Services

The review phase is quick: our Discovery runs 2 to 3 weeks, with kickoff 7 to 10 business days after signing, and anything actively bleeding (crashes, data loss, exposed credentials) gets stabilized first once work begins. The modernization itself depends on how much of the system needs replacing, which is exactly what the review prices out phase by phase. Be wary of anyone promising a full rescue timeline before they've read the code, since the honest answer starts with what the review finds.

With read-only access to your repositories, we map the architecture, data model, test coverage, security posture, and deployment process, then report what's solid, what's fragile, and what's dangerous in plain language with a phased, line-item plan. Nothing gets changed during the review, so there's no risk to the running product. It's worth insisting on this step with any partner, because the review is where a rescue quote stops being a guess and becomes a number you can hold someone to.

Scoped honestly, it starts small: our $1,199 fixed-price Discovery reviews the existing code and returns line-item pricing, so you know the real number before committing to anything. From there, patch work is priced per fix, incremental modernization spreads cost across phases while the platform keeps running, and a full rebuild lands in the same ranges as new software of similar scope, typically $25k to $75k for a focused MVP-scale product. Budget 15 to 20 percent of build cost per year for maintenance afterward so the platform never needs rescuing again.

Patch when the architecture is sound and the problems are local, modernize piece by piece when the core works but parts are past their support window, and rebuild only when the foundation itself (data model, test coverage, stack) fights every change. A quick gut check: walk the system and count what you'd keep. Keeping most of it points to a patch, keeping only the data and the lessons points to a rebuild. Ask any partner you're evaluating to show you that keep list before they quote either path.

A software rescue is taking a system you can no longer move forward (a stalled build, an aging platform, a codebase nobody on your team understands) and getting it back under control through a code review, stabilization, and a deliberate plan to patch, modernize, or rebuild. It rarely means throwing everything away. The existing system is the most accurate requirements document you'll ever have, so a good rescue starts by reading it, and any partner who quotes a rescue without reviewing the code first is guessing with your money.

Yes, with deliberate architecture. The compliant paths are real: AI vendors that sign business associate agreements, zero-retention configurations, and de-identifying data before it leaves your system. The common failures are mundane, like piping patient conversations through an unvetted transcription tool or letting prompt logs accumulate PHI in a logging service outside the BAA chain. Decide feature by feature, before launch, what data the model sees and under what agreement. An AI feature bolted on in a sprint is how clean systems become liabilities.

Less than founders fear when it's designed in from the start, and far more when it's retrofitted. Built in early, compliance is mostly architecture decisions (eligible services, data modeling, access controls, audit logging) layered onto a normal custom build, though a compliant product usually starts above the entry point of a comparable standard build because the safeguards add scope to every feature they touch. Retrofitting touches every endpoint and table and forces a full re-test of a live product. Our discovery engagement maps PHI flows and prices the compliant build line by line before you commit.

Yes, with every vendor in the path of protected health information: hosting, email, error tracking, transcription, and any AI API that sees identified patient data. The major clouds sign business associate agreements covering specific eligible services, and some AI providers offer them on enterprise tiers with zero-retention configurations. One vendor without an agreement breaks the chain no matter how good your own controls are, which is why the vendor list belongs in your architecture plan instead of a post-launch cleanup.

No. No government body certifies software as HIPAA-compliant, so a "HIPAA-certified" badge is a marketing claim rather than a legal status. What exists is the ongoing practice of compliance: risk assessments, safeguards, agreements, and documentation maintained for as long as you handle protected health information. Third-party audits can be useful evidence for enterprise buyers, and they still only describe how you operated at a point in time. Treat any vendor leading with a certification stamp as a signal to ask harder questions.

HIPAA compliance is a combination of technical safeguards built into the software (unique user access controls, encryption in transit and at rest, audit logging, integrity controls), signed business associate agreements with every vendor that touches protected health information, and documented policies for the people operating it. No single feature makes a system compliant; the architecture, the vendor chain, and the operating habits have to hold together. Ask any team you're vetting to walk you through where PHI flows in their proposed design; specifics are the difference between compliance and marketing.

You shouldn't, and with a planned migration you won't. Most platforms offer export paths (CSV, API access, or both), and the real work is mapping old fields to the new data model and deciding which historical records matter. That mapping belongs in the migration plan before development starts, along with a validation step that proves the numbers match after the move. Data migration is unglamorous, and it's the part sloppy rebuilds get wrong, so make sure it has its own line in the scope.

No. The better pattern is to migrate the product core one workflow at a time while keeping the parts of your stack that still serve you, like a marketing site on Webflow. Shipping the highest-pain workflow first gets you relief early, and running the old and new systems in parallel for a short period means users move over without drama. A big-bang rewrite is almost never the right plan, and a partner who proposes one without justifying it deserves follow-up questions.

A focused first version of a custom product typically runs $25,000 to $75,000, and migrations often land toward the friendlier end of that range because the requirements are already proven by your existing app. The bigger number to watch is the one you're already paying: a metered platform bill that scales with usage forever. Our discovery engagement (Fixed price, two to three weeks) prices your specific migration line by line, so you can compare the two honestly before committing.

Yes. We regularly take products built on Bubble, Webflow, Glide, and similar platforms and rebuild the product core as custom software, using the existing app as the specification. That existing build is an asset: it already encodes your workflows, edge cases, and user feedback, which makes scoping faster and more accurate than starting from a blank page. Ask any partner how they'll use your current app in scoping; a team that treats it as a spec instead of a liability will save you weeks.

Building the wrong thing. Software that ships late or runs over budget can recover; software nobody wants cannot. That's why the first two phases, validation and discovery, exist. They're cheap compared to development, and they kill bad bets before the expensive part starts. Treat any plan that jumps straight to code as a red flag, no matter how fast the quote sounds.

No. A large share of successful software founders are subject-matter experts with no technical background. What you need is accountable technical judgment, and you can get it from a partner as well as from a co-founder. The right firm translates business goals into architecture and scope, explains tradeoffs in plain language, and puts the code and IP in your name. Ask any partner who owns the work when it's done; the answer tells you whether they expect you to depend on them forever.

Get evidence that the problem is real and your buyer will act on it before you pay for development. Talk to the people you're building for, put a clickable prototype in front of them, and watch where they hesitate. A design prototype costs a small fraction of a build and gives you something concrete to test and pitch. If the signal is good, a discovery engagement turns it into a defined scope and a fixed price. If it's weak, you just saved the cost of building the wrong thing.

A dev shop takes a spec and returns code. A commercialization firm is accountable for the path from idea to revenue, which means it also does the work around the code: validating the idea, defining scope, designing the experience, planning the launch, and supporting the product as it grows. The difference shows up in the questions each one asks. A dev shop asks what you want built. A commercialization partner asks who will pay for it and what has to be true for them to keep paying.

Three come up constantly: building for every user type at once instead of the one that proves the business, treating admin panels and dashboards as launch requirements, and skipping scope definition entirely so the build drifts for months. Each one turns a focused first release into a slow, expensive general product. A short discovery phase prevents most of this, because a written scope gives you something concrete to say no against.

When a stranger can complete your core workflow without you standing next to them, and the product handles the unglamorous parts (accounts, errors, data safety) well enough that you'd trust it with a paying customer. Polish beyond that can wait. The costlier failure is the opposite one: teams that keep adding features to avoid the exposure of launching. Set a launch bar tied to one workflow, hit it, and let real usage tell you what deserves attention next.

Often, yes. AI app builders are genuinely good at producing a working demo fast, and that demo is useful evidence of what you want. What it usually lacks is what production requires: security, a sound data model, testing, and an architecture that survives real users. The practical path is a code review of what you have, then a plan for what to keep, what to rebuild, and what it will cost. That's exactly the kind of question a discovery engagement answers.

A prototype is a clickable model of your product, usually built in a design tool like Figma, with no working code behind it. An MVP is real software that real users run. Prototypes are for testing the experience and pitching before you spend on engineering; an MVP is for learning whether people actually use and pay for the thing. Ask any partner which one your budget buys, because a demo that looks finished and a product that works are very different purchases.

More than most pilots are built to survive. Turning a proof of concept into something the wider business runs on means hardening it for real usage, meeting the security and data standards a central IT team will accept, and documenting it well enough to hand over. This is the gap where most corporate innovation dies, because a demo that works is not a system anyone can adopt, and it's exactly the work we specialize in.

With short feedback loops and progress an executive can actually read. Corporate innovation teams answer to sponsors, dual reporting lines, and shifting priorities, so we work in a cadence that produces working software rather than status decks, and we keep useful work moving when an approval stretches out or a sponsor changes direction. A partner who has only worked with founders will build you something good and then be surprised when it stalls in your process.

We've been through it before, which is usually the difference between weeks and months. That means signing your master services agreement, carrying the insurance your legal team requires, completing vendor risk questionnaires, and working inside your change control and approval gates without needing to be walked through each step. Ask any partner whether they've been onboarded as an enterprise vendor before, because if procurement is new to them, that learning curve becomes your delay.

Yes. New applications should authenticate through your existing single sign-on rather than standing up a separate login for your IT team to govern. We work with standard enterprise identity providers and SSO protocols, and we scope authentication during planning so access, roles, and permissions are settled before development starts instead of being retrofitted later.

By expecting it and preparing for it, rather than treating it as red tape at the end. A partner who has been through enterprise reviews before can answer a vendor security assessment directly, produce what reviewers actually ask for (data flow diagrams, access controls, audit logging, evidence of secure development practices), and support a penetration test near the end. Watch how a firm reacts when you first raise security. The ones who have done this can tell you what your reviewers will flag before you ask.

The engineering is similar; everything around it is not. An enterprise build has to authenticate against your identity provider, connect to systems of record that predate the project, pass an InfoSec review, and clear procurement, none of which a startup MVP faces. Budget for that surrounding work from the start, because it usually determines the timeline more than the code itself does.

Hourly (time-and-materials) billing is common and flexible, but it shifts the risk of overruns onto you ‚ the meter runs until the work is done. A fixed price gives you budget certainty, but it depends on a clearly defined scope, which is why the firms that offer it usually define that scope through discovery first. For most buyers, a fixed price set after proper discovery is the lower-risk path.

Look for in-house multidisciplinary teams, a transparent process, relevant portfolio work, clear communication, transparent pricing, and ownership of outcomes rather than just tasks. Ask how they handle scope changes and post-launch support.

Typically: validate the idea, define scope through discovery, design the experience, build the product, launch it, and iterate based on real usage and revenue.

It's the full process of turning a software idea into a product that generates revenue — strategy, design, development, launch, and growth. It treats software as a business outcome, not just a build.

Include the one core workflow that proves your value; leave out everything that's "nice to have." The discipline of cutting is what makes an MVP fast, affordable and solves the core problem.

Most MVPs go from discovery to launch over several months, depending on scope. Tight scope is the single biggest lever on timeline. Clearly defining a software scope during an initial discovery can help tighten scope and decrease timelines.

An MVP is the smallest version of your product that delivers real value and lets you learn from actual users. It's not a half-built product — it's a focused one.

Tighten scope to a true MVP, do proper discovery first, and sequence features so you launch and learn before building everything. Cutting discovery to "save money" almost always costs more later.

Because "an app" can mean anything from a single-feature MVP to an enterprise platform. Quotes vary with scope, team location and seniority, and how much discovery has been done — a vague brief produces a vague (and risky) estimate. Taking the time upfront to fully understand the application saves time, money and is the only way to provide an accurate quote.

Most custom apps range from tens of thousands of dollars for a focused MVP to six figures for a complex, multi-platform product. The biggest cost drivers are scope, number of platforms, integrations, and design complexity.