Stop renting heroes.
Own the knowing.
Every services company forgets what its people know, the day they leave. Supernova is the Company Brain: your data in, an expert ontology on top, and seventeen working agents, a sales squad and a delivery squad, turning model power into outcomes you can actually promise. Priced like bread, not oven-hours.
Antino, the AI-native services & product company. Built on the Company Brain.
The project's memory just resigned.
Meera, a delivery head, is still at her desk when the email lands. One line in the subject: “Resignation.” It's from the one developer who knows how the payments module actually works.
Not the code, the code is in the repo. The understanding, why it's built this way, what breaks if you touch it, what the client really meant, walks out in a person's head, thirty days from now.
Every services company is one resignation away from forgetting how its own software works.
Hi Meera, it's been a great four years. My last day will be in thirty days. Happy to document what I can before I go.
The payments module's why, its history, its edge cases, leaves with the sender.
Everything was working. Which is exactly when the dangerous question got asked: why won't it scale beyond a handful of people?
The 10× lottery
Same tools, same week, one dev gained 10%, another 300%. AI adoption behaved like a lottery ticket, not infrastructure.
You can't promise outcomes on a 30× spread between your own people.
Velocity without visibility
Output exploded, and nobody knew what was actually being built. Things looked great. Solid? Nobody could say.
Speed you can't inspect is risk, not progress.
Heroes don't scale
Quality and quantity depended on who showed up. Every resignation a small fire, every vacation a slowdown, every hire a gamble.
It wasn't a system, it was a collection of heroes.
The handover snaps
No global knowledge base. Move a project from one team to another and it broke, the context left with the person.
The root cause hiding under the other three.
Four fault lines, one root cause: the knowing lived in people, not in the company.
Model power is scattered sticks. The harness is the rope.
On its own an LLM is loose firewood, hallucination, variance, luck. Tie that raw power to org-owned context and gathered knowledge, and the same sticks become something a business can pick up and carry.
Raw power, nothing tying it down.
Bound to the org, firewood you can carry.
Because delivery is a chain of translations between unit artifacts. Every workflow step is one linguistic hop, and “language” can be English, an entity model, a page spec, or a Playwright test.
Use case
“A returning buyer reorders in one tap.”
Ontology
Order, Cart, Reorder: entities, state machines, relations.
Page + mock
Order history page, one-tap reorder CTA, rendered as a mock.
E2E test
One spec per use case; the test asserts the cart matches.
One pipeline, each hop is a translation you can inspect, ground, and repeat
One blueprint that turns raw model power into functions that run.
Not a chatbot bolted to your data, an org-owned brain. Four pieces on one backbone, resolving into outcomes you can measure and promise.
Any data in
Proposals, signed agreements, email threads, recorded calls, the CRM, the whole company, ingested. Nothing about how you work is out of scope.
Expert ontology
An expert-defined structure on that data, the concepts, entities and relationships of your business, made explicit.
Atomic theory
Every job breaks into atoms, the same structure across every role. An SDR finding prospects, a dev turning a use case into tests: same theory, applied.
Harnessed agents
Agents are workers with tools and a ledger: they read the real corpus, save structured artifacts with verbatim provenance quotes, and every run is a session you can open.
Measured outcomes. Model power, turned into functions that run, and results you can promise.
Any data in. Expert ontology on top. Atomic theory applied. Outcomes you can promise.
One deal. Nine ratchet stages. A named human at every gate.
A closed deal births the project, corpus attached: the proposal, the signed agreement, the real threads. From there the journey ratchets, each stage an agent-drafted artifact behind a human gate, and every approved artifact becomes context for the next. The agent proposes; the artifact is the review surface; a person approves.
Research
Agents read the project's own corpus: the proposal, the signed agreement, the real email threads. Every claim carries a verbatim quote from its source.
Use cases
The product's working spec as a tree: flows, business rules, blocking questions, and a refine loop that regenerates stories and tests together.
Ontology
The data model derived from the approved use cases: entities, fields, state machines, relationships, rendered as an ERD.
Roles
What the product grants, as plain-language permissions naming ontology entities, with personas mapped onto each role.
Design
The client product's design language, codified and previewed on a full component kit before anything wears it.
Pages
The surface map: every page owned by a role, with purpose, primary actions, entities shown, and the stories it serves.
Mocks
One rendered mock per page, wearing the approved tokens, reviewed in real device widths.
E2E tests
One Playwright spec per use case; the test ids inside are the implementation contract, shipped into the scaffold.
Planning
A Jira-shaped plan: milestones, releases with exit-criteria gates, sprints over the backlog, and the change footprint checked before anything mutates.
Stories move through five states on a live board, bugs are first-class issues, QA verify gates sit before done, and release exit criteria only turn green when earned. Eight human roles run it, Designer, Dev and QA included. Every approval is audited, and every agent run is a session you can open: model calls, tool calls, cost.
Eight years of gold. A Rosetta Stone no one else has.
Every project quietly left a pair behind: what was asked sitting right next to what shipped. Not notes about the work. The work itself, both halves, still attached.
- Specs, scopes & user stories
- Use cases & flows
- What the client actually meant
- Production code & tests
- Delivered, running systems
- Symbols, files, calls, deps
Distilled into the Brain
Every pair poured through, distilled
Workers that actually work
Seventeen of them, grounded in the pairs: they read the real corpus, quote it back verbatim, and go down to the most atomic level of the work, what we came to call infinite control.
240 projects · 8 years · every pair intact
This is the moat nobody else has.
Not a demo. A shipped system, working the deal desk and the delivery floor. Open any run and check.
Sales came first: seven agents on the deal desk. Then the honest flinch, would the same harness carry delivery? Ten more agents later, it runs the whole journey, deal to release.
The resignation email arrives again. Meera reads it, wishes the developer well, and feels nothing break. The payments module's why, its history, its patterns already live in the Company Brain. Tomorrow, someone picks it up mid-sentence.
The company remembers now. People add to it. Nobody leaves with it.
Renting heroes by the oven-hour.
You pay for time on a clock. When the person leaves, the knowing leaves with them, and the next project starts from zero.
Priced like bread, not oven-hours.
You buy the outcome, and the knowing stays. Every project feeds the Company Brain, so the next person starts mid-sentence.
Stop renting heroes. Own the knowing.
Antino, the AI-native services & product company. Built on the Company Brain.
Questions, answered.
One org-owned blueprint, a named human at every gate, and a tamper-evident trail behind each decision. The details, without the hand-waving.
Is Supernova just for software teams?
No. Every function decomposes into atomic tasks, and the same harness applies to any role. Sales was the first squad, seven agents on the deal desk; delivery was the second, ten agents running the journey from deal to release. The blueprint is the constant, the domain is not.
What actually is the Company Brain?
Your data in, an expert-defined ontology on top, atomic decomposition of the work, and harnessed agents on top of that: workers with tools and a ledger, grounded in your own corpus. It is one org-owned blueprint that turns raw model power into repeatable outcomes.
How does a human stay in control?
Every stage is a maker-checker gate: the agent proposes, the structured artifact is the review surface, and a named human approves. Nothing ships unapproved, and every run is a session you can open: model calls, tool calls, cost.
Is our data used to train shared models?
No. The ontology and the agents are org-owned. Your pairs are your moat, not anyone else's, and never folded into a shared model.
How does this hold up to an audit?
Every approval writes a detailed audit event, and every agent run keeps its full session: the model calls, the tool calls, the cost, the artifact it produced. The record is built as the work happens, not reconstructed after.
How long until first value?
The first milestone is an approved use-case tree: a reviewed working spec where every requirement quotes the source that earned it. Value shows up as an approved artifact, not a promise.