You wouldn’t push code to production without testing it. You wouldn’t size infrastructure based on a gut feeling. You wouldn’t accept “that seems about right” as an answer for anything that actually matters.
So why do so many technically-minded people buy life insurance by picking a round number and hoping it’s enough?
The answer is usually that nobody told them there was a better way. There is.
The Coverage Gap Is a Silent Failure Mode
Life insurance underinsurance is a bug that doesn’t surface until the worst possible moment — and by then there’s no patch, no rollback, and no incident post-mortem that fixes anything.
Most people with coverage have some protection. The problem is coverage that was never properly scoped. A policy purchased because it “came with benefits,” or because someone suggested a multiplier that sounded reasonable, or because the premium fit the budget — that’s a system configured without requirements gathering.
The failure mode is quiet. Premiums get paid, the policy sits in a drawer, and nobody finds out the coverage was insufficient until a family is trying to cover a $680,000 mortgage and a decade of childcare costs on a benefit payout that was sized for neither.
Define Requirements Before You Build the Solution
Any engineer knows you don’t architect a solution before you understand the problem. Life insurance is the same. The product is easy to find. The hard part — the part most people skip — is defining exactly what the system needs to do.
A proper needs analysis is essentially a requirements document for your coverage. It forces you to quantify every financial obligation your family would carry if your income disappeared:
Income replacement — How many years of income needs to be substituted, and at what level? This isn’t a generic multiplier. It’s a function of your salary, your spouse’s earning capacity, your kids’ ages, and how long your household would need the runway. A family with two newborns and a single income has dramatically different parameters than one with school-age kids and two comparable salaries.
Debt obligations — Mortgage balance, car loans, lines of credit, anything co-signed. These are hard numbers. Pull them, add them up, and put them in the model.
Operational costs — If the stay-at-home parent is gone, what does it cost to replace what they were doing? Full-time childcare for two kids in Ottawa runs $3,000 to $4,000 a month. Over ten years that’s $360,000 to $480,000 in a scenario most people never run.
Future liabilities — Post-secondary education costs, if that’s part of your plan. Cap them conservatively if you want, but put a number on it.
Existing offsets — Your spouse’s income, liquid savings, investments, and any group life coverage already in force. These reduce the net requirement. A lot of Ottawa tech workers and federal employees assume their group plan covers the gap. It usually covers one to two times salary. For a senior developer earning $130,000 with a $750,000 mortgage, that’s a $620,000 shortfall before you account for anything else.
There’s a Calculator for This
You don’t have to build the model yourself. The life insurance needs calculator takes each of these variables as inputs and returns a concrete coverage target based on your actual household data — not population averages, not rules of thumb, not someone’s best guess.
Run it before you look at a single product. What comes out the other end is a number you can work backwards from: a defined requirement that your coverage solution needs to meet. Everything after that is just finding the most efficient way to meet it.
Benchmark the Market Before You Engage a Vendor
Once you have a target, it’s worth understanding the pricing landscape before any conversations happen. Term life insurance is a fairly standardized product — 20-year term, level premiums, defined benefit — but carrier pricing varies more than you’d expect for something this commoditized.
The life insurance marketplace runs a multi-carrier comparison against your parameters. Same coverage amount, same term length, different providers — laid out so you can see the spread and understand what you’re actually evaluating. It’s the equivalent of benchmarking before you commit to a vendor: you’re not buying yet, you’re building context so the eventual decision is informed rather than arbitrary.
An online quote isn’t a final offer. Underwriting adjusts pricing based on your health profile, and what looks like the cheapest option in a comparison may not hold after the application process. But benchmarking first means you walk into any subsequent conversation with calibrated expectations.
Get a Human to Review the Architecture
Independent brokers are the equivalent of a senior engineer reviewing your design before you commit to it. They’re not building something from scratch — they’re pressure-testing what you’ve already scoped, catching edge cases you didn’t think of, and making sure the implementation matches the requirements.
An independent broker works across multiple carriers with no incentive to push a particular product. They’ll flag things the calculator and the marketplace won’t: whether your health history creates underwriting risk, whether separate policies for you and your spouse outperform a joint policy, whether the lowest-cost option in the benchmark has exclusions that matter for your situation.
Ottawa tech households — especially those with federal employment, stock compensation, or contractor income — often have coverage scenarios that need a human to map properly. Group benefits, spousal plans, and variable income all affect the net requirement in ways that aren’t always obvious without someone who does this daily.
Ship Something That Actually Works
The goal isn’t to have a life insurance policy. The goal is to have coverage that performs correctly under the conditions it was designed for — which means it was designed with those conditions in mind in the first place.
Run the needs analysis. Get a real number. Benchmark the market. Talk to an independent broker who can validate the architecture.
That’s it. No black-box assumptions, no configuration drift, no finding out in production that the system was under-specced from the start.
