The product

Kodzonia from the inside.

None of this is roadmap: it's the product running.

Kodi
Permissions for this conversation
Always askEvery destructive action needs your approval
Accept editsEdits files on its own; asks for the rest
AutomaticDecides by the declared impact level
AutonomousRuns without stopping for approval — everything still recorded
MODEL
Kodi Auto
Modelo máximo (1M de contexto)Modelo forte (256k de contexto)Modelo equilibrado (256k)Modelo leve (128k)Seleção automática — FMR
Power
StandardExtreme — max effort + workflows
Subagents
CONTEXT WINDOW
Last send90.2k / 256k
Compacts at~223k (87%)
ModelKodi Auto

Actual prompt tokens from your last send vs the model window; this turn enters the count on the next send.

You type

Build the product landing page from the text in the README, and serve it locally so I can look.

  1. Read the README and the brand guide 2 files
  2. Wrote the pages and the styles app/page.tsx +184
  3. Installed what was missing and started the server localhost:3000
  4. Opened it in the browser for you to check done in 2m 18s
Ask the agent for something…
SubagentsBrowserMCP
Subagentsthis conversation

3 runs · 1 ok · 0 failed · custo registrado

Filter by name or status

Running · 01232Stop all

Explorer-ModelosbgrunningSearching in the code3m 12s
Explorer-SandboxbgrunningListing files5m 34s
Explorer-CustobgrunningReading files5m 34s

Finished · 01Clear

Explorer-Modelosbgcompletedcusto registrado4m 51s
localhost:3000
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What it does

Every capability, with what changes for you.

It executes, it doesn't suggest

Reads and edits files. Runs commands in the terminal. Commits to git. Queries your database. Drives web pages and operates your cloud console when you authorize it. Prepares environments, installs what's missing, brings the service up and checks it running. The work that goes through your hands today — done, not described. Connects the MCP servers your company already runs and operates their tools like any other.

You stop being the middleman who copies, pastes and executes

It actually understands code

Not text search. Semantic analysis: the agent sees symbols, types and references — who calls what, who depends on whom — across the market's main languages, from legacy to modern.

Rename a symbol across the whole system without breaking what merely shares the name

The cost of a cheap model, the result of an expensive one

The agent reads with a light model. The moment a task is about to change something, it switches to the stronger one — before touching the file, with the switch recorded in the audit trail. Under the hood this is FMR (Fragmented Model Routing), our own technology, born of months of research into how models behave.

  • Want to pick it yourself? Go ahead: 18 models across three providers, every spec verified, never guessed.
  • On automatic, it only chooses among the models governance has approved.
  • Switching models does not change the experience: the tools, the memory, the specialists and the audit trail are Kodzonia's, not the model's.

A cheap model's cost with an expensive model's result — without you playing router

It delegates to specialists

Five specialists come out of the box — Explorer, Planner, Reviewer, Tester, Implementer. The rest is written for your project: the Specialist Builder studies everything that defines it — PRs, commits, the team's working patterns, architecture, frameworks, pipeline, cloud provider — and writes the specialists that codebase calls for. One for your UI, one for your pipeline, one for your docs. Each with its own name, scope and permissions — a team that works like it was already inside.

Not a generic assistant with settings — a team written for your code

Your process, executed the same every time

Workflows orchestrate the subagents in whatever methodology the team chooses: Superpowers — an established working framework — ships native, the framework you already use plugs in the same way — and more are coming. Whatever the process, the guarantee holds: it runs the same every time, and when information is missing the subagent asks instead of inventing.

PR review and migration become procedure — in your method, not ours

You know what each delivery cost

Model, tokens, cache, duration, error and the tree of what called what — per call, per day and per project. Exportable as CSV.

You can set the cost of a task beside the hours it would have taken, and decide on a number

Multi-project with real isolation

Several projects open at once, each with its own runtime, scope, memory and cancellation — isolated by architecture, not by convention. One project's agent cannot see another's code, secrets or conversations — and the provider connection each project may use is authorized on the account, not stored on your disk.

One client's code never leaks into another's context — isolation you can put in a contract

A team with roles, not a shared password

You invite by email and decide who governs and who works. Whoever governs decides the providers, which models are available, at what capability tier each one runs and which ones automatic routing may pick from, the limits and the permissions — sees each person’s cost and revokes access on the spot; whoever works opens the project and delivers. People joining and leaving is a decision, not a hunt for keys scattered across machines.

Someone leaves on Friday and takes no credential with them

The project is yours, end to end

Conversations, decisions, memories and audit belong to your project, and the code was never anywhere but where you work.

The project's data is yours, and the code never left your machine

Adaptive supervision

Delegate the heavy lifting. Without babysitting your agents.

When Kodi dispatches subagents to work in parallel, each one has an open line to whoever is orchestrating: it asks when unsure, declares what's blocking it, requests what it's missing. And the one who decides is still you.

Delegation shouldn't feel like a bet.

Handing work to a background agent usually means dispatching and hoping:

  • It stalls mid-task and nobody notices until the end.
  • It loops and burns budget.
  • It waits on an approval nobody saw — and the approval expires on its own.
  • Context fills up and the work surfaces as a panicked handoff.

Supervision exists to end that black box.

An open line, both ways

The subagent doesn’t vanish between the request and the result. It asks — always declaring the assumption it will follow if no one answers —, flags blockers, requests more tools or more budget before hitting its limits, and delivers partial results you can already use.

Waking up that respects your attention

Whenever a subagent is waiting — on a question, an approval, a blocker — Kodi is notified immediately. That is a fixed rule, not a heuristic. Everything else arrives in a grouped digest of just what changed.

Waiting that doesn't burn tokens

Orchestration waits for a task to finish inside its own turn, for up to 30 minutes, without a single model call. The cost of supervision doesn’t grow with the size of the fleet.

Stalls and loops, detected mechanically

No model guesswork: objective signals — time without real progress (a long build isn't mistaken for a stall; a hung shell is), the same call repeating in a circle, three consecutive tool errors. And the alert fires once — it doesn't degrade into spam.

Progress that survives

Server-persisted checkpoints, self-compaction when context reaches 80% of the window, resuming exactly where it left off, or forking from the same point. An app crash doesn't erase finished work.

Mid-flight intervention

Need to grant one more tool? Extend the budget? Raise or lower the model, within the same family? Stop it and ask for whatever is already done? All of it on a live task — no kill-and-restart.

grouped event digest
10sgrouped event digest
of waiting with zero model cost
30 minof waiting with zero model cost
of context before self-compaction
80%of context before self-compaction
approvals granted by agents on your behalf
0approvals granted by agents on your behalf

Autonomy on a short leash.

Big delegation requires big containment, and in Kodzonia it's structural:

  • Destructive action is always yours. There is no mechanism for an agent to approve on your behalf — not even the orchestrator does. The approval card reaches a human.
  • A granted tool doesn't skip the gate. Every grant still goes through the normal per-call approval, and the orchestrator can only grant tools it can use itself.
  • Budget has a ceiling. Extending a task's runway never exceeds the project's safety limit.
  • Supervision never kills the turn. If it fails, the system falls back to the previous behavior — your conversation doesn't die because of it.

What this feature doesn't do

It doesn't pause and resume running subagents. It doesn't swap models across providers. And it never decides a dangerous action for you.

Project memory

The project doesn't start over.

Kodzonia keeps what your project learned: decisions, conventions, pitfalls, the why behind each choice. On the next task, and for the next person who joins, the agent already arrives knowing.

Memory with structure, not a notes file

Every memory has a type — decision, convention, technical debt, preference, pitfall, glossary —, an origin (you or the agent), a date, tags and the area of the code it applies to, such as src/auth/**. When the agent works in that area, what was decided there enters its context. Memories link to each other: a new decision can retire the old one, and the trail stays.

Two layers

Project memory

One for the whole team, live. The rule someone taught just now already holds on everyone's machine, with no exporting and no syncing.

Personal memory

Your preferences and your way of working, yours alone. Colleagues neither read nor change them. What's yours doesn't become the team's rule, and what's the team's doesn't depend on you.

Every change becomes a version

Each edit creates a version. You see what changed, who changed it — you or the agent — and revert to any point.

Search that understands meaning

Search works even when you don't remember the exact term. The semantic index is computed on your machine, and memory has a context budget of its own, so it never competes with the code the agent needs to read.

  • The rule explained once never needs explaining again, not even when the person who knew it has left.
  • Switching models doesn't lose the project's memory.
  • Personal memory is yours, and the project's belongs to the team.
Where it earns its place

The work nobody wants to pick up.

The tasks that stall a sprint in any team: a lot to read, a lot at stake, and few people who know the ground.

Untested legacy

Touching the code nobody wants to touch

An agent reads the whole module before proposing anything, drafts the plan, edits, runs the suite and fixes what broke — with the full diff for you to approve.

Migration

Migrating what is spread across many files

Repetitive work becomes a script: who does what, and in what order, stops depending on who ran it. The same process behaves the same on file 3 and on file 300.

PR review

Reviewing with the eyes of someone who did not write it

One specialist reviews the diff and another tries to knock down each finding. The author is not the judge — and every point comes with file and line.

New joiners

Giving context to whoever just arrived

The decision made six months ago is still available in the project. Whoever joins finds the why, not just the code — without hunting for someone who remembers.

Integrations

Understanding the integration only one person understands

Search by meaning across the whole repository, with real code analysis: who calls what, who depends on whom, what breaks if you change it.

Cost

Knowing what AI is costing, per task

Every call recorded with consumption and duration, and limits you set per task and per agent. Spend becomes an architecture decision, not an invoice surprise.

What makes it hold

Why Kodzonia stays when the pilot ends.

Four things built for a real project under real pressure — context that doesn’t get lost, process that repeats, scope that holds, and cost you can read.

  1. 01

    The project does not start over

    memory that persists

    What was decided still holds on the next task, and for the next person who joins. You do not re-explain the same business rule every time you open a conversation.

  2. 02

    The same request takes the same path

    process, not improvisation

    Repeating work — review, migration, the check before shipping — happens the same way every time, whoever asked and whenever. The outcome does not shift with the model's mood.

  3. 03

    The agent stays where you put it

    a scope you declare

    Authorized folders, actions that require your approval, and a brake on anything destructive. The limit applies to everything the agent sets off, not only to what you talk to it about.

  4. 04

    Cost becomes a decision, not a shock

    cost per delivery

    Every model call is recorded with what it consumed and how long it took. You can know the cost of a task, a day or a project without waiting for the end of the month — and set it against what that delivery was worth.

For the team

The whole team, with a defined role.

The project is a resource of your Zonialabs account. Whoever governs decides providers, which models the team uses, at what capability tier and which ones automatic routing picks from, permissions and limits — and sees each person’s cost, call by call; whoever works opens the project and finds conversations, decisions and history where they left off.

The project’s memory is one, live: the rule someone taught just now already holds on everyone’s machine — instantly, no exporting, no syncing. And every AI call is logged with model, tokens, cost and duration — per project and per member. Team spend is a number with a name on it, not a surprise at month end. And the source code stays where it always was: on the machine of whoever works on it.

Proof

Where it has already run

Kodzonia wasn't tested on a sample project. It was used to build software that serves real people today — and to build itself. Contracts keep the names off this page. What the systems do, we can tell.

Payments

A payments platform in production

Built with the product, from the code to what runs today. Real money moves through it.

Applied AI

Three systems inside an enterprise ecosystem

A connector exposing the legacy system to agents, an applied-AI layer on top of it, and the operations platform — all in code with decades of history, exactly where AI assistants usually break.

The product itself

Kodzonia built Kodzonia

The product is built with itself, every day, on real code — each new feature is born being used by the person who wrote it. There is no better stress test than that.

01

The project belongs to you

Your source code stays on your machine: the agent and the tools work right there. What belongs to the project — conversations, memories, decisions, audit — lives on your account's server, under your own access control.

  • History, decisions and audit live in your account, under your own access control
  • No credential ever becomes text in your code or your conversation
02

22 tool categories

Files, terminal, git, semantic code search, browser, databases, cloud infrastructure and MCP servers. Every action has a stable name and a declared impact level.

  • The agent picks the right tool without you configuring anything
  • Deletion is reversible and every edit leaves a restore point
  • MCP servers come in as first-class tools — whatever your company already exposes, the agent already uses
03

A curated model catalog

Support for 18 models from the market's main providers, classed in four capability tiers, each with a verified fact sheet. And above the catalog, FMR — Fragmented Model Routing, proprietary technology born of months of research into LLM behavior: every task scored against hundreds of need parameters and routed to the right model, at the right moment.

  • If you do not pick one, it classifies the request and uses a model sized to the task
  • The moment the task starts changing things, it switches to the strongest model before touching anything
  • Every fact checked against the provider — what is not known stays blank instead of becoming a guess
  • The conversation is flagged once it crosses into the range where the provider charges extra for long context
04

Subagents that stay in scope

Five factory specialists — investigate, plan, review, test, implement. And the Specialist Builder, which studies what defines your project — PRs, commits, working patterns, architecture, frameworks, pipeline, cloud provider — and writes the specialists that codebase calls for, each with its own name, scope and permissions.

  • A large task becomes parallel work, not a long conversation
  • No subagent gets more permission than whoever called it
  • Runs in the background and returns the result when done
05

Your process, every time

Repeated work stops depending on who ran it — in whatever methodology the team chooses. Superpowers — an established working framework — ships native, the framework you already use plugs in the same way, and more are coming. Review, migration and pre-ship checks happen the same every time — and when information is missing, the agent asks instead of inventing.

  • The outcome doesn't change with the model's mood
  • New joiners inherit the procedure already written
  • The question reaches you, not a guess reaching the code
06

The project doesn't start over

Kodzonia keeps what your project learned: decisions, conventions, pitfalls, the why behind each choice. Every memory has a type, an origin (you or the agent), a date, tags and the area of the code it applies to — when the agent works in that area, what was decided there enters its context.

  • Project memory for the team and personal memory that is yours alone: colleagues neither read nor change yours
  • Every edit becomes a version, and you revert to any point
  • Taught on one machine, holding on all of them — instantly, with governance
  • Search works even when you don't remember the exact term
07

The same experience, on any model

That is the point of the tool: the behavior comes from engineering — tools, memory, specialists, audit — and the model is the engine, interchangeable. You choose a model, never a technical address. If a provider raises prices, changes policy or goes down, the engine swaps and the project continues exactly where it was.

  • Anthropic, OpenAI and Kimi side by side — and the policy is yours: the project only uses the ones you authorize
  • Switching rewrites nothing and loses no project memory
  • Credentials never exposed — and consumption shows up call by call, with who spent what
  • On the way: DeepSeek, Muse Spark 1.3 (Max) and GLM
08

Security that ships as code, not as a promise

The protections live in the engine itself, not in text asking the model to behave. Anything coming from outside is treated as untrusted by default.

  • Destructive actions need your approval, with the effect shown first
  • State-changing requests only reach authorized destinations
  • Secrets do not become conversation history
09

Real code intelligence

Semantic analysis, not text search. The agent understands the structure of the code — symbols, types, references — across the market's main languages. Works with yours? That answer fits in one conversation.

  • Rename a symbol without breaking the ones that merely look alike
  • Find who uses what before changing it
  • Tells you what it could not read instead of inventing
10

When the whole team comes in

Whoever joins the project inherits the history and decisions of whoever came before — and the rule discovered on a Tuesday does not go on vacation with the person. Every call has its cost recorded: team spend is a number, not a surprise.

  • History and decisions open to whoever the team authorizes
  • PR review and migration follow the same script, whoever runs them
  • Cost is recorded per call, so the spend per task is visible

RequirementsmacOS · native desktop app · Zonialabs account

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We show the product live, on your kind of code, and open the audit dashboard in front of you.