Built for teams running AI agents in production

Own the context.
Rent the intelligence.

Models are rented and replaceable. The context your team builds — your standards, your decisions, the history of why things are the way they are — is the asset that compounds. NeuroHive keeps that asset yours, and plugs any AI platform into it.

Book a demo → See how it works

Any model — hosted, open-source or your own · Swap and combine freely · Nothing locked to a vendor

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Projects

Active All companies
Northwind Retail App 7 sessions · 13 open issues · deployed 2h ago healthy
Calder & Roe Pentest 2 sessions · 4 open issues · staging healthy
Harbourline Freight Watch 1 session · idle 3d · production
ERP Access Review 0 sessions · 1 open issue · production healthy
What you're living with

AI made your team faster. It also made everything harder to find.

The problem

Output went up. Traceability went down.

More gets built than ever. Nobody can say where it lives, who approved it, or what the agent was told.

Context lives nowhere

Every agent session starts cold. The same background gets re-explained a dozen times, slightly differently each time — and drifts.

Secrets live everywhere

Credentials end up in chat logs, .env files and someone's notes app. Nobody can answer who has access to what.

You're renting more than you think

Your history, your instructions, your accumulated context — all sitting inside a vendor's product. Leaving means leaving it behind.

AI federation

The people with the ideas are rarely the people who can build them

That gap is where most good ideas die — not from lack of merit, but from lack of a credible path from "we should do this" to something running in production.

You have the idea

Operations, finance, legal, sales

You know exactly what's broken, because you live in it. What you don't have is a way to get it built that doesn't start with a six-week scoping exercise.

  • Describe the problem in your own language
  • Watch it become a real project with an owner and a date
  • See progress without learning a developer's tools
  • Approve the things that need a human decision
We bring the execution

An expert team, federated with AI

Senior engineers set the standards, review the work and own the outcome. Agents do the volume. Neither works unsupervised — and neither works alone.

  • Architecture and review by people who've shipped before
  • Agents handling breadth, under those standards
  • Every decision recorded, every change traceable
  • You keep the code, the data and the infrastructure

One shared language

Business people see status, decisions and outcomes. Engineers see repos, issues and deployments. Same project, same truth — each side reading the view that makes sense to them.

Judgment stays human

AI is extraordinary at volume and consistency, and genuinely bad at knowing what matters. NeuroHive is built around that asymmetry — machines do the work, people make the calls.

Capacity without headcount

Ideas that were never worth a hiring round become worth a week. The small improvements that get permanently deferred finally get built.

Project hub

Everything about a project, on one page

Stop stitching context together from six tools. Each project carries its own code, issues, notes, secrets, hosts, deployments and agent history — with tabs, not tab-hunting.

  • Todos as list, board or Gantt — same data, whichever way the team thinks.
  • Notes with Recently Deleted — nothing is lost to a stray click.
  • Search that spans every project — one shortcut, titles and content, straight to the row.
⬡ Northwind Retail App
OverviewTodosNotes SourceMemorySecretsAI
In progress SMS checkpoint double-fire#567 · due Friday
Todo Document limit on bulk upload#571 · unassigned
Done v2.4.0 release gate#563 · shipped
Agent memory

Your agents stop forgetting — whichever agent it is

NeuroHive reads and edits the instruction files your agents actually load, shows you exactly what every new session will be told, and what that costs you in context. Because it lives in your files rather than a vendor's account, the model you switch to next inherits all of it.

  • Edit directives in place — the file on disk stays the source of truth, written atomically, never overwritten behind your back.
  • Carry standing facts forward — promote what matters, retire what's stale.
  • See the real cost — memory is injected into every single session. NeuroHive shows the bill and flags duplication.
  • Portable by construction — plain files in your repositories, not rows in someone else's platform.
⬡ Memory · Directives
CLAUDE.md in sync 4.1 KB · 652 tokens
## Repo directives
— File an issue for every fix or feature
— Conventional commits, close with Closes #N
— Semver: PATCH for fixes, MINOR for features
Injected every session ≈0.3% of a 200k window
Agent console

Talk to your agents where the work lives

Find any session by what it was actually doing — not by hunting through browser tabs. Read the transcript, see how much context it has burned, and pick the conversation back up without leaving the project.

  • Every session, previewed — last message and context usage at a glance.
  • Live or recent, never lost — running sessions surface first; nothing drops into a void the moment it stops.
  • Send, stop, resume — full control in place, with the native tool one click away.
Session console
Checkout rebuild
15k msgs · 60%
QA / Review pass
1.4k msgs · 70%
Static analysis
217 msgs · 19%
you Does the SMS checkpoint still double-fire after v2.4.0?
claude Reproduced — notify-4417 fires twice in the same second. Points at the retry wrapper, not the cron.
tool Read · worker/retry.ts
Context history

The window expires. The record doesn't.

This is the part most teams discover the hard way — usually about six months in, when nobody can remember why a decision was made.

Every model has a context window, and every context window is a temporary thing. It fills up. It gets compacted — older turns summarized, then summarized again, then quietly dropped. Eventually the session ends, or the model changes, or the provider deprecates the version you were on. Whatever was held in that window is simply gone.

That's fine for the conversation. It is not fine for the conclusions.

A long working session produces two very different things. There's the transcript — thousands of messages of exploration, false starts, tool output and correction. And there's the handful of things you actually learned: this approach doesn't work and here's why, the retry wrapper was the real cause, this table is the source of truth, that dependency can't move until Q3.

The first is disposable. The second is the asset — and in most setups it dies with the window that held it. Someone re-derives it three weeks later, slightly differently, and nobody notices the drift.

Ephemeral · the window

What the model is holding right now

Rented space with a hard limit. Compacted as it fills, cleared when the session ends, and specific to whichever model was running.

  • The live conversation
  • Recent tool output
  • Whatever survived the last compaction
  • The model's working state
Durable · the record

What NeuroHive keeps regardless

Held as a durable record, addressable long after the session ends. Outlives the session, the model and the provider.

  • Every session, still addressable
  • Standing decisions and conclusions
  • An index of what lives where
  • The audit trail of what changed
1
History — sessions stay addressable after they end A finished session doesn't vanish into a closed tab. It stays listed against its project with its transcript, its size and its last state, readable long after the window that produced it is gone. "What did we try in August" is a question with an answer.
2
Conclusions — promote what you learned out of the transcript When a session produces something worth keeping, promote it. It stops being line 8,400 of a transcript nobody will reread and becomes a standing directive every future session is told — whichever model reads it next. Retire it when it stops being true.
3
Index — NeuroHive knows where things live Which repository, which host, which deployment, which session touched what. Search spans every project at once, so "where did we decide that" resolves to a row rather than a memory. New people inherit it; so do new models.
4
Compaction is not amnesia The window compacting is normal and expected — it's how long sessions keep working. The point is that nothing important depends on surviving it, because the things that mattered were written down somewhere the compaction can't reach.

This is what makes switching models survivable. The new one starts with everything the old one was told and everything the project has concluded since — not a blank slate wearing a familiar name.

Everything else

The rest of the control room

The unglamorous things that decide whether a tool survives contact with a real team.

Source, in context

Issues, pull requests and releases from GitHub, GitLab or Gitea — grouped, filtered and attached to the project they belong to.

Encrypted secrets

Multi-field credentials — client ID, secret, endpoint — encrypted at rest, revealed deliberately, never printed into a log.

Deployments

What shipped, where, when and by whom. Environment by environment, with history you can actually point at.

Hosts & storage

The machines and volumes behind each project, browsable over SSH without leaving the page.

Companies & teams

Group projects by client or business unit. Scope who sees what, down to the individual project.

Status API

A read-only endpoint for dashboards and assistants. You choose exactly which projects it can see.

Full audit trail

Every create, edit and delete recorded with before and after — sensitive fields redacted automatically.

Works anywhere

Desktop, tablet, phone — light or dark. Tested down to some genuinely strange embedded browsers.

Idea to shipped

Turn an idea into a real project — executed fast, and safely

The speed of agents with the guardrails of a real engineering process. Not one or the other.

1

Capture

An idea lands as a project with an owner, a company and a priority. It exists somewhere real from minute one.

2

Brief

Directives and standing context make sure every agent starts knowing your stack, your rules and your conventions.

3

Execute

Agents work against the linked repo. Issues get filed, commits get traced, sessions stay attached to the project.

4

Ship

Releases and deployments are recorded against the project, with a full audit trail behind them.

Guardrails

Real access calls for real controls

An agent that can edit code, reach servers and touch credentials is not a chatbot. It's a new member of staff with root. NeuroHive treats it that way.

Most AI tooling asks you to choose between speed and control. That's a false trade, and it comes from building the guardrails last. NeuroHive puts the boundary at the point where an action actually happens — not in a policy document nobody reads.

The rule is simple: an agent can only reach what its project can reach. Scope is inherited, never assumed. A bot attached to one project cannot wander into another's repositories, hosts or secrets, because it was never handed them in the first place.

Anything consequential stops and asks. Writes are atomic and conflict-aware — if a file changed underneath a pending edit, the save is refused and you're shown both versions rather than quietly losing one. Directive files are edited, never created, so an agent cannot invent new instructions for itself. And every action lands in an audit trail with credentials stripped before anything is written down.

Scope is inherited

Agents reach exactly what their project reaches. No ambient credentials, no implicit access to the rest of the estate.

Stop and ask

Consequential actions pause for a human. You see what's about to happen, and the diff, before it does.

Safe writes

Atomic and conflict-aware. Concurrent edits are detected and surfaced — never silently overwritten.

Secrets stay server-side

Platform keys never reach the browser and are never handed to a model. Calls to connected systems are made on your behalf.

Off by default

Nothing is exposed outward unless you switch it on, project by project. Silence is the default state.

Audited, redacted

Every create, edit and delete captured with before and after — tokens and passwords removed on the way in.

Ownership & pluggability

The model is rented. The context is yours.

Most AI tooling quietly makes you a tenant in someone else's platform. This is the part of NeuroHive designed specifically so that never happens.

Every few months a different model becomes the best one. Prices move, capabilities leapfrog, providers deprecate what you were depending on. Work welded to one vendor turns each of those into a migration project.

NeuroHive separates two things the industry keeps conflating. The intelligence is a commodity you rent, swappable in an afternoon. The context — your standards, your decisions, the record of why things are the way they are — is the asset that compounds, and it stays yours: plain files in your own repositories, exportable at any time, never locked in a vendor's format. Point a different model at it tomorrow and all of it walks over unchanged.

Swap

Change provider without changing how you work. Your projects, context and history don't move, because they were never stored on the provider's side.

Combine

You aren't picking a winner. Run a frontier model where reasoning is hard, a cheap local one for bulk work, and different models on different projects — at the same time.

Keep private

Open-source and custom models on your own hardware, for the work that legally or contractually cannot leave your network.

Walk away

If you ever drop NeuroHive, your repos, files, database and directives are exactly where they always were. Nothing to export, nothing held hostage.

Open-source and custom models, for when data can't leave

Some work simply cannot be sent to a third-party API — client confidentiality, regulated data, contractual restrictions, or your own policy. Point those projects at an open-source model running on your own hardware, or a model you've fine-tuned yourself, and the data never crosses your boundary. Same interface, same guardrails, same audit trail as everything else. Privacy becomes a per-project setting rather than an all-or-nothing architectural decision.

Context that outlives the model that wrote it

Your directives, standing decisions and project memory are written once and read by whatever model you're using this quarter. When you switch — and you will — the new model inherits everything the old one was told. No re-briefing, no rebuilding months of accumulated knowledge, no quiet drift back to explaining the same things over again. That continuity is the entire reason to own your context instead of renting it.

AnthropicOpenAIAzure OpenAIGoogle GeminiMistral LM StudioOllamavLLMllama.cppOpenRouterCustom / fine-tuned GitHubGitLabGiteaBitbucketClaude CodeCodex DockerSSH hostsAny OIDC provider

Any repo host, any machine, any model — treated identically, so none of them get to decide how you work.

Own your context. Use any intelligence.

See NeuroHive running against a real project — your repos, your hosts, your models. Thirty minutes.

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