Peregrine OS

The main differentiator in our software tool is that there is no contract required, no minimum amount of months. Each tool's subscription price recurs monthly and can be canceled at any time. This is truly a no risk product.

We are also much more affordable than other enterprise intelligence tools. No more multi-year contracts worth 10s of thousands of dollars.

Additionally, we built Flightpath.AI, the engine that powers Peregrine OS. This AI can read and contextualize all of your activity and data.

Peregrine OS is for anyone who has to walk into a pitch, a call, or a proposal already knowing the account cold, without a research team behind them to do that digging. That's small and mid-size agencies, sure, but just as much independent consultants, fractional CMOs and fractional marketing leaders juggling five clients at once, and solo strategists. Basically, it's for anyone whose business depends on showing up informed and doesn't have the headcount to have someone else do that work for them.

The common thread isn't the industry label, it's the constraint: real, billable time is the scarcest resource, and hours spent manually digging for a signal is time not spent pitching or delivering. The bigger the team, the more that gets absorbed by a research function; the leaner the operation, the more it falls straight on the person who's supposed to be selling or advising. That's who we built this for.

Broadly, four things:

Always-on signal feeds. A set of daily trackers that watch for the events that actually create an opening: a new CMO or VP Marketing, a funding round, a hiring surge, industry news worth knowing about, so that instead of manually checking LinkedIn, Crunchbase, and a dozen news sources every morning, it's just sitting there waiting for you.

On-demand account and brand briefs. Point it at a company and get a full intelligence report back in minutes: where they stand, how they compare to competitors, what budget signals say about timing, and a recommended angle, the kind of prep that used to take an afternoon.

Pre-call contact intelligence. Narrower and faster, give it one person's name, title, and company, and it comes back with what's happening at their company right now, a read on how they likely make decisions, and outreach lines already written in the right tone, built for the ten minutes before you actually reach out.

Honesty about confidence: Every report shows what's verified versus inferred rather than presenting everything with the same false certainty — so you know how much weight to put on any given line instead of finding out the hard way.

The honest math: our tools run $39–229 a month depending on the tool, with a 14-day free trial so you can prove it before paying anything. That's roughly the value of one to three billable hours for most of the people we serve. So the bar is low; if a tool saves even an hour a week of manually checking LinkedIn, funding databases, and news sources, it's already paid for itself, and everything after that is pure margin.

And because it's priced per tool instead of one expensive platform, you're only paying for the narrow thing solving your actual problem, not subsidizing a dozen features you'll never open, the way most "all-in-one" intelligence platforms make you.

We ship in small, frequent pieces instead of big releases. Most weeks look like: a real usage pattern surfaces something worth fixing or adding, we build it, watch how it actually performs, and adjust, not a quarterly roadmap cycle where feedback sits for months before it reaches the product. That means the gap between "customer hits a wall" and "that wall is gone" is usually days, not a release cycle.

We're built on top of frontier AI models, not our own. So as the underlying models get better at reasoning, writing, and following instructions, our output quality improves without us having to rebuild anything, we inherit that progress instead of racing to keep pace with it ourselves. Our job is the part that doesn't improve on its own: what signals actually matter, how to verify them, and what a good answer looks like for this specific use case.

Ten years is a long horizon for a fast-moving space, so the honest answer is a direction, not a map with dates on it.
The narrow-tools philosophy doesn't change — the number of tools does. Right now we cover a handful of real, specific moments: a new CMO gets hired, a company raises money, you're ten minutes from a cold call. In ten years, the goal is that almost any moment where someone needs to walk into a room informed has a tool built for exactly that moment, not one bigger dashboard trying to cover everything at once, the same sharp-tool discipline, just applied to a much longer list of moments.

We move from research into some real action. Today, everything we build tells you what's true and what to do about it, it stops short of doing it. The next real step is tools that can act on your behalf where it makes sense, with you still deciding, not a black box making calls for you. That's already the plan for one part of the product; over ten years it's reasonable for that to become true more broadly, carefully, one tool at a time.

Peregrine OS is a responsive web product first, not a native app, and that's a deliberate choice, not a gap we haven't gotten to. The moment our tools are built for, pulling contact intelligence ten minutes before a call, checking a signal feed between meetings, is exactly the kind of moment that happens on a phone in a hallway or a parking lot, not at a desk. So every tool page is built mobile-first in its own layout, not as an afterthought skin over a desktop design: touch-sized targets, collapsed navigation, tables that become scrollable cards instead of getting crushed. What we haven't built, and aren't rushing into, is a dedicated native app. A responsive web product means every update ships instantly to every device with no app-store review cycle in the way, which matters a lot for a product that iterates as fast as ours does. If usage patterns eventually show people specifically want an installed app or push notifications a browser tab can't give them, that's the trigger to revisit it, not a calendar date.

Two separate things live under that question for us: is the software secure, and is the output trustworthy. On security, every tool sits behind real authentication and rate limiting, not a shared password, and we've run our own adversarial security passes that found and closed real gaps, like an internal endpoint that had no auth check at all and was quietly burning API quota. On trustworthiness, every claim our AI modules generate is expected to trace back to a real, checkable source, and when data coverage is thin, the report says so instead of quietly smoothing over the gap. On regulatory compliance, we're GDPR compliant through CookieYes across every page: consent is captured before any non-essential tracking script fires, not after, cookies are categorized and blocked by default until a visitor actually opts in, and Google's own Consent Mode defaults ship denied until that consent is given. We also maintain a written incident response plan with a named lead, not just an intention. Compliance isn't treated as a checkbox we cleared once; it's built into the actual page-load behavior of the product, not bolted on separately.

Mostly by removing the reasons people usually distrust software vendors. Every tool has a real 14-day free trial, nothing is charged until it ends, and cancelling is a self-serve click, not an email to a retention team. Pricing is public and per-tool instead of hidden behind a sales call, so nobody's guessing what this costs before they've even tried it. Every page ends with a real inbox, not a contact form that disappears into a queue, and we mean it when we say reply and a person reads it. But the deeper answer is that we treat feedback as a build input, not a suggestion box. When something's unclear or a feature request comes in, the instinct is to go back and ask what someone would actually use before guessing and shipping the wrong thing. That's slower in the moment. It's also why the product tends to keep getting renewed instead of trialed once and dropped, because it keeps changing based on what people actually told us.

The hardest problem wasn't a feature, it was reliability at the AI layer itself: getting a model to reliably produce a large, structured, accurate report inside a hard time budget, without silently failing in ways that looked fine from the outside. Our account-intelligence brief generator kept hitting a wall where a big generation call would time out, and the failure modes were genuinely different each time. In one incident, a model caught its own mistake mid-response and wrote a corrected answer right after it, and our own parsing logic discarded the whole thing because it was looking for one clean object instead of a self-correction. We didn't fix this with one patch. We fixed it by refusing to guess: reading the actual runtime logs for every failure, confirming root cause before writing a line of code, and shipping several distinct, narrow fixes as each real cause was found rather than one big rewrite based on a theory. It's still not permanently solved. It's the kind of problem you keep a real, honest log of and keep chipping at.