If you've ever been the technical founder of an 11-to-50 person product company, you already know the shape of this problem. It's 9:47 on a Tuesday. You opened your laptop to ship a feature. Forty minutes later you haven't written a line of code. You're staring at a Linear board that nobody groomed, a GitHub queue with three unassigned bugs, a Slack DM asking who's covering the on-call gap next week, and a half-finished spreadsheet trying to figure out whether last month's invoice actually reflects the work shipped. None of these are your job. All of them are now your job.
This is the operational tax founders pay for not yet being big enough to hire the people who would absorb it. By our own informal survey of around forty technical founders in this band, the median spends roughly 30% of their week on what we'd politely call engineering operations: task assignment, skill matching, time tracking, leave management, GitHub triage, vendor coordination, and the endless one-on-one nudges that keep work moving. That's a day and a half a week of context-switching out of the work you're uniquely qualified to do. It usually gets compressed into evenings and weekends, because daytime got eaten by meetings.
The reason it stays unsolved is that the stack is fragmented by design. Jira owns the ticket. GitHub owns the code. Slack owns the conversation. BambooHR or Personio owns the leave calendar. Toggl owns the timer. A spreadsheet owns the budget. Each tool was built by a different company optimising for a different buyer, and the seam between them is you. No single product covers the full operational surface area of an engineering team, and stitching them together with Zapier produces an integration spaghetti that breaks the moment your headcount changes, your repo structure shifts, or someone renames a Slack channel.
The deeper issue is that none of those tools were ever supposed to make a decision. They store state. They notify. They don't look at a queue of fifteen open issues and say "given who's on holiday, who's on-call, who shipped the most relevant code last quarter, and what we've already spent on this feature, here's who should own these next three." A founder makes that call by holding the whole picture in their head. That's the call we set out to automate.
Why this matters more than it sounds
A capable engineering COO, someone whose actual job is to keep delivery flowing, runs £150,000 to £200,000 fully loaded in London or New York, plus equity. They're worth it. They make founders 1.5x faster by absorbing the operational drag. But you can't justify that hire pre-Series A, which means the people who would benefit most from a COO are the ones who structurally can't have one. The founder ends up doing the job badly, in stolen hours, with no training and no headspace, and the team feels the consequences in slower delivery and missed handoffs.
This is the gap Infersync is built to fill. Not "another project management tool." Not a Jira-with-AI bolt-on. An operating layer that runs the playbook a senior COO would run, on the systems engineering teams already use, at a price an early-stage company can actually pay. Think of it as the £7-per-seat version of the person you can't yet hire — and one that scales out, not up, as you grow.
How Infersync works
The product sits between your GitHub org, your Slack workspace, and the people doing the work, and it makes four decisions for you every day.
It assigns tasks based on skill and cost, not vibes. When an issue lands in a connected repo, Infersync parses the title, labels, body, and linked files. It compares the required skill profile against the live capabilities of your team — which it learns from their actual commit and PR history, not a self-reported skill matrix that goes stale in a month. It then picks the lowest-cost qualified person (or AI agent) who has capacity in the next 48 hours. If your senior React engineer is the only person who's touched the auth flow in the last six months, that's who gets the auth bug, every time, without you having to remember. If two people are equally qualified, the cheaper-blended-rate one wins, which over a quarter rebalances workload toward your mid-level engineers and stops your seniors from being assigned everything that mentions "important."
It tracks time without anyone tracking time. Manual time tracking is the most universally hated ritual in software, and the data it produces is unreliable — engineers either over-report (defensive) or under-report (busy) and you can't tell which. Infersync derives time-on-task from the signals your tools already emit: branch creation, PR open, commits, review comments, merge, deploy. We treat the gap between branch creation and merge as the outer bound and use commit cadence to estimate active focus time inside that window. The output is a clean per-feature cost report the founder can actually use to answer the question "what did we spend on the billing rewrite last month" — and one you can hand to a customer when a feature ships as paid work.
It manages leave and capacity as one thing. Holidays, sick days, on-call rotations, parental leave, and conference travel all flow through one calendar that the assignment engine reads from. If someone's on PTO, they don't get assigned. If on-call coverage is thin next Thursday, Infersync flags it before it becomes a 2am incident. The capacity model is forward-looking: it doesn't just know who's out today, it knows that your iOS lead is at WWDC the week your iOS release is supposed to ship, and surfaces the conflict three weeks in advance.
It routes everything through Slack so you don't have to babysit it. Notifications are deliberate: a single morning digest of what's moving and what's stuck, real-time pings only when something genuinely needs a human (a stuck PR, an unassigned P0, a capacity collision, an agent run that hit a guardrail). Founders stop micromanaging because they stop needing to. The team stops getting noise because the system only speaks when it has something worth saying.
A routing rule, expressed in the shape Infersync uses internally, looks roughly like this:
// Skill-weighted routing for a single repo
{
repo: "infersync/api",
rules: [
{
match: { labels: ["bug", "auth"] },
prefer: { skills: ["typescript", "supabase-auth"], cost: "lowest-qualified" },
fallback: "round-robin",
notify: ["#eng-alerts"],
},
{
match: { labels: ["agent-eligible"] },
assignee: "ai:claude-sonnet",
humanReviewer: "round-robin:senior",
},
],
}Walk through the canonical workflow. A GitHub issue lands at 11:14am tagged bug and auth. Infersync reads it within seconds. It identifies the two people on the team who've shipped auth code in the last quarter, checks their current load and calendar, picks the cheaper of the two given capacity, and assigns the issue. It posts a one-line notification to #eng-alerts. The assignee opens a branch. Infersync starts the timer. When the PR merges three days later, the cost of that fix lands in your monthly engineering report, attributed to the feature it belongs to. You did nothing. You also know exactly what happened.
Where this is going
Right now Infersync runs the engineering layer because that's where the pain is most acute and the signals are cleanest — code and tickets are structured data. But the operating model generalises. The same routing-and-cost engine that decides which engineer gets which bug is, structurally, the same engine that should decide which support agent gets which ticket, which contractor gets which design brief, which AI agent gets which back-office task.
The longer-term product is a unified operations platform that coordinates humans and AI agents across every function of the company — engineering, customer ops, finance, HR — on one cost-aware substrate. Today you stitch eight SaaS tools together with three contractors and an ops hire, and you (the founder) are the integration layer between them. In two years that should be a single layer that any team of five can run, and any team of fifty can scale on, with the founder finally evicted from the middle of every workflow. Engineering is just where we start because it's the function we know best, the data is the cleanest, and the buyers — technical founders — feel the pain most viscerally and decide fastest.
Try it
We're live now. Early bird pricing is £7 per user per month for the base plan — which gets you the assignment engine, time tracking, leave management, and the Slack and GitHub integrations described above. That's roughly the cost of one team coffee run per engineer per month, in exchange for getting your Tuesday mornings back.
You can start a free trial right now. If you want to talk to a founder before signing up, or you're running a team where the off-the-shelf product doesn't quite fit, mail hello@infersync.com or DM us on Twitter at @useInfersync. We're pre-revenue, iterating fast, and we treat early customers as design partners — your feedback ends up in next week's build, not a 2027 roadmap deck.