Your best people spend their days on work they've done a hundred times.
Nova takes it off their hands.
Nova is an AI agent that does the work — not just advises on it. It puts the most capable AI models to work inside the workflows your team already runs, does the job alongside your people under their review, and gets more capable and more predictable the more it does it.
recipe: pipeline.triage · v3Root cause — upstream contract change
A third-party provider quietly renamed the revenue field in yesterday's release. The mapping fell through and the downstream metric zeroed out. An edge case no runbook covered.
#392 — restore revenue mapping. write · draft only
The problem
Every team runs on work nobody wrote down. It repeats dozens of times a week — and it lives in the heads of the few people who've done it a hundred times.
The check that follows the same policy. The reconciliation that traces the same systems. The request that follows the same five rules. It's real, it repeats dozens of times a week — and it costs your senior people's hours. When they're busy, the work waits; when they leave, it walks out the door.
Worse is the case nobody thought to check: it doesn't show up in a checklist. It shows up later as a missed number, a customer ticket, a compliance gap — long after it was cheap to catch.
What Nova does
Think of Nova as a new colleague — one who does the work, not just advises on it.
She arrives already fluent in your tools and holding the right accesses. From day one she picks up real tasks: she reads across your systems, does the job, and hands back a finished result.
Your team steers, teaches, and reviews her — but only when it's needed, and less and less over time. She improves fast and never forgets, so she needs less guidance, fewer gates, and less review the longer she works with you.
One identity across many systems: @-mention her in MS Teams or Slack, leave a note in a comment thread, reply on the ticket — one agent behind all of it.
One engine, many kinds of work
It's one engine, pointed at different work. Nova hardens each kind the same way — only the systems and the output change.
Engineering is where the loop proves itself first. The same engine reaches into the revenue- and cost-driving departments where a small automated win is worth the most — finance and accounts payable, accounting, sales operations. You don't build a new tool for each.
Where we proved it first
Any company that sells software has a team responsible for keeping it working. When a data feed stops or an overnight job fails, someone has to drop everything and work out what broke, where, and why — usually a senior engineer: the most experienced, most expensive, and most stretched person on the team.
We pointed Nova at exactly this at our first customer. It picks up the alert, runs the investigation across every system involved, finds the cause, and writes up the fix for a person to approve. An hour of expert digging becomes minutes — and the routine cases stop reaching a human at all.
Every issue picked up and investigated
None fall back to the old manual scramble.
Diagnosed with a fix inside 30 minutes
Eight in ten correctly diagnosed, with a proposed fix attached.
No senior engineer pulled in
Nova runs the investigation; a person only approves the fix.
Why this is the right proof: the work is high-volume, repeats constantly, and burns expensive expert time — the exact conditions where automation pays back the most. And it's the same underlying shape as an invoice exception, a reconciliation, or a policy approval.
How it works
Does the work — day one
At delivery, Nova picks up real tasks across your systems and produces a finished result, not just a suggestion — visible in Slack, MS Teams, Jira, GitHub or GitLab.→
Reviewed before it ships
Early on, a person signs off her output before it reaches a customer or the ledger — like reviewing a new hire's work.→
Learns from corrections
Each edit or approval teaches Nova the proven path — a versioned recipe: fixed steps, scoped tools, human-approved gates.→
Hardens into routine
Cases she has handled many times run the same way every time — more predictable and more hands-off, freeing your people for the new and the ambiguous.↺
You never pick what to automate — whatever your team does often enough is what Nova gets better at. The recipes it builds are yours, and they compound across your company.
Why we built it this way
Most AI agents sell more autonomy. Nova is the opposite bet: an Agent that does less on its own over time, because what it learns becomes a fixed, inspectable, rigid flow.
Self-improvement that increases predictability, not autonomy. That is the whole product. The market's bet is more autonomy over time — the agent decides more on its own, and you trust more and see less. Nova's bet is more predictability: each repeat hardens into a rigid, versioned recipe you can read.
A human still approves every change that touches a customer, money, or production — and you never configure Nova or pick which workflow to automate. Your team just uses it, and it codifies what they actually do.
Does less over time
What the Agent learns becomes a fixed, inspectable, rigid flow — and a human still approves every change that touches production or your data.
- The proven path hardens into a rigid, versioned recipe
- Self-improvement that increases predictability
It lives in the channel
Slack, MS Teams, Jira, GitHub, GitLab. Every action is observable in-channel, by default.
- AI use stops being a private tab on a laptop
- Using the Agent becomes a team sport
Scoped to the user
The Agent acts on tools and data but never holds the access tokens.
- Permissions are granular and bound to the requesting user
- It can never surface more than that user could already see
- Every prompt passes guardrails before it reaches a model
Your data, your models, your controls
Nova is built for teams that can't be casual about data, access, or spend — in plain terms:
Your data stays yours — and never trains anyone's model
Nova runs in a dedicated, fully isolated environment. Your data is never pooled with other customers, never shared with the AI provider, and never used to train anyone's models.
Model-agnostic — you choose the brain
Run Nova on OpenAI, Anthropic, or open-source models you host yourself, and keep the most sensitive work on a model that never leaves your environment. Switch providers without losing a day of accumulated context.
Only what the person tasking it could do
Nova works with that person's access — no more — and a human approves anything that touches a customer, money, or production. All access runs through a standalone broker: least privilege, deny by default, every call logged and reviewable.
Costs you can see and cap
Every AI call is metered, so you see exactly what Nova spends and can set limits per team — no surprise bills.
The enterprise basics, covered
Single sign-on, role-based access, and a full audit trail. Data residency agreed per deployment.
How we deploy
Three steps — the infrastructure is ready in about a week, mostly on us.
Integrations
You name the stack — your dev, ops, finance and data tools. We map it.
Access
Each system gets the access its work needs: read-only where Nova only looks, read/write where she's expected to act — and even then she acts by proposal, a pull request or a draft for a person to approve.
Deploy
We provision a dedicated, fully isolated environment alongside your team and ship Nova into it. No shared infrastructure, no co-tenancy.
Standing up the infrastructure takes about a week. Connecting your systems can take longer — that part moves at the pace your side can grant access — but Nova starts working the moment the first integration is live.
How we engage
We ship it, install it with your team, and keep it hardening. A hybrid model, built so you see value before you pay to keep it.
Implementation
A short, hands-on project run with your team: we deploy Nova into a dedicated, fully isolated cloud environment, map your stack, wire up integrations, and stand up your first workflow. Working on real cases inside a week.
Platform & support
Runs the agent and keeps your recipes hardening as your team uses it — plus bug fixes, new integrations as your stack changes, and new use cases as the engine expands across your recurring work.
No value, no fee. The implementation is where we earn the subscription. If it doesn't prove real value for your team during the engagement, you opt out — the monthly fee never kicks in. Consulting gets the first workflow live; the platform compounds from there.
Who it's for
Nova is for you if…
- You have real, repeatable operational work — and a team whose best hours are spent on it
- The more integrations, data sources, and hand-run processes you have, the more Nova pays back
- You want the work done in the tools you already use, not another app to open
- It starts where your repetitive work is densest and expands from there
Nova is not for…
- Companies still finding product-market fit
- Teams with only a handful of recurring cases a week
- Anyone who would rather buy a dashboard than change how work gets done
Who builds it
We're the two founders — UK/Canada-based technical leaders with deep experience in AI, platform engineering, and SRE. We have designed and operated enterprise AI systems at Deutsche Bank, Behavox, SAP, Mail.Ru, and Lightspeed — production environments consuming billions of LLM tokens a month and orchestrating hundreds of independent agents across customer support, incident management, and the SDLC.
Point Nova at the work your team repeats.
Nova deploys in about a week. The first step is a short call to map your stack and confirm access. From there, the agent is working on real cases within days.
Built by engineers who've run hundreds of production agents. A person approves anything consequential.