t search. Docs answers questions in plain language — every answer cited to its source page.
For: Government departments · Law firms · Funds
Plan a Docs pilot →Problem: Citizens can’t find the right form, scheme or status. Desk is a multilingual assistant trained only on official documents, running on sovereign infrastructure.
For: Ministries · Municipalities · Public programmes
Discuss Desk →Problem: Back-office work is copy, check, file, repeat. Agents draft, check and file across your systems — a human approves anything that changes a record.
For: Operations · Finance · Compliance teams
Plan an agent build →Problem: Giant models are expensive and generic. Models fine-tunes a small open model on your domain until it beats a giant at your one job — and you own the weights.
For: Enterprises · Product companies · Gov AI missions
How ownership works →Problem: Inspection depends on tired eyes and cloud uploads. Vision runs on a small box beside the camera — factories, sites and infrastructure, no cloud round-trip.
For: Manufacturing · Infrastructure · Field ops
Discuss Vision →Problem: Knowledge is split across drives, mail and databases. Search unifies it privately with text-to-SQL and document search in one interface your team trusts.
For: Enterprises · Research teams · B2C platforms
Join the pilot list →Have a problem statement of your own? Send it — if it fits, it becomes the next pilot in this portfolio.
Sollyn is a new studio, so we show patterns honestly — the exact builds we deliver, what they prove, and how to start yours — rather than invented client logos.
Department files become searchable, cited answers for officers and citizens. Sovereign deployment, local languages.
Start this projectInternal documents, tickets and data in one private copilot. No data to outside providers, full audit log.
Start this projectFine-tuned model flags clauses, deadlines and risks in contracts and leases — lawyers approve every change.
Scope itDefect detection on Jetson / RTX edge hardware on the line. Works offline, improves with your labelled images.
Scope itZero-to-launch product: assistant, personalisation and support agent designed as one experience, cost-optimised inference.
Start this projectOne workflow automated end-to-end — intake, drafting, checking, filing — with approvals and logs at every step.
Start this projectGovernments, enterprises, consumer companies and startups — different buyers, same problem: the data that makes AI useful is the data you can least afford to hand to someone else.
Sovereign deployments for ministries, agencies and public programmes — models and data stay inside national infrastructure, in the languages your citizens speak.
For banks, law firms, funds, manufacturers and operators whose records are the business. Private deployments, full ownership, no per-question rent to a model provider.
For founders and product companies shipping to users at scale — AI features designed as products people love, engineered to keep unit economics sane as you grow.
For founders building AI-native companies: the model work, product engineering and deployment discipline that usually takes a funded team a year — delivered by a lab that builds this every week.
Distribution decides who wins in this business. We take clients where the ambition — and the budgets — are: four regions, one studio, deployments that respect each region’s data rules.
Enterprise and venture-backed product builds, deployed in your US cloud or data centre.
GDPR-first builds: data residency inside the EU, models hosted on European infrastructure.
Government and enterprise programmes across the Gulf, with in-country hosting as standard.
Our home engineering base, building for clients across Asia-Pacific and beyond.
The builds we run again and again — proven patterns, adapted to your data and your perimeter:
The lab work that makes client systems better — small models, honest evaluation, deployment on modest hardware:
Most AI tools send your files to someone else’s servers to be processed. Ours don’t. The model sits in your server room, your private cloud, your office — and your data never crosses the wall.
On-prem servers, your sovereign or private cloud, a Mac Studio in the office, or a small box next to a camera. Your call.
No file, record or question goes to an outside AI provider. If a cloud model is ever used, it’s your account, your keys, one task only.
The models trained on your data, the code and the product around them are yours. We never train anyone else’s model on your data.
Once it’s running, you pay for machines — not a per-token fee another company can raise whenever it likes.
You cannot claim an era with average work. These six standards govern everything that leaves this lab — every product, every pilot, every line of copy on this page.
Demos are easy; the world is full of them. A Sollyn build is finished when it runs in production, on real data, under real load, in the client’s own environment — not when it looks good in a presentation.
Every claim a system makes is evaluated on the client’s data before it ships. Answers cite their sources, numbers come from fixed maths, and the eval harness is one the client can audit themselves.
Not by policy, not by promise — by design. Data that never leaves the building cannot leak, cannot be subpoenaed from a vendor, and cannot train a competitor’s model.
Government software does not have to feel like government software. Every interface is designed to the standard of the best consumer products — because citizens and employees deserve the same craft.
Models, code, data, documentation. If we disappeared tomorrow, everything we built for you keeps running. Dependence on us is a failure of our job, not a business model.
The lab runs on a continuous improvement loop: ship, measure against real usage and real search behaviour, review, improve, ship again. Standing still is the one result we don’t accept.
That is what “the Microsoft of AI” actually demands — not size on day one, but an unbroken standard, applied to every client, in every region, on every build, until the name itself becomes the benchmark.
Research labs build differently from consultancies: the people who study the models are the people who ship your product.
We study how your teams work, where your data lives and what must stay inside. You get a plan: what to build first, what it costs to run, and what to simply buy instead.
One task, your real data, inside your perimeter. Your team judges the system on your own documents before you commit to anything bigger.
Model fine-tuning, product design and engineering in one loop — working versions shown weekly, evaluated against tests you can audit.
Live on your infrastructure, documented and handed over. Models, code and data are yours. We stay on for support only if you want us.
Pick what sounds like you. You’ll get the pilot path we’d recommend — the exact scope and price come in a written proposal after a discovery call.
We start with one high-value task, prove it on your data, then expand. Proposal with fixed scope and price within 48 hours of a discovery call.
Start with this buildFour steps, in order. You start with a conversation, not a commitment — and nothing scales until it has first proved itself in a pilot built to be used.
A conversation about the problem, the users, and what success looks like. That shared picture — the job to be done, the people it serves, the outcome that counts — is what everything after this step is built on.
Sollyn turns the problem into a written problem statement — with scope, data, and success criteria defined in writing, so you know exactly what will be built and how it will be judged before any build begins.
A focused pilot built from one of the six offerings. Built to be used, not demoed — your team works with it on the real problem, and the results decide what happens next.
What works in the pilot is hardened, integrated, and rolled out — connected to your systems and put into wider use, on the foundation the pilot already proved.
Step one is a conversation. Tell us the problem — we will take it from there.
Book a discovery callShort, plain-language guides written for search engines and AI answer engines alike — the questions government and enterprise buyers ask before they ever book a call.
If your data can’t go to a public chatbot, you need AI on your own infrastructure. What it costs, what hardware it needs, and what to pilot first.
Read the short answer in FAQ →For one narrow task, often yes. How distillation and fine-tuning work, how we prove it with evals on your data, and when a giant model is still the right call.
See how we build models →Data residency, language coverage, audit logs, human approvals and ownership — the five things to demand in any government AI procurement.
See our platform principles →One task, your real data, inside your perimeter, judged by your team. The pilot structure we use — and the red flags to watch for in any vendor’s pilot.
See our process →What to look for when hiring a studio to build a custom AI product — six evaluation criteria, each mapped to one of the six principles of the Sollyn Standard.
Read the guide below →Five proofs a pilot should deliver before it earns the Scale step — each mapped to the Sollyn Standard, with the question to ask any studio before you commit to more.
Read the guide below →Hiring a studio to build a custom AI product is a different decision from buying a ready-made tool. You are choosing who will design and build a product that real people will rely on — whether those people are a company’s team or customers, a government department, a startup, or consumers.
A polished demo can hide the questions that matter most: will it run in production, can its claims be proven, where does the data live, will people want to use it, who owns it, and will it keep getting better? Sollyn Labs builds custom AI products for companies, governments, startups and consumers, and it judges its own work by six principles — the Sollyn Standard. Use those same six principles as your evaluation criteria for any studio you consider, including us.
Demos are easy; the world is full of them. A build is finished when it runs in production, on real data, under real load, in the client’s own environment — not when it looks good in a presentation.
Ask the studio: When is a build finished — at the demo, or when it is running in production, on our real data, under real load, in our own environment?
Every claim a system makes should be evaluated on the client’s data before it ships. Answers cite their sources, numbers come from fixed maths, and the eval harness is one the client can audit themselves.
Ask the studio: How will you prove this works on our data before it ships — and can we audit the evaluation ourselves?
Privacy should be by design, not by policy or promise. Data that never leaves the building cannot leak, cannot be subpoenaed from a vendor, and cannot train a competitor’s model.
Ask the studio: Is privacy built into the architecture itself — so our data never leaves our building — or does it rest only on a policy or a promise?
Government software does not have to feel like government software. Every interface should be designed to the standard of the best consumer products — because citizens and employees deserve the same craft.
Ask the studio: Who designs the interface our people or citizens will use every day — and to what standard?
Models, code, data, documentation. If the studio disappeared tomorrow, everything it built for you should keep running. Dependence on the studio is a failure of its job, not a business model.
Ask the studio: Do we own the models, code, data and documentation completely — and would everything keep running if you disappeared tomorrow?
Look for a studio that runs on a continuous improvement loop: ship, measure against real usage and real search behaviour, review, improve, ship again. Standing still is the one result that should not be accepted.
Ask the studio: After launch, how do you measure the product against real usage — and how does it improve, cycle after cycle?
At Sollyn, working together begins with a Discovery call — a conversation about the problem, the users and what success looks like. Then comes Scoping, a written problem statement with scope, data and success criteria; a Pilot build from one of the six offerings — Docs, Desk, Agents, Models, Vision or Search — built to be used, not demoed; and, only once the pilot has proved itself, Scale, where what works is hardened, integrated and rolled out. Judge us by the six criteria above, starting with step one.
Pilots are where most AI engagements fail — not because the technology does not work, but because nobody agreed beforehand what “working” means. A pilot can look impressive, answer a few well-chosen questions and still leave the only decision that matters unanswered: should this be scaled?
At Sollyn, that agreement comes first. Scoping turns the problem into a written problem statement with scope, data and success criteria, and the Pilot build that follows is built to be used, not demoed. Before a pilot earns the next step — Scale — it should deliver five proofs. Each one is drawn from the Sollyn Standard, the six principles Sollyn judges its own work by. Use them to judge any pilot, including ours.
The proof: it runs on real work, not a demo. Demos are easy; the world is full of them. A pilot proves this only when it runs on real data, under real load, in the client’s own environment — on the real problem it was built for, not on a presentation version of it.
Ask the studio: Will this pilot run on our real work, in our own environment — or only as a demo?
The proof: it moves a number the buyer already tracks. Success criteria are defined in writing during Scoping, before the build begins. A pilot should be measured against real usage and against a number you already follow — so improvement is visible, not a feeling. A studio should run on a continuous improvement loop: ship, measure, review, improve, ship again.
Ask the studio: Which number we already track will this pilot be measured against — and how will we know if it moved?
The proof: it works inside the buyer’s own systems and data, not beside them. Every claim a system makes should be evaluated on the client’s data before it ships. Answers cite their sources, numbers come from fixed maths, and the eval harness is one the client can audit themselves. A pilot that sits beside your systems, working on separate data, has not proved it can do the job inside them.
Ask the studio: Will you prove this inside our own systems and on our own data — and can we audit the evaluation ourselves?
The proof: the people it is built for actually use it. A pilot is built to be used, not demoed — so the test is whether the people it serves work with it on the real problem. Every interface should be designed to the standard of the best consumer products, because citizens and employees deserve the same craft. If the intended users avoid it, the pilot has not proved itself, however capable the model behind it is.
Ask the studio: Will the people this is built for use it in their real work during the pilot — and how will you know they want to keep using it?
The proof: the buyer’s data stays theirs and the build is theirs to keep. Privacy should be by design, not by policy or promise — data that never leaves the building cannot leak, cannot be subpoenaed from a vendor, and cannot train a competitor’s model. And ownership should be complete: models, code, data, documentation. If the studio disappeared tomorrow, everything it built for you should keep running. Dependence on the studio is a failure of its job, not a business model.
Ask the studio: Does our data stay inside our perimeter by architecture, and do we own the models, code, data and documentation completely — so everything keeps running without you?
Scale is step four of working with Sollyn — and it only follows a pilot that has delivered the five proofs above. First, a Discovery call: a conversation about the problem, the users and what success looks like. Then Scoping: a written problem statement with scope, data and success criteria. Then a Pilot build from one of the six offerings — Docs, Desk, Agents, Models, Vision or Search — built to be used, not demoed. At Scale, what works in the pilot is hardened, integrated and rolled out — connected to your systems and put into wider use, on the foundation the pilot already proved. If you have a problem you want a pilot to prove itself on, start where every engagement starts: book a discovery call — step one is just a conversation.
Distribution is part of the build, not an afterthought. This page already carries the foundations; the ongoing work is content, citations and listings.
Canonical URL at labs.sollyn.com, semantic headings, descriptive title & meta description, Open Graph / Twitter cards, fast static page, and clean internal links between products, projects and guides.
FAQ answers are written as complete, self-contained statements — the format featured snippets and voice / chat assistants lift verbatim. Each product states its problem in one sentence.
Organisation, WebSite and FAQPage JSON-LD on this page identifies who Sollyn Labs is, what it builds, who it serves and where — so generative engines can describe and cite the studio accurately.
For Kabir. The setup is decided: GitHub holds the code, Cloudflare Pages hosts it (chosen over Vercel), studio email forwards into the Gmail already connected to Muse, and only the labs subdomain is delegated — the sollyn.com root belongs to another company and is never touched. You can only reach Muse through chat, so nothing below needs a special portal: every step is done on the platform’s own site, and the result of each step is either an invite (sent to the operator) or one line pasted back into this chat.
Where: github.com → your account → New repository. Do: create a private repo named sollyn-labs. Then Settings → Collaborators → Add people and invite the operator’s GitHub username (ask for it in chat if you don’t have it yet). Give back: just confirm “repo created, invite sent” in chat — Muse pushes this site’s code there and every later update flows through it.
Where: dash.cloudflare.com → sign up free → Manage Account → Members → Invite. Do: invite the operator email with a role scoped to Pages and DNS for this account only. Why Cloudflare over Vercel: hosting, the labs DNS record and email routing all live in one dashboard, on the generous free tier.
Where: Cloudflare dashboard → Workers & Pages → Create → Pages → Connect to Git. Do: pick the sollyn-labs repo, framework preset None, build output = site root, and deploy. Every push to GitHub then auto-deploys — that is the steady write access. Give back: nothing; the invite in step 02 covers it.
labs only, root untouchedWhere: Cloudflare Pages project → Custom domains → Set up a domain → enter labs.sollyn.com. Cloudflare tells you the single record to add at your registrar: CNAME labs → <project>.pages.dev. Do: add exactly that one record where sollyn.com is registered (or hand DNS to Cloudflare and Muse adds it under the step-02 invite). The root domain, its site and its records stay exactly as they are — this step cannot affect them.
Receiving — where: Cloudflare dashboard → sollyn.com → Email → Email Routing → Enable, add the destination = the Gmail address already connected to Muse, then a routing rule: hello@sollyn.com (and a catch-all if you want) → that Gmail. Inbound studio mail then lands in the inbox Muse already reads. Sending — where: in that Gmail, Settings → See all settings → Accounts and Import → Send mail as → Add another email address → add hello@sollyn.com and verify via the forwarded confirmation mail. One DNS job: add SPF, DKIM and DMARC records for the domain (Cloudflare’s Email Routing wizard plus your sending setup generate the exact values — paste them into chat and Muse checks them line by line). Result: receive and send as hello@sollyn.com from the one connected Gmail.
Where: search.google.com/search-console and bing.com/webmasters. Do: add the property labs.sollyn.com, verify with the DNS TXT record each one shows you (one record each, added at the registrar or via the Cloudflare invite), then Settings → Users and permissions → Add user → the operator email, Full access. Add Cloudflare Web Analytics (one toggle in the Pages project) or a GA4 property with the operator as Analyst. This is the data the improvement loop runs on.
Where: LinkedIn (Company Page), Product Hunt, Clutch / G2-type directories, GitHub organisation if you want one. Do: create each account under a studio email you own, then add the operator as admin / maintainer — you stay super-admin everywhere. Give back: tell Muse in chat which accounts exist; nothing is posted or pitched from any of them without your per-item approval.
sollyn-labs + send collaborator invitelabs CNAME — root domain untouchedlabs. Approval gate stays yours: access lets the site be updated, measured and listed. It does not authorise spend, purchases, contracts, or sending outreach in your name — those still need your explicit go-ahead, one by one.Fastest path: steps 1–4 take about 20 minutes and put this page live at labs.sollyn.com with automatic deploys. Steps 5–7 can follow the same day. Do them in any order — just tell Muse in chat which step you’ve finished, and it picks up from there.
Got questions? We have got answers. If you do not find what you are looking for, feel free to reach out.
Sollyn Labs is an AI-native product studio and engineering lab with one ambition: to be the platform company of the AI age — the lab that delivers new-age AI products to companies, customers, governments and startups, the way the defining company of the software era once delivered software to everyone. We design and build custom AI products for government organisations, enterprises (B2B), consumer companies (B2C) and startups — and we build them to run privately, on infrastructure you control.
We do three things: we fine-tune open-weight models until they are excellent at one job; we build small–medium language models from scratch when a client needs one that is fully theirs; and we design and engineer the products your staff and customers use every day to work with those models.
Microsoft defined the software era not by writing one program, but by delivering software itself — to every company, government and household, in every country, at a standard the whole industry measured itself against. That position does not exist yet in AI. The models exist; the company that turns them into trusted, finished products for everyone does not.
That is the job Sollyn Labs has taken on: one lab that trains the models, designs the products, engineers them to production grade and deploys them worldwide — for enterprises, consumers, governments and startups alike. It is a statement of ambition and standard, not of size: we are a young studio, and the way a young studio earns an era is by holding the highest bar in its field on every single build.
We are a product studio and research lab, not a staffing firm. The people who build your system are the same people who train and study the models inside it — and that matters, because private AI is hard to get right. Fitting a capable model onto one machine in a fund’s office, or a small computer beside a factory camera, takes engineers who understand models from the inside.
Large consultancies mostly supply people and process; months pass in planning before anything is built. We show a working first version within days, and full systems go live in weeks.
The world — that is the point of the studio. We take clients across the United States, Europe, the Middle East and Asia, and we deploy under each region’s rules: data residency in-country where required, GDPR-first builds in Europe, sovereign hosting for Gulf and government programmes.
Where a deployment must physically sit inside your country, we build it that way. Geography is a design requirement, not an afterthought.
Private AI is AI that runs on computers you control. Your files, records and questions never leave your organisation.
Most AI works the other way: when you use a tool like ChatGPT, your data travels over the internet to the company that runs it, gets processed on their computers, and the answer travels back. With private AI, the model sits in your own server room, your own private cloud account, your office, or a device in your hand.
It is like owning a printer instead of sending your files to a print shop. You get the same pages — and your files never leave the building.
No. In a private setup, the models run on your own computers and no outside AI company is involved. No file, record or question leaves your network.
If you ever want a cloud model for one specific task, we connect it through your own account and your own keys — and only for that task, with your written sign-off.
Those plans work well for everyday tasks, and many companies should use them. But your data still goes to the provider’s computers to be processed. A bank, a hospital, a law firm or a government department often cannot allow that for its most valuable data — customer records, patient notes, citizen files, legal documents.
Private AI lets you use AI on exactly that data. You also own what you build, and no provider can raise the price on you later.
For one specific job, often better. A giant model knows a little about everything. We take an open model — one anyone can download and run — and train it hard on a single task: reading your leases, answering questions about your sales data, drafting your department’s standard replies. A model trained that way can match or beat a giant model at that task, at a fraction of the running cost.
The biggest cloud models are still better at broad, write-about-anything work. So we pick the model that fits the job — and we prove it with evaluations on your data before you commit.
Often far less than you would expect — it depends on the job. A Mac Mini can run a full data system for one factory. A Mac Studio can run the AI for an entire investment fund’s office. Camera systems run on small edge computers like the NVIDIA Jetson Orin, or a server with an RTX-class GPU. Company-wide systems run on GPU servers or NVIDIA DGX machines.
We work out the right hardware with you before work starts. If you would rather not buy anything, you can run on machines dedicated only to you.
You do. The models we train on your data belong to you, and so do the code, the product and everything around them. We never use your data to train models for anyone else — that is written into how we work, not just the contract.
We build checks into every system, so it is hard for the AI to make something up without anyone noticing:
Yes — and we recommend it. A pilot means we pick one task, run it on your real data inside your perimeter, and show you the results. If it works, we move to the next task. Your team judges the system on your own documents before you commit to anything bigger.
Every engagement is scoped before work starts, so you know the full price — build and running costs — before you sign anything. To begin, book a 30-minute discovery call below or write to us at hello@sollyn.com. You will get a proposal within 48 hours.
Working with Sollyn starts in four steps:
To begin, book a discovery call — step one is just a conversation.
Whether you are a ministry, a multinational, a consumer brand or a two-person startup — tell us what you want to build. A 30-minute call with an engineer, not a salesperson, and a written proposal within 48 hours.
labs.sollyn.com
Governments · Enterprises · Startups · Consumers
US · Europe · Middle East · Asia
Proposal within 48 hours